fw-context-mcp 0.10.0

Build-aware code intelligence MCP server for embedded C/C++ firmware

Your AI Assistant Is Blind to Your Firmware. Here's How to Fix It.

fw-context gives Claude, OpenCode, Cursor, and other AI assistants a compiler-accurate understanding of your embedded C/C++ codebase. No cloud. No guesswork. Just answers that match what actually compiles.

---

"I asked Claude who calls uart_init. It read four wrong files, missed the callback, and burned 8 000 tokens on a half-right answer. With fw-context, the same question took one tool call and 200 tokens — and the answer was compiler-verified." — The experience that built this tool

---

The 10-Second Pitch

Your AI assistant is smart enough to understand your firmware. It just can't see it properly.

Give Claude or OpenCode a Python question, and it reads three files, understands the imports, and gives you a precise answer. Give it the same question about your Zephyr project, and it greps through headers it shouldn't read, misses the callback hidden behind a function pointer, and burns thousands of tokens on the wrong #ifdef branch.

The model isn't the problem. The information it can access is.

fw-context is an MCP server that sits between your AI assistant and your codebase. Instead of the assistant reading source files blindly, it queries a compiler-accurate index built by parsing your project with the exact flags from compile_commands.json. The same -I paths. The same -D defines. The same CONFIG_* symbols as your actual build.

Your AI goes from grepping text to querying a database of what actually compiles.

Get started in 60 seconds →

---

LLM Alone vs LLM + fw-context — The Difference in One Table

Let's be concrete. Here's what happens when you ask real embedded development questions — first with an LLM alone, then with the same LLM backed by fw-context.

Question	LLM Alone	LLM + fw-context
"Who calls spi_transfer?"	Grep for spi_transfer → 47 matches in vendor HAL, declarations, and comments → manually filter → still uncertain about callbacks	find_callers("spi_transfer") → 4 direct callers at exact file:line, verified against compiled AST
"What would break if I change this signature?"	Read every caller manually → grep for function name in each file → hope you didn't miss the callback registration → low confidence	find_all_callers_recursive("spi_transfer", max_depth=5) → transitive tree of 12 callers across 8 files, exhaustive
"What does this function do?"	Read the file → trace includes to understand types → look up macros → read callees manually	get_symbol_context("boiler_control") → body + callers + callees + pre-computed plain-English analysis → one call
"Is there dead code?"	Generate function list → grep for each → filter entry points/ISRs/constructors → verify candidates → impractical for large codebases	find_dead_code(project_only=true) → candidates at exact file:line with dead/possibly_dead classification
"How does battery data reach the heating logic?"	Read modbus_poll → read handleData → search for batt_soc references → trace through boiler_control → piece together mentally	find_references("rt_data") → 25 references in 5 functions across 4 files → complete data flow map
"Find all code related to power management"	Grep power, sleep, low_power, pm_, suspend, resume → 300+ hits, 90% in vendor headers → overload	semantic_search("power management low power sleep modes") → top 10 conceptually related symbols ranked by similarity
"Give me an overview of this 4 300-line file"	Read file → 4 300 lines → thousands of tokens on one file → still don't know the structure	get_file_map("modem_msg.cpp") → 426 symbols grouped by kind (methods, fields, enums, nested types)
"What's the full dependency tree of main()?"	Read main → find each called function → read each of those → find their callees → exponential explosion of reading	find_callees_recursive("loop", max_depth=3) → 30 callees at depths 1-3, project + framework

The Token Economics

This isn't just about time. It's about what questions are even practical to ask.

Metric	LLM Alone	LLM + fw-context
Tokens to understand one function	3 000–8 000 (read file + includes)	200–500 (one get_symbol_context call)
Tokens to trace callers (3 levels)	15 000–40 000 (exponential file reading)	300–800 (one find_all_callers_recursive call)
Tokens to find all references to a variable	5 000–15 000 (grep + read each file)	200–400 (one find_references call)
Questions per conversation before context fills up	3–5	15–25

The AI with fw-context doesn't just answer faster — it can answer questions you'd never ask without it, because the token cost of asking would exhaust your context window before you got to the interesting part.

---

Why LLMs Struggle With Embedded C/C++

The problem isn't model capability. Claude, GPT-4, Gemini — they can all read and understand C code. The problem is that reading source files is the wrong way to understand compiled firmware.

1. The LLM reads text. The compiler reads translation units.

When your AI opens main.c, it sees 200 lines. When your compiler compiles main.c, it sees 15 000+ lines — every #include resolved, every macro expanded, every #ifdef branch collapsed to exactly one path per board.

