Fortran Companion MCP Server

A Model Context Protocol (MCP) server designed to enable AI models to write, format, compile, lint, and structure modern Fortran code (F2003/F2018/F2023) using best practices and industry design patterns.

🌟 Why This Project?

AI models are trained on large volumes of legacy Fortran code (e.g., Fortran 66, 77, 90). When generating Fortran today, they regularly produce "AI Slop":

• Implicit variable typing (omitting implicit none).

• Non-standard vendor-specific types (like real*8 or integer*4 instead of standard kind parameters).

• Obsolescent control structures (numbered loops like do 10 i = 1, N and goto statements).

• Global state blocks (common and equivalence) which violate modern scoping rules.

• Lack of explicit interfaces (omitting module containers and dummy argument intent attributes).

This MCP server equips AI agents (in Cursor, Claude Desktop, or Antigravity CLI) with tools to statically check, auto-format, compile, and structure Fortran code. This creates a self-correcting feedback loop where the AI compiles and lints its own work, ensuring only high-quality, modern Fortran is delivered.

Related MCP server: MCP Project Standards Server

🔁 How It Works

You don't call these tools yourself — your AI assistant does, automatically. This is a server your AI talks to — not a CLI you run, not a library you import.

1. Install it once into your MCP-capable AI host (Claude Desktop, Cursor, or Antigravity CLI — see Setup & Connection below).

2. Work with your AI as you normally would — ask it to write a new module, modernize a legacy routine, or review a file.

3. Behind the scenes, the AI uses these tools to check its own work — it lints, compiles, and formats what it just wrote, reads the diagnostics, and fixes its own mistakes before showing you the result.

The net effect: you get modern, standards-compliant Fortran instead of "AI Slop," without hand-correcting the model's output.

👥 Who It's For

• Engineers pairing with an AI on Fortran who want generated code to follow F2003/F2018 standards out of the box.

• Teams modernizing legacy F77/F90 code — convert fixed-format layouts, common blocks, and obsolete types into modular, intent-checked modern Fortran, with regression checks to prove behavior didn't change.

• Anyone auditing or onboarding onto a large codebase — get project-wide modernization metrics, a module dependency graph, and oversized "god-file" hot spots to see what needs attention first.

🛠️ What It Does (Tool Catalog)

The server exposes 26 MCP tools the AI can call, grouped by purpose:

Author & verify

• explain_best_practices — the modern-Fortran style guide the agent is told to follow.

• lint_code / lint_file — static analysis for obsolete types, missing intent, missing implicit none, fixed-format layout, and deprecated statements.

• format_code / format_file — clean indentation and formatting via fprettify.

• compile_project — strict gfortran/fpm compilation (-Wall -Wextra -Wimplicit-interface -fcheck=all -std=f2018).

• run_tests — runs the project's fpm/make test suite.

• validate_syntax / validate_syntax_file — fast, build-free syntax check via the fparser2 AST; preprocess-aware for source laced with C-preprocessor macros.

• initialize_project — scaffolds a modern modular project (fpm or Makefile layout) with intents and test templates.

Modernize & refactor

• modernize_file — converts F77 idioms (.eq. → ==, double precision → parameterized kinds, injects iso_fortran_env) and reformats.

• verify_regression — runs legacy vs. modernized binaries and compares output + exit status to prove no behavior change.

• convert_common_to_module — turns global common blocks into module-scoped state.

• rename_legacy_identifiers — safe, scoped identifier renaming.

• analyze_pure_candidates — finds procedures that could become pure.

• audit_implicit_interfaces — flags call sites that lack explicit interfaces.

• suggest_refactoring / suggest_refactoring_file — recommends idiomatic restructurings.

• suggest_design_pattern — serves architectural blueprints and code templates: OOP (classes, inheritance, polymorphism, deferred bindings), Generics (interface overloading), RAII (allocatable cleanup), Callbacks (Strategy via abstract interfaces / procedure pointers), and C-Interop (iso_c_binding).

Understand a codebase

• project_metrics — per-file and aggregate modernization scores and legacy-feature counts.

• dependency_graph — module use graph with fan-in/fan-out, keystone modules, and mutable-global-state detection.

• find_large_units — oversized procedures/modules ranked by length, nesting, and select case arity.

Scaffold & interoperate

• scaffold_unit_test / scaffold_hpc_grid — unit-test and HPC grid boilerplate.

• generate_c_bindings / generate_python_interface — iso_c_binding C bindings and Python (ctypes/numpy) interfaces.

📂 Project Structure

fortran-mcp/
├── pyproject.toml              # Python project configuration & dependencies
├── README.md                   # Project overview & documentation
├── session_handoff.md          # Context handoff for AI agents
├── docs/
│   └── FEATURE_IDEAS.md        # Prioritized backlog from large-codebase field use
├── src/
│   └── fortran_mcp/
│       ├── __init__.py
│       ├── linter.py           # Custom Fortran linter engine
│       ├── design_patterns.md  # Design-pattern blueprints served by the server
│       └── server.py           # FastMCP tool definitions and entrypoint
├── test/                       # Demonstration and verification test suite
│   ├── unformatted_legacy.f90  # Non-compliant Fortran demonstration file
│   ├── modern_compliant.f90    # Formatted, best-practice compliant Fortran file
│   ├── run_benchmarks.py       # Regression & benchmark harness (run in CI)
│   ├── illustrate_mcp_tools.py # Python script orchestrating the MCP tool calls
│   └── README.md               # Test runner documentation
└── dist/                       # Packaged python distributions (tar/wheel)

⚙️ Setup & Connection

This server uses the standard Python MCP SDK (fastmcp) and requires Python 3.14+ (or 3.10+ compatible environments) with uv or pip.

1. Add to Antigravity CLI

Add the server configuration to your local Antigravity config file at ~/.gemini/antigravity-cli/mcp_config.json:

{
  "mcpServers": {
    "fortran-companion": {
      "command": "uv",
      "args": [
        "--directory",
        "/path/to/fortran-mcp",
        "run",
        "fortran-mcp"
      ]
    }
  }
}

2. Add to Claude Desktop

To use this with Claude Desktop, insert the block below into your claude_desktop_config.json (typically under ~/Library/Application Support/Claude/ on macOS or %APPDATA%/Claude/ on Windows):

{
  "mcpServers": {
    "fortran-companion": {
      "command": "uv",
      "args": [
        "--directory",
        "/path/to/fortran-mcp",
        "run",
        "fortran-mcp"
      ]
    }
  }
}

🚀 How to Test

Start a new conversation session with any MCP-capable host (like Claude or Antigravity) in this workspace. Try prompts like:

"Design a modules-based modern Fortran project that integrates a user-defined function using midpoint integration. Make sure you use modern intents, explicit kinds, and test it."

The agent will use the MCP tools to bootstrap the structure, write, lint, format, and compile the code successfully, correcting its errors on the fly.