Which integrations are available for this server?

Allows AI agents to execute SQL queries, inspect schemas, and analyze query performance on one or more PostgreSQL databases registered as named projects.

How do I use query-executor?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@query-executor describe the schema for the default project" That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

query-executor

by Nitesh-Nandan

Overview Schema Related Servers Score Discussions

Python

Local

Query Executor MCP Server

A Model Context Protocol server that gives AI agents read (and optionally write) access to one or more PostgreSQL databases. Each database is registered as a named project — the agent picks the right one per call via project_id.

How it works

flowchart TD
    A[AI Agent\nClaude / Cursor / etc.] -->|MCP stdio| B[Query Executor\nMCP Server]

    B --> C{Resolve project_id}
    C -->|default| D[(default DB)]
    C -->|project-alpha| E[(project-alpha DB)]
    C -->|project-beta| F[(project-beta DB)]

    subgraph Tools
        T1[describe_postgres_schema]
        T2[execute_postgres]
        T3[explain_postgres]
        T4[pg_stat_statements]
    end

    B --> Tools
    Tools --> C

sequenceDiagram
    participant Agent
    participant Server as MCP Server
    participant Config as databases.json
    participant DB as PostgreSQL

    Agent->>Server: tool call (project_id, sql)
    Server->>Config: resolve project_id → DSN + mode
    Config-->>Server: { dsn, mode }
    alt mode = readonly
        Server->>Server: assert first token is SELECT/WITH
    end
    Server->>DB: execute query
    DB-->>Server: rows
    Server-->>Agent: JSON { rows, row_count }

Related MCP server: mcp-postgres

Features

Multi-project — connect to any number of PostgreSQL databases simultaneously; each call targets one via project_id
Per-project mode — readonly blocks all writes at the first SQL token; readwrite allows all SQL
Safe fallback — omitting project_id routes to the configured default project
Four tools — schema inspection, query execution, EXPLAIN ANALYZE, and slow-query analysis
stdio transport — works with any MCP client (Claude Desktop, Cursor, Claude Code, etc.)
Docker-ready — single image, credentials baked in at build time from gitignored files

Project layout

query-executor/
├── Dockerfile
├── Makefile
├── pyproject.toml
├── uv.lock
├── main.py                     # connection check entrypoint
├── .env                        # gitignored — copy from .env.example
├── databases.json              # gitignored — copy from databases.example.json
├── .env.example
├── databases.example.json
└── query_executor/
    ├── config.py               # loads .env + databases.json; single source of truth
    ├── query_connector.py      # raw asyncpg functions (no MCP imports)
    ├── tools.py                # Pydantic input models + tool implementations
    └── server.py               # FastMCP bootstrap + entrypoint

Quick start

Requires: UV and Docker.

1. Install dependencies

uv sync

2. Configure

cp .env.example .env
cp databases.example.json databases.json

Edit databases.json with your real connection strings:

{
  "default": {
    "dsn": "postgresql://user:password@localhost:5432/mydb",
    "mode": "readonly"
  },
  "staging": {
    "dsn": "postgresql://user:password@staging-host:5432/stagingdb",
    "mode": "readwrite"
  }
}

`mode`	Behaviour
`readonly`	Only `SELECT` / `WITH` queries are allowed. Any write or DDL raises an error before reaching the database.
`readwrite`	All SQL is permitted. Use only on non-production databases.

If a project does not specify mode, it defaults to readonly.

3. Check connections

make check

Welcome to Query Executor!
  Default project : default
    • default  [readonly] (default)
    • staging  [readwrite]

Testing database connections...
  [default]  mode=readonly  ... OK — PostgreSQL 15.4
  [staging]  mode=readwrite ... OK — PostgreSQL 15.4

All 2 connection(s) OK.

4. Build the Docker image

make build

MCP client configuration

The server runs over stdio — the MCP client spawns the process and communicates via stdin/stdout.

