pglens

A PostgreSQL MCP server with tools for schema inspection, data exploration, query execution, and database health monitoring.

Motivation

Most Postgres MCP servers expose query and list_tables, and that's about it. Agents end up guessing column names, enum values, and join paths, which leads to multiple failed attempts before landing on working SQL.

pglens adds the tools that close those gaps: checking what values actually exist in a column, discovering foreign-key relationships, previewing sample data, and validating query plans. The idea is straightforward: let the agent look before it leaps.

Tools

Schema and discovery

Tool	What it does
database_info	Server version, database name, current user, encoding, timezone, uptime, size
list_schemas	Schemas with table and view counts
list_tables	Tables with row counts and descriptions
list_views	Views with their SQL definitions
list_extensions	Installed extensions and versions
describe_table	Columns, types, PKs, FKs, indexes, check constraints
find_related_tables	FK relationships in both directions
find_join_path	Multi-hop join paths between two tables via foreign keys
list_indexes	All indexes across a schema with types, sizes, and usage stats
list_functions	Stored functions/procedures with source code
list_triggers	Triggers on a table with definitions and status
list_policies	Row-level security policies on a table

Data exploration

Tool	What it does
table_row_counts	Exact row count via COUNT(*) (vs estimated in list_tables)
sample_rows	Random rows from a table
column_values	Distinct values with frequency counts
column_stats	Min, max, null fraction, distinct count, common values
search_data	Case-insensitive search across text columns
search_columns	Find columns by name across all tables
search_enum_values	Enum types and their allowed values

Query execution

Tool	What it does
explain_query	Query plan without execution
query	Read-only SQL with limit/offset pagination (default 500 rows)

Performance and health

Tool	What it does
table_stats	Index hit rates, dead tuples, vacuum timestamps
table_sizes	Disk usage per table, ranked by size
unused_indexes	Indexes that are never scanned
bloat_stats	Dead tuples, vacuum status, wraparound risk
active_queries	Currently running sessions and their queries
blocking_locks	Lock wait chains (who blocks whom)
sequence_health	Sequences approaching exhaustion
matview_status	Materialized view freshness and refresh eligibility

Safety before DDL

Tool	What it does
object_dependencies	What depends on a given object (views, functions, constraints)

There is also a query_guide prompt that describes a reasonable workflow for using these tools together.

A note on column_values

Agents frequently write WHERE status = 'active' when the actual value is 'Active' or 'enabled'. column_values returns the real distinct values in a column with counts, so the agent can pick the right one instead of guessing.

Installation

pip install pglens

Or with uv:

uv pip install pglens

Usage

pglens reads standard PostgreSQL environment variables.

export PGHOST=localhost
export PGUSER=myuser
export PGPASSWORD=mypassword
export PGDATABASE=mydb

pglens

Alternatively, set a connection string via PGLENS_DSN:

export PGLENS_DSN="postgresql://myuser:mypassword@localhost:5432/mydb"
pglens

When PGLENS_DSN is set, it takes precedence over individual PG* environment variables.

By default the server uses stdio transport. To run as an HTTP server for remote use:

pglens --transport streamable-http

Flag	Choices	Default	Description
--transport	stdio, streamable-http	stdio	MCP transport type

Claude Desktop

{
  "mcpServers": {
    "pglens": {
      "command": "pglens",
      "env": {
        "PGHOST": "localhost",
        "PGPORT": "5432",
        "PGUSER": "myuser",
        "PGPASSWORD": "mypassword",
        "PGDATABASE": "mydb"
      }
    }
  }
}

Claude Code

{
  "mcpServers": {
    "pglens": {
      "command": "pglens",
      "env": {
        "PGHOST": "localhost",
        "PGDATABASE": "mydb"
      }
    }
  }
}

Zed

{
  "context_servers": {
    "pglens": {
      "command": {
        "path": "pglens",
        "args": []
      }
    }
  }
}

Architecture

adapters/tools/*.py   (MCP tool definitions, organized by category)
    |
adapters/mcp_adapter.py  (FastMCP server, lifespan, pool management)
    |
adapters/asyncpg_adapter.py  (SQL queries, asyncpg pool)
    |
PostgreSQL

AsyncpgDatabase holds the asyncpg pool and all query methods. Tool modules in adapters/tools/ are thin wrappers that register MCP tools via decorators and delegate to it. All queries use pure pg_catalog introspection — no PostgreSQL extensions required.

Adding a tool

1. Add a method to AsyncpgDatabase in adapters/asyncpg_adapter.py
2. Add a @mcp.tool() function in the appropriate adapters/tools/*.py module

Safety

• All user-influenced queries run inside readonly=True transactions
• Table and column identifiers are escaped via PostgreSQL's quote_ident()
• No DDL tools are exposed

Requirements

• Python 3.11+
• PostgreSQL

License

MIT