Skip to main content

Build-aware code intelligence MCP server for embedded C/C++ firmware (Mbed OS, Zephyr, PlatformIO)

Project description

fw-context

mcp-name: io.github.turbyho/fw-context-mcp

MCP server for embedded C/C++ firmware — gives AI assistants (Claude Code, Cursor, OpenCode, etc.) real understanding of your codebase. Parses your actual build with libclang, extracts every symbol, and builds a persistent index with full-text search, call graph, and vector embeddings.

No hallucination. No grepping. No reading thousands of framework headers into context.

What it does

Your AI assistant goes from guessing to knowing:

"What does uart_init do and who calls it?"get_symbol_context("uart_init") — body, callers, callees in one call.

"Find all BLE advertising functions and how they're connected."search_code("ble advertising", kind="function")find_call_path("gap_init", "start_advertising")

"Show me the implementation of adc_read — not the declaration."get_source("adc_read") — exact body via libclang, no file reading.

"What would break if I change spi_transfer?"find_all_callers_recursive("spi_transfer") — every caller, direct and indirect.

"Give me a map of modem_msg.cpp before I read it."get_file_map("src/modem_msg.cpp") — 426 symbols grouped by kind.

19 MCP tools — symbol search, source reading, call-graph traversal, hotspot analysis, dead code detection, vector search. All backed by real compiler flags from compile_commands.json#ifdef-aware, not grep.

Quick start

# 1. Install
git clone git@github.com:turbyho/fw-context-mcp.git ~/.fw-context/src
uv venv ~/.fw-context/.venv --python 3.12
uv pip install --python ~/.fw-context/.venv/bin/python ~/.fw-context/src/
echo 'export PATH="$HOME/.fw-context/.venv/bin:$PATH"' >> ~/.zshrc

# 2. Register with your AI assistant
fw-context init

# 3. Generate compile_commands.json and index
cd your-firmware-project

# Mbed OS:
bear -- mbed compile --profile release

# Zephyr:
west build -b <board> -- -DCMAKE_EXPORT_COMPILE_COMMANDS=ON
fw-context index build/compile_commands.json

# PlatformIO:
pio run --target compiledb
fw-context index

# CMake / Make:
bear -- make
# or: bear -- cmake --build build

fw-context index

# 4. Restart your assistant and start asking about your code

For detailed prerequisites, Ollama setup, and AI assistant integration: Installation guide →

Why not just use LSP?

LSP servers (clangd, ccls) are excellent for interactive editing. But they have limitations for AI-assisted exploration:

Limitation fw-context solution
No full-text search across the codebase FTS5 over 6 columns — find "all functions related to modem init"
Index dies with the server — rebuild from scratch Persistent SQLite file — survives reboots, reads in milliseconds
Editor protocol, not AI protocol MCP tools purpose-built for AI assistant workflow
Blind to which #ifdef branch is active Uses real compiler flags from compile_commands.json

Use clangd for editing, fw-context for AI-assisted exploration.

Architecture

Data flow

   BUILD                          INDEX                          QUERY
   =====                          =====                          =====
   bear / west / pio    libclang parses each TU          AI assistant calls
   cmake / make         extracts symbols + refs          MCP tools over
        │               generates embeddings            JSON-RPC (stdio)
        ▼                       │                              │
   compile_commands      SQLite db on disk               lookup_symbol(…)
   .json                 ~/.fw-context/index/            search_code(…)
                         │            │                  find_callers(…)
                         ▼            ▼                  explain_symbol(…)
                    symbols + refs   vec0                 get_symbol_context(…)
                    (FTS5 index)   (vector KNN)                 │
                                                          ▼
                                                    AI assistant answers
                                                    your question about
                                                    the code

