feat(translate-mcp): scaffold per-tenant translation MCP (CF-3122)
Node.js MCP server exposing translate/search_tm/upsert_glossary/record_correction over StreamableHTTP on :9222. Routes translation calls to gemini-2.5-flash via LiteLLM, augmented with per-tenant TM + glossary + tone profile from the SmartTranslate pgvector DB. Schema migration in sql/001_schema.sql already applied to smarttranslate DB. Fleet registration in Infrastructure/litellm/config.yaml. Refs: CF-3122 CF-3123 CF-3124 CF-3125
This commit is contained in:
Christian Gick
138
src/db.js
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138
src/db.js
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/**
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* Postgres access for translate-mcp.
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* Connects to the SmartTranslate DB where translation_memory, glossary_terms,
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* and tenant_profile live (CF-3124).
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*/
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import pg from 'pg';
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import { createHash } from 'crypto';
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import pgvector from 'pgvector/pg';
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const { Pool } = pg;
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let pool = null;
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export async function getPool() {
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if (pool) return pool;
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const dsn = process.env.TRANSLATE_DB_DSN;
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if (!dsn) throw new Error('TRANSLATE_DB_DSN not set');
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pool = new Pool({ connectionString: dsn, max: 5 });
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pool.on('connect', async (client) => {
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await pgvector.registerType(client);
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});
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return pool;
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}
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export function hashSource(text, sourceLang, targetLang) {
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return createHash('sha256')
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.update(`${sourceLang}|${targetLang}|${text}`)
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.digest('hex');
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}
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export async function getTenantProfile(tenant) {
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const p = await getPool();
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const r = await p.query(
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`SELECT tenant, tone, formality, industry, base_rules,
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forbidden_terms, preferred_author_voice, direct_address_allowed,
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default_source_lang, default_target_langs
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FROM tenant_profile WHERE tenant = $1`,
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[tenant]
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);
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return r.rows[0] ?? null;
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}
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export async function listTenants() {
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const p = await getPool();
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const r = await p.query('SELECT tenant FROM tenant_profile ORDER BY tenant');
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return r.rows.map((x) => x.tenant);
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}
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export async function tmExactHit(text, sourceLang, targetLang, tenant) {
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const p = await getPool();
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const sha = hashSource(text, sourceLang, targetLang);
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const r = await p.query(
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`SELECT id, target_text, is_correction, correction_weight, query_count
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FROM translation_memory
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WHERE source_text_hash = $1 AND tenant = $2
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ORDER BY is_correction DESC, correction_weight DESC
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LIMIT 1`,
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[sha, tenant]
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);
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if (r.rows[0]) {
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await p.query(
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'UPDATE translation_memory SET query_count = query_count + 1, last_queried = now() WHERE id = $1',
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[r.rows[0].id]
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);
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}
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return r.rows[0] ?? null;
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}
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export async function tmVectorHits(embedding, tenant, sourceLang, targetLang, topK = 5) {
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const p = await getPool();
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const vec = pgvector.toSql(embedding);
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const r = await p.query(
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`SELECT id, source_text, target_text, is_correction, correction_weight,
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1 - (embedding <=> $1::vector) AS similarity
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FROM translation_memory
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WHERE tenant = $2 AND source_lang = $3 AND target_lang = $4
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AND embedding IS NOT NULL
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ORDER BY is_correction DESC, embedding <=> $1::vector
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LIMIT $5`,
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[vec, tenant, sourceLang, targetLang, topK]
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);
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return r.rows;
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}
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export async function glossaryHits(text, tenant, sourceLang, targetLang) {
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const p = await getPool();
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const r = await p.query(
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`SELECT source_term, target_term, is_dnt, context_hint, notes
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FROM glossary_terms
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WHERE tenant = $1 AND source_lang = $2 AND target_lang = $3`,
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[tenant, sourceLang, targetLang]
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);
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const lower = text.toLowerCase();
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return r.rows.filter((g) => lower.includes(String(g.source_term).toLowerCase()));
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}
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export async function tmInsert({ text, translated, sourceLang, targetLang, tenant, embedding, modelUsed, tier = 'mcp' }) {
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const p = await getPool();
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const sha = hashSource(text, sourceLang, targetLang);
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const vec = pgvector.toSql(embedding);
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const r = await p.query(
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`INSERT INTO translation_memory
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(id, source_text, target_text, source_lang, target_lang, source_text_hash,
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embedding, is_correction, correction_weight, query_count,
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tenant, model_used, tier, created_at)
