Skip to main content
Glama
crude-code

Crude Code MCP Server

Official
by crude-code

Crude Code — MCP Server & Renderer

An oil & gas data-analytics platform built as a Model Context Protocol server plus an inline renderer that draws results directly inside the host chat app (Claude Desktop / claude.ai).

The design principle: the model does the thinking; the server does the deterministic work. There are no inner agents. The host model explores a Postgres database with a guarded, read-only SQL tool, then publishes finished deliverables as claude.ai artifacts it builds itself — from raw run_sql data, or from run_valuation's payload plus a frozen deal-sheet template. Maps are the one surface the server still renders: it hands the renderer a spec it validates, hydrates, and serves once.

What's in here

Path

What it is

server/

FastMCP server (mcp_server.py), the valuation engine (valuation/), and maps (maps/)

renderer/

Inline React + TypeScript app (Vite, Tailwind) built to a single dist/app.html

prompts/

Model-facing prompts and the shared DB-schema reference

utils/

SQL guard, map handle store, identity, logging

tests/

Pytest suite covering the tools, engine, maps, and guards

See CLAUDE.md for the full architecture reference.

Related MCP server: nl2sql-mcp

The tools

  • run_sql — guarded, SELECT-only, capped exploration query

  • forecast_wells / run_valuation — well-decline forecasting and economics, returning the data behind a claude.ai deal-sheet artifact

  • map — a MapLibre GL well/unit/PLSS map

  • get_skill — fetches a packaged, occasional-use procedure (e.g. dataroom extraction)

  • save_dataroom_extraction — persists a dataroom extraction so the deal record outlives the chat

  • message_team — files bugs, feedback, and data requests to the team (durable row + best-effort email)

Requirements

  • Python 3.11+ and a virtualenv (.venv)

  • Node 20+ (for the renderer build)

  • A Postgres database whose schema matches utils/schemas.py and prompts/outer/shared_schema.md. Populating that database (primary-source ingestion) is out of scope for this repo — point CC_DB_URL at your own.

Quick start

# 1. Python deps
python -m venv .venv
.venv/bin/pip install -r requirements.txt

# 2. Configure environment
cp .env.example .env   # then fill in CC_DB_URL and SUPABASE_DATABASE_URL

# 3. Run the MCP server (port 9000, /mcp endpoint)
.venv/bin/python server/mcp_server.py

# 4. Build the renderer
cd renderer && npm install && npm run build   # -> dist/app.html

Testing

.venv/bin/pytest -q

Tests that need a database, the Anthropic API, or network access auto-skip when the corresponding environment variable is unset.

Maintenance & contributions

This is a working platform maintained by one person alongside a full-time job. It's open-sourced for transparency and as a reference for building real systems on MCP + skills. Issues and PRs are welcome, but responses are best-effort — please set expectations accordingly.

License

Apache 2.0.

A
license - permissive license
-
quality - not tested
B
maintenance

Maintenance

Maintainers
Response time
Release cycle
1Releases (12mo)
Commit activity

Resources

Unclaimed servers have limited discoverability.

Looking for Admin?

If you are the server author, to access and configure the admin panel.

Related MCP Servers

  • A
    license
    -
    quality
    D
    maintenance
    An MCP (Model Context Protocol) server that exposes natural language to SQL functionality, allowing any MCP-compatible client to convert plain English questions into SQL queries for database interaction using AI.
    3
    MIT
  • F
    license
    -
    quality
    F
    maintenance
    A production-ready MCP server that transforms natural language into safe, executable SQL queries with multi-database support and intelligent schema analysis.
    1
  • A
    license
    -
    quality
    A
    maintenance
    Security-first, read-only MCP server for Microsoft SQL Server, enabling safe natural-language querying of databases.
    17
    MIT

View all related MCP servers

Related MCP Connectors

  • GibsonAI MCP server: manage your databases with natural language

  • Analytical memory for AI agents: a real Postgres queried in plain English over MCP. One command.

  • MCP server providing access to the Scorecard API to evaluate and optimize LLM systems.

View all MCP Connectors

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/crude-code/mcp-app'

If you have feedback or need assistance with the MCP directory API, please join our Discord server