Components

   CLI (fw-context)            MCP server (fw-context-mcp)          Ollama (optional)
   ================            ===========================          ==================
   fw-context index            exposes 19 tools over               local LLM runtime
   fw-context export           JSON-RPC (stdio)                    HTTP :11434
   fw-context watch                  │                                  │
   fw-context status           search_code ───────────── lookup   smart_search ──▶ translates NL → FTS5 terms
   fw-context reset            lookup_symbol ─────────── prefix   explain_symbol ─▶ explains function
   fw-context init             smart_search ──────────── NL       embeddings ────▶ mxbai-embed-large
   fw-context search           get_file_map ──────────── file structure by kind
                               get_source ────────────── body
                               get_symbol_context ────── body+callers+callees
                               find_callers ──────────── direct callers
                               find_references ───────── all uses
                               find_call_path ────────── BFS in call graph
                               find_all_callers_recursive  transitive callers
                               find_callees_recursive ── transitive callees
                               find_dead_code ────────── never called
                               find_hotspots ─────────── most-called
                               get_active_build ──────── index health
                               reindex_file ──────────── re-parse one file
                               reset_index ───────────── delete + rebuild
                               list_projects ─────────── all indexed projects
                               check_ollama ──────────── verify LLM
Component Runs as Purpose
CLI (fw-context) User command Index, export, watch, status, reset, init, search
Indexer Called by CLI libclang parses every TU, stores in SQLite + FTS5 + vec0
MCP server (fw-context-mcp) Subprocess (AI assistant) 19 tools over JSON-RPC — search, graph, source, maintenance
Ollama (optional) Local daemon NL search, symbol explanation, embedding generation

Key capabilities

  • Fast lookups — FTS5 full-text search, prefix/exact symbol lookup, call-graph traversal
  • Natural-language search"how does the modem connect?" → finds network_registration, modem_attach, … (Ollama, optional)
  • Vector search — semantic similarity via sqlite-vec + Ollama embeddings, hybrid FTS5+vector re-ranking
  • Graph analytics — call paths, transitive callers/callees, dead code detection, hotspot analysis
  • Incremental indexing — only changed files re-parsed; auto-reindex on query detects and fixes staleness
  • Offline-first — index is a file on disk at ~/.fw-context/index/. No daemon, no cloud, no network.
  • #ifdef-aware — uses real compiler flags; sees exactly what your compiler sees

Supported ecosystems

Works with any build system that produces compile_commands.json:

Ecosystem Auto-detection
Mbed OS mbed-os/ directory or mbed_app.json
Zephyr RTOS west.yml or prj.conf
PlatformIO platformio.ini
Bare-metal / FreeRTOS Any build with bear
Custom toolchain Any build with bear

Subsequent runs are incremental — seconds for a few changed files.

Documentation

Document Covers
Installation Prerequisites, install, upgrade, Ollama setup, AI assistant integration
Tools Reference All 18 MCP tools, 8 CLI commands, internal workings, search pipeline
Configuration .fw-context/config.toml — global defaults, per-project overrides, every setting
MCP Server JSON-RPC protocol, tool schemas, error handling, debugging

Directory layout

~/.fw-context/
├── config.toml              # global defaults
├── .venv/                   # Python virtual environment
│   └── bin/
│       ├── fw-context       # CLI
│       └── fw-context-mcp   # MCP server
└── index/
    └── <project-id>/
        └── index.db         # SQLite + FTS5 + vec0 + refs

your-firmware/
├── .fw-context/
│   └── config.toml          # per-project overrides
└── compile_commands.json

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

fw_context_mcp-0.1.1.tar.gz (115.5 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

fw_context_mcp-0.1.1-py3-none-any.whl (76.5 kB view details)

Uploaded Python 3

File details

Details for the file fw_context_mcp-0.1.1.tar.gz.

File metadata

  • Download URL: fw_context_mcp-0.1.1.tar.gz
  • Upload date:
  • Size: 115.5 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.14.5

File hashes

Hashes for fw_context_mcp-0.1.1.tar.gz
Algorithm Hash digest
SHA256 a4b1e1aaeb03e958785f096aa977c64ea8eda7c4b1cb768d98d784200a6334c3
MD5 5745538dc84a9ab4d905fde6bd4f6a7d
BLAKE2b-256 d46c8df03d7c7eb18a9cc0a851ba241e3018f87161f01b5db59119b4e36ed085

See more details on using hashes here.

File details

Details for the file fw_context_mcp-0.1.1-py3-none-any.whl.

File metadata

  • Download URL: fw_context_mcp-0.1.1-py3-none-any.whl
  • Upload date:
  • Size: 76.5 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.14.5

File hashes

Hashes for fw_context_mcp-0.1.1-py3-none-any.whl
Algorithm Hash digest
SHA256 43f69883239ec6b974f8a17508f9d893a7c05b44cb3e6c9c2c6c9497017022a5
MD5 1f17e3c37ea47dcc9817b4ecbb4c4ce9
BLAKE2b-256 22600fe43de1369d2b4a22a5b795515e98aed924a12c3fd154dfabaafbb94699

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page