Anumana

Name: Anumana
Author: sinhaKAN-ra

by sinhaKAN-raversion 0.2.1i★ –iRuns on your computer

Registry nameio.github.sinhaKAN-ra/anumana

Install

Overview

Description from the maker (original English)

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

PopNx MCP’s three pillars

These three are never merged into one score: opinions, measurements and popularity are different things.

User voiceiNo reviews yetReviews open together with accounts in a later step
Our basic checksi1 of 1 passedChecked from public information; code not scanned yet
UsageiVery new↓ 28/wk · ★ –

What you can do with it

We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.

Connection: Runs on your computer
Transport: stdio
Category: Other
You need to fill in: ANUMANA_DSN, ANUMANA_TARGETS

Install

Pick the AI app you use, then copy the settings.

Before you install

Basic checks: 1 of 1 passed
Runs on your computer with your user’s permissions (files, network, apps).
You fill in yourself: ANUMANA_DSN, ANUMANA_TARGETS, ANUMANA_POLICIES

Values marked secret are keys to your account. Never share them or paste them into a chat.

You need

PyPIPython’s uv installed (for the uvx command)

Which app do you use?

ClaudeClaude CodeCursorVS CodeChatGPT

Pick your app above. We remember it, so next time the button says “Install in …”.

Open Claude Desktop → Settings → Developer → Edit Config, and paste this into claude_desktop_config.json

{
  "mcpServers": {
    "anumana": {
      "command": "uvx",
      "args": [
        "anumana-mcp"
      ],
      "env": {
        "ANUMANA_DSN": "your-value",
        "ANUMANA_TARGETS": "your-value",
        "ANUMANA_POLICIES": "your-value"
      }
    }
  }
}

Run this in your terminal

claude mcp add anumana --env ANUMANA_DSN=your-value --env ANUMANA_TARGETS=your-value --env ANUMANA_POLICIES=your-value -- uvx anumana-mcp

Fill in the empty values before running it.

Open in Cursor

Or copy the settings yourself

Paste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json

{
  "mcpServers": {
    "anumana": {
      "command": "uvx",
      "args": [
        "anumana-mcp"
      ],
      "env": {
        "ANUMANA_DSN": "your-value",
        "ANUMANA_TARGETS": "your-value",
        "ANUMANA_POLICIES": "your-value"
      }
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

Open in VS Code

Or copy the settings yourself

Paste into .vscode/mcp.json in your project

{
  "servers": {
    "anumana": {
      "type": "stdio",
      "command": "uvx",
      "args": [
        "anumana-mcp"
      ],
      "env": {
        "ANUMANA_DSN": "your-value",
        "ANUMANA_TARGETS": "your-value",
        "ANUMANA_POLICIES": "your-value"
      }
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

This one runs on your computer, and ChatGPT can only connect to servers on the internet. Pick another app.

Your choice stays on this device in a cookie; no account needed.

Values you fill in

ANUMANA_DSN secret env
Read-only connection string for the primary target database (e.g. postgresql://readonly@host:5432/db). Optional if ANUMANA_TARGETS is used.
ANUMANA_TARGETS secret env
JSON array of named targets for a multi-database setup, each with name, engine, and connection string.
ANUMANA_POLICIES env
JSON array of operator enforcement policies (block/warn/allow rules by risk tier or flag). Optional; open by default.

These settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.

What it can do

Runs on your computer; the tool list appears in your AI app after you install it.

Basic checksi

We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 9, 2026

✓ Pass
Maker matches the repo
The registry name was verified with the GitHub account sinhaKAN-ra, which owns the repo
… Not checked yet
Still maintained
We have not read this repo from GitHub yet
… Not checked yet
Has a licence
We have not read this repo from GitHub yet
– Not applicable
Endpoint responds
This one runs on your computer; there is no internet endpoint
– Not applicable
Sign-in with OAuth
Runs on your computer and uses local settings instead
… Not checked yet
No hidden instructions in tool descriptions
Runs on your computer; we can only read tool lists from internet endpoints

Compared with others in Otheri

More stars than 0% of the other 18875 servers in this category
Published to the registry more recently than 57% of the other 18875 servers in this category
Basic checks equal to or better than 100% of the other 18875 servers in this category

Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.

Common questions

Is Anumana safe?

It passed 1 of 1 applicable basic checks. We have not read the code. Before adding a key or token, see what access the server asks for and use a key with only the permissions you need.

What keys or codes does it need?

You need to fill in: ANUMANA_DSN, ANUMANA_TARGETS

Does it run on my computer or in the cloud?

It runs on your computer. Your AI app starts it when needed, and data does not pass through the maker’s servers (unless it calls an online service itself).

Is it still maintained?

The GitHub repo last changed –, and the latest version (0.2.1) reached the registry 35 days ago.

Which apps can use it?

Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Python (uv) on your computer.

What can I use instead?

In the same category: Spec Driven Development, Remote Desktop Commander, Vagaro MCP Server and API by Cotlet

Alternatives

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README from the maker

Open the README

Copied from the maker’s repo as written. PopNx MCP has not reviewed or endorsed it. HTML, images and scripts are removed. Links lead off this site.

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 `EXPLAIN`s, 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

Anumana

Overview

PopNx MCP’s three pillars

What you can do with it

Install

Before you install

Values you fill in

What it can do

Basic checksi

Maker matches the repo

Still maintained

Has a licence

Endpoint responds

Sign-in with OAuth

No hidden instructions in tool descriptions

Compared with others in Otheri

Common questions

Alternatives

Spec Driven Development

Remote Desktop Commander

Vagaro MCP Server and API by Cotlet

README from the maker

Anumana

What it does (the features)

The one honest rule

Install

or from source:

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

Try it with no database (30 seconds)

Test against a real Postgres

What's deliberately NOT here

License

Community & contact

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