How do I install Anumana?

Anumana is a local plugin. Install it using PyPI package: anumana-mcp and add the generated configuration snippet to your AI app's MCP config file. Then restart your AI app.

Is Anumana safe to use?

Yes. Anumana passed MCP Marketplace's automated security scan with a score of 10/10 (low risk). Every server on MCP Marketplace is security-scanned before it's listed; see the full security report on this page for the findings and permissions.

What credentials does Anumana need?

Anumana requires the following credentials or environment variables: ANUMANA_DSN, ANUMANA_TARGETS, ANUMANA_POLICIES. You can find setup instructions on the server detail page.

What AI apps work with Anumana?

Anumana uses the Model Context Protocol (MCP) and works with any MCP-compatible AI app, including Claude, ChatGPT / Codex, Gemini, Copilot, Cursor, and more.

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Anumana MCP Server

by SinhaKAN Ra

Developer ToolsLow Risk10.0MCP RegistryLocal

Free

Server data from the Official MCP Registry

Predict query cost, explain the plan, and rewrite it before you run it — 12 DB engines.

About

Predict query cost, explain the plan, and rewrite it before you run it — 12 DB engines.

Security Report

10.0

Low Risk10.0Low Risk

Valid MCP server (1 strong, 3 medium validity signals). No known CVEs in dependencies. Package registry verified. Imported from the Official MCP Registry.

5 files analyzed · 1 issue found

Security scores are indicators to help you make informed decisions, not guarantees. Always review permissions before connecting any MCP server.

Permissions Required

This plugin requests these system permissions. Most are normal for its category.

database

Check that this permission is expected for this type of plugin.

What You'll Need

Set these up before or after installing:

Read-only connection string for the primary target database (e.g. postgresql://readonly@host:5432/db). Optional if ANUMANA_TARGETS is used.Required

Environment variable: ANUMANA_DSN

JSON array of named targets for a multi-database setup, each with name, engine, and connection string.Required

Environment variable: ANUMANA_TARGETS

JSON array of operator enforcement policies (block/warn/allow rules by risk tier or flag). Optional; open by default.Optional

Environment variable: ANUMANA_POLICIES

How to Install

Add this to your MCP configuration file:

{
  "mcpServers": {
    "io-github-sinhakan-ra-anumana": {
      "env": {
        "ANUMANA_DSN": "your-anumana-dsn-here",
        "ANUMANA_TARGETS": "your-anumana-targets-here",
        "ANUMANA_POLICIES": "your-anumana-policies-here"
      },
      "args": [
        "anumana-mcp"
      ],
      "command": "uvx"
    }
  }
}

Documentation

View on GitHub

From the project's GitHub README.

Anumana

Know what your query will cost — before you run it. Inference-grade foresight for every query your AI writes.

Anumana is an MCP server that catches the costly query your AI coding agent just wrote — before it runs or reaches a PR. It rides inside Claude, Cursor, Windsurf, Codex, Kiro, or any MCP-compatible agent, reads your real schema via EXPLAIN (never EXPLAIN ANALYZE), and tells you — in plain English — how the query behaves and whether it'll hurt.

Its scope is the queries AI agents actually generate: text-to-SQL today, and RAG / vector search (pgvector) alongside it — because an agent writing a similarity search has no idea it just triggered a brute-force scan over every embedding. Anumana is the feedback loop the agent is missing.

It is not another NL→SQL tool and not a DB-health dashboard. It does one job: stop AI-written database code from silently rotting production.

What it does (the features)

Tool	What it answers
`preflight_query`	"Will this SQL query be costly?" — risk tier (cheap/moderate/expensive/dangerous), rows scanned vs returned, scan strategy, and overhead flags. Without running it.
`preflight_vector_search`	"Will this RAG similarity search be costly?" — catches the vector traps a plain SQL check misses: brute-force scan with no HNSW/IVFFlat index, `top_k` too large, unbounded search, metadata-filter/ANN recall loss.
`rewrite_query`	"Make it cheaper." — a verified equivalent rewrite with before/after planner cost, plus index suggestions gated on selectivity (won't tell you to index a column when the filter matches most of the table). `engine="pgvector"` suggests an HNSW index.
`explain_query_working`	"How does this run?" — two layers: the logical gather order (FROM → WHERE → GROUP BY → HAVING → SELECT → ORDER BY → LIMIT) and the actual physical plan for your schema, step by step.
`preflight_schema_only`	"I haven't given you DB creds yet." — static analysis against pasted `CREATE TABLE` DDL, no connection. Offline, zero-trust front door.

Engines (12, across 7 paradigms): Postgres and SQLite are live-tested; MySQL, pgvector, MongoDB, DynamoDB, FalkorDB, Cassandra, Redshift, BigQuery, Snowflake and ClickHouse ship as offline-verified, untested adapters that are promoted to live one at a time. Full matrix + cost signals in SUPPORTED_ENGINES.md. The adapter interface is in DESIGN.md.

The one honest rule

Postgres planner cost is unitless — not milliseconds (docs). Anumana never fakes a ~3.2s number. It reports rows scanned, scan strategy, a risk tier, overhead flags, and the cost-delta of a rewrite — all defensible, nothing invented. Every estimate carries an accuracy tier (UPPER_BOUND live, HEURISTIC schema-only).

Install

pip install anumana-mcp          # once published to PyPI
# or from source:
pip install -e .

Then point your agent at it. The user installs it; the agent discovers the tools automatically on connect via the MCP tools/list handshake — there is no store to publish into.

Claude Desktop / Cursor / Windsurf / Kiro — `mcpServers` config block

{
  "mcpServers": {
    "anumana": {
      "command": "uvx",
      "args": ["anumana-mcp"],
      "env": { "ANUMANA_DSN": "postgres://readonly@localhost:5432/mydb" }
    }
  }
}

Use a read-only Postgres role. Anumana only ever EXPLAINs, but read-only is defence in depth. Omit ANUMANA_DSN to run in schema-only mode (DDL in, no DB).

Try it with no database (30 seconds)

python3 src/demo.py          # runs the engine on a canned plan, zero deps

Test against a real Postgres

# a throwaway table, then:
ANUMANA_DSN=postgres://localhost/mydb anumana-mcp

See src/live_test.py for a psql-backed harness that proves the real cost-delta and the selectivity gate on live data.

What's deliberately NOT here

No run_query (we never execute your SQL), no NL→SQL (the agent already does that), no DB-health reports, no dollar-billing. Staying narrow is the strategy.

License

MIT — see LICENSE.

Community & contact

Contributions welcome — see CONTRIBUTING.md and the Code of Conduct. Adding a database engine is the highest- leverage contribution; the adapter contract is small (SUPPORTED_ENGINES.md).

Bugs / ideas: open a GitHub issue.
Security: see SECURITY.md — report privately.
Maintainer: nomore.report@gmail.com

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