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VALUES (gen_random_uuid(), $1, $2, $3, $4, $5, $6::vector, false, 1.0, 1, $7, $8, $9, now())
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RETURNING id`,
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[text, translated, sourceLang, targetLang, sha, vec, tenant, modelUsed, tier]
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);
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return r.rows[0].id;
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}
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export async function upsertGlossary({ tenant, sourceTerm, targetTerm, sourceLang, targetLang, isDnt = false, notes = null }) {
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const p = await getPool();
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await p.query(
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`INSERT INTO glossary_terms
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(id, customer_id, tenant, source_term, target_term, source_lang, target_lang, is_dnt, notes, created_at)
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VALUES (gen_random_uuid(), NULL, $1, $2, $3, $4, $5, $6, $7, now())
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ON CONFLICT (tenant, LOWER(source_term), target_lang, COALESCE(context_hint, ''))
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WHERE tenant IS NOT NULL
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DO UPDATE SET target_term = EXCLUDED.target_term, is_dnt = EXCLUDED.is_dnt,
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notes = EXCLUDED.notes, updated_at = now()`,
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[tenant, sourceTerm, targetTerm, sourceLang, targetLang, isDnt, notes]
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);
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}
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export async function recordCorrection(tmId, correctedTarget, tenant) {
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const p = await getPool();
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await p.query(
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`UPDATE translation_memory
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SET target_text = $1,
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is_correction = true,
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correction_weight = COALESCE(correction_weight, 1.0) + 1.0,
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last_queried = now()
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WHERE id = $2 AND tenant = $3`,
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[correctedTarget, tmId, tenant]
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);
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}
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70
src/http-server.js
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70
src/http-server.js
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@@ -0,0 +1,70 @@
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#!/usr/bin/env node
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/**
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* HTTP wrapper for translate-mcp using StreamableHTTPServerTransport.
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* Matches network-mcp / learning-mcp fleet pattern.
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*/
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import express from 'express';
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import { randomUUID } from 'crypto';
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import { StreamableHTTPServerTransport } from '@modelcontextprotocol/sdk/server/streamableHttp.js';
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import { createServer } from './server.js';
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const PORT = parseInt(process.env.MCP_HTTP_PORT ?? '9222');
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const HOST = process.env.MCP_HTTP_HOST ?? '0.0.0.0';
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const transports = new Map();
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const sessionServers = new Map();
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const app = express();
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app.use(express.json({ limit: '2mb' }));
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app.post('/mcp', async (req, res) => {
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try {
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const sid0 = req.headers['mcp-session-id'];
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if (sid0 && transports.has(sid0)) {
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await transports.get(sid0).handleRequest(req, res, req.body);
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return;
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}
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const transport = new StreamableHTTPServerTransport({
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sessionIdGenerator: () => randomUUID(),
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onsessioninitialized: (sid) => transports.set(sid, transport),
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});
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transport.onclose = () => {
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const sid = transport.sessionId;
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if (sid) {
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transports.delete(sid);
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sessionServers.delete(sid);
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}
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};
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const ss = createServer();
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await ss.connect(transport);
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const sid = transport.sessionId;
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if (sid) sessionServers.set(sid, ss);
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await transport.handleRequest(req, res, req.body);
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} catch (err) {
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console.error('[translate-mcp]', err);
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if (!res.headersSent) res.status(500).json({ error: 'Internal' });
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}
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});
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app.get('/mcp', async (req, res) => {
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const sid = req.headers['mcp-session-id'];
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if (!sid || !transports.has(sid)) { res.status(400).json({ error: 'bad' }); return; }
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await transports.get(sid).handleRequest(req, res);
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});
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app.delete('/mcp', async (req, res) => {
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const sid = req.headers['mcp-session-id'];
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if (!sid || !transports.has(sid)) { res.status(400).json({ error: 'bad' }); return; }
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await transports.get(sid).handleRequest(req, res);
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});
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app.get('/health', (_req, res) =>
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res.json({ status: 'ok', server: 'translate-mcp', activeSessions: transports.size })
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);
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app.listen(PORT, HOST, () =>
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console.error(`translate-mcp: HTTP on http://${HOST}:${PORT}/mcp`)
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);
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process.on('SIGINT', () => process.exit(0));
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process.on('SIGTERM', () => process.exit(0));
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84
src/llm.js
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84
src/llm.js
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@@ -0,0 +1,84 @@
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/**
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* LiteLLM client: embeddings + gemini-2.5-flash chat completions.
