mcp
БесплатноНе проверенThe grounded data layer for any LLM: governed SQL, metrics, lineage and catalog over your data.
Описание
The grounded data layer for any LLM: governed SQL, metrics, lineage and catalog over your data.
README
Connect AI clients (Claude Desktop, ChatGPT, Cursor, …) to the Matih data platform over the Model Context Protocol. Write SQL, profile tables, run analyses + charts, manage dashboards, and upload files — all through MCP tools, with OAuth/PKCE auth and PII-safe egress.
New here? See QUICKSTART.md for a step-by-step customer guide — create a token, connect Claude Desktop / Cursor, and copy-paste
curlsmoke tests — plus a troubleshooting table.
Use as a stdio MCP server (Claude Desktop / Cursor)
Add to your client's MCP config. The token is delivered via the environment — never as a CLI flag (which leaks into the OS process list):
{
"mcpServers": {
"matih": {
"command": "npx",
"args": ["-y", "@matihlabs/mcp", "https://<slug>.app.matih.ai/api/v1/mcp"],
"env": { "MATIH_MCP_TOKEN": "<your Matih bearer token>" }
}
}
}
The bridge turns the remote Matih HTTP MCP endpoint into a local stdio MCP server,
forwarding every tools/call, resources/read, and prompts/get to Matih.
Endpoint: replace
<slug>with your workspace slug — your Matih app lives athttps://<slug>.app.matih.ai(the bareapp.matih.aihost does not resolve). Get amat_agt_…token from Settings → Developer Tokens. See QUICKSTART.md for the full walkthrough.
Use as a library
import { McpClient, MatihTools, StaticTokenProvider } from "@matihlabs/mcp";
const client = new McpClient({
endpoint: "https://<slug>.app.matih.ai/api/v1/mcp",
tokenProvider: new StaticTokenProvider(process.env.MATIH_MCP_TOKEN!),
});
// discovery-first: fetch the LIVE tool surface (auto-initializes + caches)
const live = await client.tools();
console.log(live.map((t) => t.name));
// call ANY tool generically — including Beta tools with no typed wrapper
const hot = await client.callTool("get_hot_context", {});
// typed convenience wrappers for the 34 STABLE tools
const graph = await new MatihTools(client).exploreGraph({ query: "orders", depth: 2 });
const matih = new MatihTools(client);
const result = await matih.runSql({ connection_id: "<id>", sql: "select 1" });
OAuth (PKCE) instead of a static token
import { McpClient, OAuthTokenProvider } from "@matihlabs/mcp";
const tokenProvider = new OAuthTokenProvider({
resourceMetadataUrl: "https://<slug>.app.matih.ai/.well-known/oauth-protected-resource/api/v1/mcp",
clientId: "<registered client id>",
acquire: async ({ metadata, clientId, resource }) => {
// open metadata.authorization_endpoint (PKCE S256, resource=<resource>),
// capture the code at your redirect_uri, return { code, verifier, redirectUri }.
},
});
The provider runs RFC 9728 → RFC 8414 discovery, PKCE S256, RFC 8707 resource-bound
tokens, caches, and refreshes; a 401 invalid_token triggers one re-auth.
Tools
Discovery is the primary API — await client.tools() returns the live tool
descriptors and client.callTool(name, args) reaches every advertised tool.
The typed facade (MatihTools) covers the 34 stable tools
(STABLE_TOOL_NAMES):
- Query & SQL:
ask(natural-language → grounded answer + the SQL it ran),run_sql,run_analysis,get_query_result,export_result - Catalog & discovery:
list_connections,list_databases,list_schemas,list_tables,describe_table,profile_table - Ontology & semantic layer:
search_ontology,get_entity,get_relationships,get_semantic_model,get_glossary,explore_graph - Governed metrics & taxonomy:
get_metric,run_metric,draft_metric(write-class — proposes a DRAFTmetric_definto the human-gated DRAFT → REVIEW → APPROVED → SHIP pipeline),list_metric_drafts,export_semantic_model(OSI),get_taxonomy,export_taxonomy(SKOS) - Dashboards & charts:
create_chart,create_dashboard,get_dashboard,publish_dashboard - Uploads:
upload_file,upload_status,create_upload_url,finalize_upload - Identity & scope:
whoami,get_scope
The 1 Beta tool (BETA_TOOL_NAMES: get_hot_context) is deliberately
not hard-typed while its shape may evolve — call it via
client.callTool(name, args). The governed metrics & taxonomy group and
explore_graph were promoted from Beta to stable in July 2026 (PDR W3-1).
Plus catalog / lineage resources and the explain_metric prompt. Every
tool is bounded by the developer token's scope (connections + capabilities you
grant in Settings) and never exceeds your own permissions — get_scope shows
exactly what a token allows.
Notes
- Egress consent. Matih gates third-party-LLM data egress per tenant. If your
tenant hasn't accepted the data-processing agreement, calls return a clear
EGRESS_CONSENT_REQUIREDerror with a link to accept it. - Node ≥ 20 (uses native
fetch+node:crypto; zero runtime dependencies).
License: Apache-2.0 · https://matih.ai
Установить mcp в Claude Desktop, Claude Code, Cursor
unyly install ai-matihСтавит в Claude Desktop, Claude Code, Cursor и VS Code — сам разбирается с npx, uvx и сборкой из исходников.
Впервые? Поставь CLI: curl -fsSL https://unyly.org/install | sh
Или настроить вручную
Выполни в терминале:
claude mcp add ai-matih -- npx -y @matihlabs/mcpFAQ
mcp MCP бесплатный?
Да, mcp MCP бесплатный — установка в пару кликов через Unyly без оплаты.
Нужен ли API-ключ для mcp?
Нет, mcp работает без API-ключей и переменных окружения.
mcp — hosted или self-hosted?
Доступен hosted-вариант: Unyly запускает сервер в облаке, локальная установка не обязательна.
Как установить mcp в Claude Desktop, Claude Code или Cursor?
Открой mcp на unyly.org, выбери вкладку своего клиента (Claude Desktop, Claude Code, Cursor) и нажми Install — конфиг сгенерируется автоматически, без правки JSON.
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