
Matih
ai.matihPublished by matih.aiOfficial publisherv0.2.1Updated Oct 24, 2026
The grounded data layer for any LLM: governed SQL, metrics, lineage and catalog over your data.
Overview
Connects an AI assistant to the Matih data platform to run governed SQL, metrics, catalog and lineage queries, and build dashboards.
- What you can do
- Exposes the Matih data platform over MCP, with 34 stable tools plus one beta tool. Query and SQL tools include ask (natural language to grounded answer with the SQL it ran), run_sql, run_analysis, get_query_result and export_result. Catalog tools list connections, databases, schemas and tables and describe or profile tables; semantic-layer tools search the ontology, entities, relationships, semantic model, glossary and graph. Governed metrics and taxonomy tools read and draft metrics, and dashboard tools create charts and dashboards, while upload tools handle files. Catalog and lineage resources and an explain_metric prompt are also provided.
- When to use it
- Use it when an assistant needs to work against a Matih workspace: answering data questions with governed SQL, exploring tables and lineage, reading or drafting metrics, or producing charts and dashboards. It is aimed at teams already using Matih as their data layer rather than at general-purpose database access.
- Requirements
- A Matih workspace endpoint of the form https:// .app.matih.ai/api/v1/mcp, plus a Matih developer token (mat_agt_…) supplied as the MATIH_MCP_TOKEN environment variable. The local stdio bridge runs via npx and needs Node.js 20 or newer. OAuth with PKCE is an alternative to a static token. The tenant must have accepted the data-processing agreement for third-party LLM egress.
Installation
In SourceWeft
- Open Matih in the dashboard and add it to a workspace.
- Connect the required credentials. They are stored privately and never shown on public pages.
- Enable the server for the chats that should use its tools.
Web executable via Streamable HTTP. Remote servers run from the web runtime once configured in a workspace.
Use with an agent
Paste this prompt into Claude Code, Codex, Cursor or another agent — or into SourceWeft chat. The agent reads this server's install guide, shows you its publisher, endpoint and sign-in, and adds it to your client once you agree. Keys never go into a command.
Read https://sourceweft.com/mcp/ai.matih%2Fmcp/install.md and connect the MCP server it describes to my client. Before changing any config, show me its publisher, endpoint and sign-in method and wait for my OK. Never put an API key in a command or in a file I would commit; ask me where it should come from.Other MCP clients
Add this to your client's mcpServers config. The client completes OAuth sign-in on first connect.
{
"mcpServers": {
"matih": {
"type": "http",
"url": "https://mcp.matih.ai/api/v1/mcp"
}
}
}README
@matihlabs/mcp
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):
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
OAuth (PKCE) instead of a static token
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
Source: README.md at commit 8a2ca5d
Tools
0Version history
1- v0.2.1LatestSep 16, 2026
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