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*/
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const LITELLM_URL = process.env.LITELLM_URL ?? 'http://llm:4000';
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const LITELLM_KEY = process.env.LITELLM_API_KEY ?? '';
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const TRANSLATE_MODEL = process.env.TRANSLATE_MODEL ?? 'gemini-2.5-flash';
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const EMBED_MODEL = process.env.TRANSLATE_EMBED_MODEL ?? 'mxbai-embed-large';
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function headers() {
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const h = { 'Content-Type': 'application/json' };
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if (LITELLM_KEY) h['x-litellm-api-key'] = LITELLM_KEY;
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return h;
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}
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export async function embed(text) {
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const r = await fetch(`${LITELLM_URL}/v1/embeddings`, {
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method: 'POST',
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headers: headers(),
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body: JSON.stringify({ model: EMBED_MODEL, input: text }),
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});
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if (!r.ok) throw new Error(`embed ${r.status}: ${await r.text()}`);
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const j = await r.json();
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return j.data[0].embedding;
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}
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export async function translateWithGemini({ systemPrompt, text, sourceLang, targetLang }) {
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const userMsg = `Translate the following text from ${sourceLang} to ${targetLang}. Return ONLY the translation, no preamble, no quotes.\n\n${text}`;
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const r = await fetch(`${LITELLM_URL}/v1/chat/completions`, {
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method: 'POST',
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headers: headers(),
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body: JSON.stringify({
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model: TRANSLATE_MODEL,
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messages: [
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{ role: 'system', content: systemPrompt },
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{ role: 'user', content: userMsg },
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],
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temperature: 0.2,
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}),
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});
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if (!r.ok) throw new Error(`translate ${r.status}: ${await r.text()}`);
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const j = await r.json();
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return {
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translated: (j.choices[0].message.content ?? '').trim(),
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model: j.model ?? TRANSLATE_MODEL,
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usage: j.usage ?? {},
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};
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}
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/**
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* Assemble tenant-aware system prompt.
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*/
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export function buildSystemPrompt(profile, glossary, tmExamples) {
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const parts = [];
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parts.push(
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`You are a professional translator for tenant '${profile.tenant}'. ` +
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'Preserve meaning, formatting, and inline HTML/Markdown exactly.'
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);
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if (profile.tone || profile.formality) {
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parts.push(`Tone: ${profile.tone ?? 'neutral'}. Formality: ${profile.formality ?? 'neutral'}.`);
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}
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if (profile.base_rules) parts.push(`Tenant rules: ${profile.base_rules}`);
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if (profile.preferred_author_voice) parts.push(`Author voice: ${profile.preferred_author_voice}.`);
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if (profile.direct_address_allowed === false) {
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parts.push('Do NOT address the reader directly (no Sie/du/vous/you). Rephrase with third-person references.');
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}
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if (Array.isArray(profile.forbidden_terms) && profile.forbidden_terms.length) {
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parts.push(`Forbidden terms (rewrite around them): ${profile.forbidden_terms.join(', ')}.`);
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}
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if (glossary.length) {
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const lines = glossary.map((g) => {
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const tag = g.is_dnt ? ' [DO NOT TRANSLATE]' : '';
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const hint = g.context_hint ? ` (${g.context_hint})` : '';
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return `- ${g.source_term} → ${g.target_term}${tag}${hint}`;
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});
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parts.push('Glossary (must use):\n' + lines.join('\n'));
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}
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const corrections = tmExamples.filter((e) => e.is_correction).slice(0, 3);
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if (corrections.length) {
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const ex = corrections.map((e) => ` SRC: "${e.source_text}"\n TGT: "${e.target_text}"`);
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parts.push('Prior human-corrected examples (match this style):\n' + ex.join('\n'));
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}
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return parts.join('\n\n');
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}
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243
src/server.js
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243
src/server.js
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@@ -0,0 +1,243 @@
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#!/usr/bin/env node
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/**
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* translate-mcp — per-tenant translation MCP.
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*
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* Tools:
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* translate — main translation call (TM + glossary + tone → gemini-2.5-flash)
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* search_tm — vector TM lookup
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* upsert_glossary — manage per-tenant glossary
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* record_correction — human-in-the-loop correction (boosts correction_weight)
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* list_tenants — list configured tenants
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* get_tenant_profile — inspect tone/rules for a tenant
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*
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* Runs in the LiteLLM MCP fleet (CF-3122). Registered in
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* Infrastructure/litellm/config.yaml as translate_mcp.