Your AI answers based on the 200 lines it read. Your board runs the 15 000-line reality it didn't see.

Symptom: AI explains how gpio_pin_set() works in the generic HAL. But on your board, with your device tree, that function resolves to nct38xx_gpio_set() — a specific driver with timing characteristics that matter if you're toggling in a tight loop. The AI's answer is correct for the generic case. It's wrong for your board.

With fw-context: get_source("gpio_pin_set") returns the body as compiled — the specific driver your board uses. explain_symbol("gpio_pin_set") describes what actually runs.

2. The LLM sees all #ifdef branches. Your board runs one.

void uart_send(const uint8_t *data, size_t len) {
#if CONFIG_UART_ASYNC_API
    return uart_async_send(data, len);       // nRF52, STM32H7
#elif defined(CONFIG_UART_INTERRUPT_DRIVEN)
    return uart_int_send(data, len);         // STM32F4, some NXP
#elif defined(SOC_FAMILY_NRF)
    return nrf_uarte_send(data, len);        // nRF91 fallback
#else
    return uart_poll_send(data, len);        // everything else
#endif
}

The LLM sees all four implementations as dead text. It can't know which one is compiled. It might describe the polling implementation because it's at the bottom, or the async one because it's at the top — either way, it's guessing.

With fw-context: libclang applies your -DCONFIG_UART_ASYNC_API=1 from compile_commands.json. get_source("uart_send") returns ONLY the uart_async_send branch — because that's what compiled. No guessing.

3. The LLM can't see callbacks, ISRs, or function pointers

// This is how embedded code actually connects:
MB.onDataHandler(&handleData);           // Modbus callback
k_work_init(&work, sensor_handler);      // RTOS work queue
IRQ_DIRECT_CONNECT(UART0_IRQn, isr);     // interrupt vector
dev->api->send(data, len);               // driver virtual table

// None of these are visible to grep.
// None appear when the LLM searches for "who calls handleData?"

With fw-context: find_references("handleData") finds the registration at modbus_setup:6 — even though it's not a direct call, it IS a reference. find_callers("handleData") returns empty — which is itself the answer: "this is invoked through a callback mechanism, not a direct call."

The empty result is data, not failure.

4. Every LLM question starts from zero

Ask "who calls spi_transfer?" → LLM greps the codebase. Then "what does spi_transfer call?" → LLM greps again. Same headers. Same includes. Same tokens.

This is like asking a colleague a follow-up question and having them forget the entire previous conversation. The LLM has no index, so it has no memory of the codebase structure.

With fw-context: The index persists across queries. search_code → get_symbol_context → find_callees_recursive — each query builds on the index, not on re-reading files. The SQLite database is the shared memory.

5. Vendor code drowns application code

In a typical Zephyr project, 90%+ of compiled code is framework, HAL, and RTOS. Grep for gpio_set and you get 300 hits — 290 in vendor directories.

The LLM reads ESP-IDF's GPIO driver (400 lines) before it finds your one call in src/buttons.c:45. It doesn't know what's "your code" and what's "the platform" — they're all just files.

With fw-context: find_callers("gpio_set", project_only=true) returns only calls from your src/ and lib/ directories. search_code boosts project paths (1.2×) over vendor paths (0.85×) in result scoring. Your code surfaces first.

---

How fw-context Supercharges Your AI

The Architecture

Your project build (west / pio / bear)
    │
    ▼
compile_commands.json          ← exact compiler flags for every .c file
    │
    ▼
libclang parses every file     ← same -I, -D, --target as your compiler
    │
    ▼
SQLite index on disk           ← symbols + call graph + FTS5 + embeddings
    │                              + pre-computed LLM analysis
    ▼
29 MCP tools                   ← your AI queries the index, not files
    │
    ▼
Your AI assistant              ← Claude, OpenCode, Cursor, Codex, Kilo Code

The key design choice: The index is built from compile_commands.json using libclang — the same parser that powers clangd and Xcode. It applies your exact compiler flags. The #ifdef branches your board uses are resolved. The macros your -D flags define are expanded. The include paths your -I flags specify are followed.

When your AI asks "show me uart_init", it sees what your compiler compiled. Not the text on disk.