Claude Desktop

Config file: ~/Library/Application Support/Claude/claude_desktop_config.json

With UV (local):

{
  "mcpServers": {
    "query-executor": {
      "command": "uv",
      "args": ["run", "python", "-m", "query_executor.server"],
      "cwd": "/Users/niteshnandan/workspace/2026/query-executor"
    }
  }
}

With Docker:

{
  "mcpServers": {
    "query-executor": {
      "command": "docker",
      "args": ["run", "-i", "--rm", "query-executor"]
    }
  }
}

Cursor

Global config: ~/.cursor/mcp.json
Project config: .cursor/mcp.json

With UV (local):

{
  "mcpServers": {
    "query-executor": {
      "command": "uv",
      "args": ["run", "python", "-m", "query_executor.server"],
      "cwd": "/Users/niteshnandan/workspace/2026/query-executor"
    }
  }
}

With Docker:

{
  "mcpServers": {
    "query-executor": {
      "command": "docker",
      "args": ["run", "-i", "--rm", "query-executor"]
    }
  }
}

Claude Code

Add to your project's .claude/mcp.json:

With UV (local):

{
  "mcpServers": {
    "query-executor": {
      "type": "stdio",
      "command": "uv",
      "args": ["run", "python", "-m", "query_executor.server"],
      "cwd": "/Users/niteshnandan/workspace/2026/query-executor"
    }
  }
}

With Docker:

{
  "mcpServers": {
    "query-executor": {
      "type": "stdio",
      "command": "docker",
      "args": ["run", "-i", "--rm", "query-executor"]
    }
  }
}

Note: Replace /Users/niteshnandan/workspace/2026/query-executor with the actual path on your machine if sharing config with others.

Tools reference

`describe_postgres_schema`

Inspect tables, columns, foreign keys, and indexes for a given schema.

Call this first before writing any query — it gives you exact column names and types so you write correct SQL on the first attempt.

Parameter	Type	Default	Description
`postgres_schema`	string	`"public"`	Schema to inspect
`project_id`	string	default project	Target database

Returns { columns, foreign_keys, indexes }.

`execute_postgres`

Run a SQL query and get rows back as JSON.

Parameter	Type	Required	Description
`sql`	string	yes	SQL to execute. In `readonly` mode only SELECT/WITH are allowed.
`project_id`	string	no	Target database

Returns { rows: [...], row_count: N }.

`explain_postgres`

Run EXPLAIN (ANALYZE, BUFFERS, FORMAT JSON) on a query and return the execution plan.

Parameter	Type	Required	Description
`sql`	string	yes	SELECT query to analyse (do not include `EXPLAIN`)
`project_id`	string	no	Target database

Returns { plan: [...] }. Key things to check in the plan:

Seq Scan on a large table → missing index on the WHERE column
Actual rows ≫ Plan rows → stale statistics; run ANALYZE <table>
High shared_blks_read → I/O-bound; working set does not fit in shared_buffers

`pg_stat_statements`

Return the top N query patterns ranked by total cumulative execution time.

Parameter	Type	Default	Description
`limit`	integer	20	Number of queries to return (max 100)
`project_id`	string	no	Target database

Returns rows with total_exec_sec, mean_exec_sec, max_exec_sec, calls, shared_blks_hit, shared_blks_read.

Requires the pg_stat_statements extension. Enabled by default on AWS RDS, GCP Cloud SQL, and Supabase. On self-hosted Postgres: CREATE EXTENSION pg_stat_statements;

Recommended workflows

Explore an unknown database

describe_postgres_schema → execute_postgres

Debug a slow query

describe_postgres_schema  (check what indexes exist)
→ explain_postgres        (verify the planner uses them)
→ execute_postgres        (run once plan looks correct)

Performance audit

pg_stat_statements        (find the most expensive patterns)
→ explain_postgres        (drill into the worst offender)
→ describe_postgres_schema (check if a missing index would help)

Environment variables

Variable	Default	Description
`DATABASES_FILE`	`databases.json`	Path to the databases registry file
`DEFAULT_PROJECT`	`default`	Fallback project when `project_id` is omitted
`LOG_LEVEL`	`INFO`	Python logging level
`CONNECT_TIMEOUT`	`10.0`	asyncpg connection timeout in seconds
`EXPLAIN_TIMEOUT_MS`	`30000`	Statement timeout for EXPLAIN ANALYZE in milliseconds
`STAT_STATEMENTS_DEFAULT_LIMIT`	`20`	Default row limit for `pg_stat_statements`

Install Server

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quality

maintenance

How are these scores calculated?

Resources

GitHub Repository

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