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*
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* Env:
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* TRANSLATE_DB_DSN postgresql://... (SmartTranslate DB with tenant columns)
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* LITELLM_URL default http://llm:4000
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* LITELLM_API_KEY optional
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* TRANSLATE_MODEL default gemini-2.5-flash
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* TRANSLATE_EMBED_MODEL default mxbai-embed-large
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* SENTRY_DSN optional
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*/
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import * as Sentry from '@sentry/node';
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import { Server } from '@modelcontextprotocol/sdk/server/index.js';
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import { CallToolRequestSchema, ListToolsRequestSchema } from '@modelcontextprotocol/sdk/types.js';
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import * as db from './db.js';
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import * as llm from './llm.js';
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if (process.env.SENTRY_DSN) {
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Sentry.init({
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dsn: process.env.SENTRY_DSN,
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environment: process.env.SENTRY_ENVIRONMENT ?? 'production',
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tracesSampleRate: 0.1,
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});
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}
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const TOOLS = [
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{
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name: 'translate',
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description:
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"Translate text for a known tenant using per-tenant TM, glossary, and tone rules. " +
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"Routes to gemini-2.5-flash via LiteLLM. Prefer this over inline translation for tenant content.",
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inputSchema: {
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type: 'object',
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required: ['text', 'tenant', 'target_lang'],
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properties: {
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text: { type: 'string' },
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tenant: { type: 'string', description: 'e.g. ifk, vpn-marketing, clicksports.de, etoro, matrixhost, agiliton' },
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target_lang: { type: 'string', description: 'ISO 639-1 (de, fr, en, ...)' },
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source_lang: { type: 'string', default: 'auto' },
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},
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},
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},
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{
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name: 'search_tm',
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description: 'Search the translation memory (vector) for a tenant.',
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inputSchema: {
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type: 'object',
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required: ['query', 'tenant', 'target_lang'],
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properties: {
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query: { type: 'string' },
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tenant: { type: 'string' },
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source_lang: { type: 'string', default: 'en' },
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target_lang: { type: 'string' },
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top_k: { type: 'integer', default: 5 },
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},
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},
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},
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{
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name: 'upsert_glossary',
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description: 'Add or update a glossary term for a tenant.',
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inputSchema: {
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type: 'object',
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required: ['tenant', 'source_term', 'target_term', 'source_lang', 'target_lang'],
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properties: {
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tenant: { type: 'string' },
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source_term: { type: 'string' },
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target_term: { type: 'string' },
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source_lang: { type: 'string' },
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target_lang: { type: 'string' },
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is_dnt: { type: 'boolean', default: false },
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notes: { type: 'string' },
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},
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},
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},
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{
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name: 'record_correction',
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description: 'Record a human-corrected translation (boosts correction_weight).',
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inputSchema: {
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type: 'object',
