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_initdo 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.cppbefore 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
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