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 }

---

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