What the Index Contains

Layer	Technology	What it stores
Symbols	SQLite	name, kind, file, line, signature, docstring for every function/method/class/enum/variable
Full-text	FTS5	inverted index over names, qualified names, tokens, summaries — prefix and phrase search
Call graph	refs table	every call edge: who calls whom, who reads which variable
Vectors	sqlite-vec	1024-dimensional embeddings for semantic/concept search
Analysis	llm_analysis table	pre-computed plain-English explanations of every function

The 27 Tools

Your AI assistant gets these capabilities — not by reading files, but by querying the index:

🔍 Search & Discovery — Find anything without grepping

• search_code — FTS5 full-text with progressive fallback (5 strategies tried automatically)
• lookup_symbol — by name or prefix, definitions prioritized
• smart_search — natural language → FTS5 via Ollama ("how does the modem connect?")
• semantic_search — find code by concept, not keyword ("power saving modes")

📖 Code Understanding — Read code with compiler precision

• get_source — exact function body via libclang extent (not line number guesses)
• get_symbol_context — body + callers + callees + LLM analysis in ONE call
• get_file_map — symbol table of contents (4 300-line file → 426 symbols grouped by kind)
• explain_symbol — plain-English description, pre-computed during indexing
• get_file_analysis — per-file summary ("Implements control logic for the boiler system...")

🕸️ Call Graph & Dependencies — Understand what depends on what

• find_callers — direct callers with exact file:line
• find_all_callers_recursive — transitive callers (BFS, max_depth 5)
• find_callees_recursive — what does this depend on, directly and transitively?
• find_call_path — how does A reach B? shortest paths through the call graph
• find_references — every read, write, and call of a symbol
• find_dead_code — defined but never called (with project/vendor filtering)
• find_hotspots — most-called functions ranked by caller count
• find_wrapper_callers — which wrapper classes call which driver methods?
• find_indirect_call_sites — where is a function pointer field/variable actually invoked?
• find_indirect_targets — which functions are assigned to a callback field/param?
• trace_data_flow — follow a type through function signatures to a target

🏗️ C++ Analysis — Navigate classes and templates

• get_inheritance_chain — base classes, derived classes, transitive hierarchy
• get_class_members — full API surface grouped by kind
• get_method_overrides — virtual dispatch chain
• get_template_instances — all concrete instantiations of a template

🔧 Maintenance — Keep the index healthy

• get_active_build — health check: is the index stale?
• reindex_file — update one file after editing
• reset_index — delete and rebuild
• list_projects — what's indexed on this machine?
• check_ollama — is Ollama available for smart_search?

---

Real Prompts, Real Results

Here's what actually happens when you use an AI backed by fw-context. These are real prompts, real tool calls, and real responses from the HA_Boiler project (ESP32 heating controller, ~700 lines of C++).

Prompt: "Show me boiler_control and explain how it fits in the system"

LLM alone: Reads control.cpp (63 lines). Doesn't know who calls it. Doesn't know what it calls. Needs to search for boiler_control references manually. Burns ~3 000 tokens.

LLM + fw-context:

→ get_symbol_context("boiler_control")

Returns in ONE call:
  ✓ Function body (42 lines, libclang-exact extent)
  ✓ Callers: [ha_update at ha_mqtt.cpp:109]
  ✓ Callees: [modbus_is_stale at modbus.cpp:99, digitalRead, digitalWrite]
  ✓ LLM Analysis: "Controls the boiler system based on battery state,
    temperature readings, and time. Adjusts the relay state (BOILER2_PIN)
    to control heating based on battery fullness, temperature thresholds,
    and time-based rules. Side effects: updates batFull flag, reads relay
    state, determines new state, toggles BOILER2_PIN, logs to Serial."

Tokens: ~500. All the context the AI needs. One call.

Prompt: "What would break if I change the signature of modbus_poll?"

LLM alone: Greps for modbus_poll → finds the definition and the call in loop(). Thinks that's everything. Doesn't know about the timing dependencies or the pending request check. Answer: "Only loop() calls it, should be safe." Wrong — changing the timing behavior would affect Modbus polling cadence.

LLM + fw-context:

→ find_callers("modbus_poll")
  → loop at main.cpp:134

→ get_symbol_context("modbus_poll")
  → Body shows: internal 2s timing (MODBUS_INTERVAL_MS), WiFi check,
    MODBUS_MAX_PENDING guard, two addRequest calls with specific tokens

→ find_references("MODBUS_INTERVAL_MS")
  → Used only in modbus_poll (internal timing)

→ find_references("MODBUS_REG_SOC")
  → Used in modbus_poll (request token) and handleData (response routing)
  → Changing the polling signature could break the token-based response routing

AI's answer: "modbus_poll is called only from loop(). However, changing its
signature would affect: (a) the internal 2-second polling cadence, (b) the
token values used to route Modbus responses in handleData (MODBUS_REG_SOC
and MODBUS_REG_VOLTAGE), (c) the pending request guard. The token constants
are used in both modbus_poll (sender) and handleData (receiver) — they form
a protocol. Changing one side without the other breaks the response routing."