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required: ['tm_id', 'corrected_target', 'tenant'],
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properties: {
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tm_id: { type: 'string', description: 'UUID of translation_memory row' },
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corrected_target: { type: 'string' },
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tenant: { type: 'string' },
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},
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},
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},
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{
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name: 'list_tenants',
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description: 'List configured tenants.',
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inputSchema: { type: 'object', properties: {} },
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},
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{
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name: 'get_tenant_profile',
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description: 'Return the tenant profile row (tone, rules, glossary hints).',
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inputSchema: {
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type: 'object',
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required: ['tenant'],
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properties: { tenant: { type: 'string' } },
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},
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},
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];
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function ok(data) {
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return { content: [{ type: 'text', text: JSON.stringify(data, null, 2) }] };
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}
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function err(message, extra = {}) {
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return { content: [{ type: 'text', text: JSON.stringify({ error: message, ...extra }) }], isError: true };
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}
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async function handleTranslate(a) {
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const profile = await db.getTenantProfile(a.tenant);
|
||||
if (!profile) return err(`unknown tenant: ${a.tenant}`, { hint: 'call list_tenants' });
|
||||
|
||||
const sourceLang = (a.source_lang && a.source_lang !== 'auto') ? a.source_lang : (profile.default_source_lang ?? 'en');
|
||||
const targetLang = a.target_lang;
|
||||
|
||||
const exact = await db.tmExactHit(a.text, sourceLang, targetLang, a.tenant);
|
||||
if (exact && !exact.is_correction) {
|
||||
return ok({
|
||||
translated: exact.target_text,
|
||||
cache: 'exact',
|
||||
tm_id: exact.id,
|
||||
tenant: a.tenant,
|
||||
});
|
||||
}
|
||||
|
||||
const embedding = await llm.embed(a.text);
|
||||
const [tmHits, glossary] = await Promise.all([
|
||||
db.tmVectorHits(embedding, a.tenant, sourceLang, targetLang, 5),
|
||||
db.glossaryHits(a.text, a.tenant, sourceLang, targetLang),
|
||||
]);
|
||||
|
||||
const systemPrompt = llm.buildSystemPrompt(profile, glossary, tmHits);
|
||||
const result = await llm.translateWithGemini({
|
||||
systemPrompt,
|
||||
text: a.text,
|
||||
sourceLang,
|
||||
targetLang,
|
||||
});
|
||||
|
||||
const newId = await db.tmInsert({
|
||||
text: a.text,
|
||||
translated: result.translated,
|
||||
sourceLang,
|
||||
targetLang,
|
||||
tenant: a.tenant,
|
||||
embedding,
|
||||
modelUsed: result.model,
|
||||
});
|
||||
|
||||
return ok({
|
||||
translated: result.translated,
|
||||
cache: 'miss',
|
||||
tm_id: newId,
|
||||
tenant: a.tenant,
|
||||
model: result.model,
|
||||
tm_hits_used: tmHits.map((h) => h.id),
|
||||
glossary_applied: glossary.map((g) => g.source_term),
|
||||
usage: result.usage,
|
||||
});
|
||||
}
|
||||
|
||||
async function handleSearchTm(a) {
|
||||
const embedding = await llm.embed(a.query);
|
||||
const hits = await db.tmVectorHits(
|
||||
embedding,
|
||||
a.tenant,
|
||||
a.source_lang ?? 'en',
|
||||
a.target_lang,
|
||||
a.top_k ?? 5
|
||||
);
|
||||
return ok({ hits });
|
||||
}
|
||||
|
||||
async function handleUpsertGlossary(a) {
|
||||
await db.upsertGlossary({
|
||||
tenant: a.tenant,
|
||||
sourceTerm: a.source_term,
|
||||
targetTerm: a.target_term,
|
||||
sourceLang: a.source_lang,
|
||||
targetLang: a.target_lang,
|
||||
isDnt: a.is_dnt ?? false,
|
||||
notes: a.notes ?? null,
|
||||
});
|
||||
return ok({ ok: true });
|
||||
}
|
||||
|
||||
async function handleRecordCorrection(a) {
|
||||
await db.recordCorrection(a.tm_id, a.corrected_target, a.tenant);
|
||||
return ok({ ok: true });
|
||||
}
|
||||
|
||||
async function handleListTenants() {
|
||||
return ok({ tenants: await db.listTenants() });
|
||||
}
|
||||
|
||||
async function handleGetTenantProfile(a) {
|
||||
const p = await db.getTenantProfile(a.tenant);
|
||||
return p ? ok(p) : err('unknown tenant');
|
||||
}
|
||||
|
||||
const HANDLERS = {
|
||||
translate: handleTranslate,
|
||||
search_tm: handleSearchTm,
|
||||
upsert_glossary: handleUpsertGlossary,
|
||||
record_correction: handleRecordCorrection,
|
||||
list_tenants: handleListTenants,
|
||||
get_tenant_profile: handleGetTenantProfile,
|
||||
};
|
||||
|
||||
export function createServer() {
|
||||
const server = new Server(
|
||||
{ name: 'translate-mcp', version: '0.1.0' },
|
||||
{ capabilities: { tools: {} } }
|
||||
);
|
||||
server.setRequestHandler(ListToolsRequestSchema, async () => ({ tools: TOOLS }));
|
||||
server.setRequestHandler(CallToolRequestSchema, async (req) => {
|
||||
const { name, arguments: args = {} } = req.params;
|
||||
const handler = HANDLERS[name];
|
||||
if (!handler) return err(`unknown tool: ${name}`);
|
||||
try {
|
||||
return await handler(args);
|
||||
} catch (e) {
|
||||
console.error(`[translate-mcp] ${name} failed:`, e);
|
||||
Sentry.captureException?.(e);
|
||||
return err(String(e?.message ?? e), { tool: name });
|
||||
}
|
||||
});
|
||||
return server;
|
||||
}
|
||||
Reference in New Issue
Block a user