Tokens: ~1 200 total across 4 tool calls. Answer: complete and correct.

Prompt: "Is there dead code in this project?"

LLM alone: Has no practical way to answer this. Would need to read every file, enumerate every function, grep for each function name, filter out false positives — impossible within a single conversation. Best it can do: "I don't have a way to exhaustively check for dead code."

LLM + fw-context:

→ find_dead_code(project_only=true)

Returns: "No dead code found — every defined function has at least one caller."

Tokens: ~150. Answer: exhaustive, compiler-verified.

This result is genuine — the HA_Boiler project is 700 lines, well-maintained, and has no dead functions. That's valuable information. In a larger or older project, you'd get a list of candidates to investigate.

Prompt: "Trace how battery SOC data flows from the Modbus register to the boiler relay decision"

LLM alone: Reads modbus.cpp → reads control.cpp → pieces together the flow manually. Might miss the stale detection (modbus_is_stale) because it's a separate function. Might miss the hysteresis constants in config.h.

LLM + fw-context:

→ find_references("rt_data")
  → 25 references across 5 functions in 4 files

→ get_symbol_context("handleData")
  → Body + note: no callers (it's a callback)

→ explain_symbol("modbus_is_stale")
  → "Returns true when more than MODBUS_STALE_MS has passed since lastModbusSuccess"

→ get_source("boiler_control")
  → Shows modbus_is_stale() is the FIRST check — stale data → batFull = false

AI's data flow answer:
  1. modbus_poll() sends request for register 266 (SOC) every 2s
  2. eModbus library receives TCP response → calls handleData() callback
  3. handleData() validates range (0–1000), stores to rt_data.batt_soc
  4. boiler_control() reads rt_data.batt_soc (lines 28, 31)
  5. First check: modbus_is_stale() — if >5 min without response → batFull=false
  6. Battery full check: batt_soc >= BATT_SOC_THRESHOLD (80.0%)
  7. Hysteresis: drops below BATT_SOC_THRESHOLD - BATT_SOC_HYSTERESIS (78.0%)
     before batFull transitions to false
  8. batFull determines which temperature sensor and logic branch is used
  9. Final output: digitalWrite(BOILER2_PIN, ...) with 30s minimum toggle guard

Tokens: ~2 000. Answer: complete data flow with hysteresis, stale detection, and relay guard. Every step traceable to specific file:line.

---

Before and After: A Real Investigation

Here's the same task — understanding an unfamiliar embedded codebase — done two ways.

Without fw-context: The Manual Detective

Open main.cpp → find loop() → see ha_update() called.
Search for ha_update → open ha_mqtt.cpp → see it calls boiler_control().
Open control.cpp → read boiler_control() — 42 lines, 5 conditions.
Search for BATT_VOLTAGE_THRESHOLD → config.h.
Search for TEMP_SENTINEL → config.h.
Search for modbus_is_stale → modbus.cpp.
Read modbus_poll() — sends SOC and voltage requests.
Read handleData() — validates and stores Modbus responses.
Read getTemp() — DS18B20 with RunningMedian filter.
Try to piece together: sensor → rt_data → boiler_control → relay.
But who calls handleData? Grep finds nothing. Must be a callback.
Search for onDataHandler → found in modbus_setup(). The callback
is registered there. The actual caller is inside eModbus library.
→ Finally have a mental model of the data flow.

Total: 8 files read, multiple searches across codebase
Questions answered: "What does boiler_control do and how does the data flow?"
Confidence: Moderate — might have missed a reference or a macro

With fw-context: Query the Index

get_symbol_context("boiler_control")
→ Body + callers + callees + LLM analysis. One call.
→ Answers "what does this do and where does it fit?"

find_references("rt_data")
→ 25 references across 5 functions in 4 files.
→ Complete data flow map.

explain_symbol("modbus_poll")
→ Pre-computed plain-English.
→ "Polls the Modbus server for battery SOC and voltage..."

find_callers("handleData")
→ Empty — it's a callback.
→ This IS the answer: callback-based, invoked by Modbus library.

find_call_path("loop", "handleData")
→ No path found.
→ Confirms the callback boundary.

get_file_analysis("src/control.cpp")
→ "Implements control logic for the boiler system..."
→ Context for the whole module.

Total: 6 tool calls, zero file reads
Questions answered: Full data flow, architecture, timing, edge cases
Confidence: High — every reference is compiler-verified, every edge is from the AST

What Changed

	LLM Alone	LLM + fw-context
Files read	8 (entire files)	0 (index queries only)
Callback discovery	Manual grep + luck	find_callers returning empty — explicit signal
Macro resolution	Manual config.h reading	libclang already resolved them
Data flow confidence	Mental model, could miss references	25 references enumerated, every one accounted for
Questions you can ask	Limited by token budget	Limited by tool coverage (29 tools, wide)

---

For AI Assistant Users: What Changes Day-to-Day

If you use Claude Code, OpenCode, Cursor, or another MCP-capable assistant, adding fw-context changes what's practical:

Questions that become practical

• "Before I refactor this driver, show me every caller, including transitive ones."
• "Are there any functions in this codebase that nothing calls anymore?"
• "Where exactly is this global variable written to? I need every write site."
• "What's the call path from the main loop to the lowest-level UART byte send?"
• "Find everything in the project related to battery management — across all files."
• "What are the top 10 most-called functions? I want to focus testing there."
• "Give me a structural overview of this 5 000-line file before I read it."

Questions that become faster

• "What does this function do?" → 1 call, not 1 file read
• "Who calls this?" → 1 call, not a grep + manual filter
• "Where is this constant used?" → 1 call, not a grep across vendor headers
• "How does X reach Y?" → 1 call, not reading every intermediate function

Questions that become correct

The AI with fw-context answers based on what compiles. Not what's in the text file. The #ifdef branch your board uses. The include path your build specifies. The macro your -D flag defines.

When your AI says "spi_transfer is called by flash_write at drivers/flash.c:142", that's not a grep result. It's a compiler-verified edge from the AST.

---

Getting Started in 60 Seconds

1. Install

git clone [e-mail]:turbyho/fw-context-mcp.git ~/.fw-context/src
cd ~/.fw-context/src && make install
fw-context init          # registers with Claude Code, OpenCode, Cursor, etc.

Prerequisites: Python 3.11+, bear, libclang-dev. Ollama optional. Full installation guide →

2. Index Your Project

cd your-firmware-project
fw-context index

Auto-detects Zephyr, PlatformIO, Mbed OS, or wraps any build with bear. First indexing builds the full database. Subsequent runs are incremental — only changed files are re-parsed.

3. Start Asking Real Questions

"Show me boiler_control — body, callers, callees, and what it does."

"Find every function that reads or writes the battery voltage."

"If I change spi_transfer's signature, what's the full impact?"

"Is there any dead code in this project? Project code only."

"How does data get from the Modbus register to the relay output?"

"What are the most-called functions? I want to prioritize testing."

Your AI now queries the index instead of grepping files.

4. Stay Fresh

fw-context reindex src/control.cpp    # after editing — incremental, fast
fw-context index                       # after a big merge
# Or just work — auto-reindex detects stale files on query

Supported AI Assistants

Assistant	Registration
Claude Code	~/.claude/mcp.json
OpenCode	~/.config/opencode/mcp.json
Cursor	.cursor/mcp.json
Codex	~/.codex/mcp.json
Kilo Code	Inherits from Claude Code

Supported Build Systems

Ecosystem	How
PlatformIO	pio run --target compiledb
Zephyr RTOS	west build (CMake-based, auto)
Mbed OS	bear -- mbed compile
ESP-IDF	idf.py build + bear
Arduino	Via PlatformIO wrapper
CMake	-DCMAKE_EXPORT_COMPILE_COMMANDS=ON
Bare-metal / Make	bear -- make

---

Honest Limitations

Every tool has boundaries. Here are fw-context's — clearly stated.

Callbacks and function pointers

fw-context tracks function pointers end-to-end across three phases:

1. Registration — find_callers and find_references detect function pointers in assignments (driver.onData = &handler), call arguments (register_cb(&handler)), variable initializers (void (*fp)(int) = &handler), and designated struct initializers (.onReady = &handler). These appear as "indirect" call edges.

2. Invocation — find_indirect_call_sites finds where function pointers are actually called, whether through fields (driver.onData(buf, len)), variables (stored_callback(42)), or parameters (cb(args)).

3. Linking — find_indirect_targets connects assignments to call sites via the field's USR, answering "which functions can be called through onData?" with precise, compiler-verified results.

Registration through stored pointers in libraries (e.g. inside eModbus) where the actual call site is in precompiled code still has no call-graph edge because the invocation side is not indexed.

ISRs, RTOS primitives, message queues

Interrupt vectors, k_work_init(), k_msgq_put()/get() — these connect functions without appearing in the call graph. Data that flows through a message queue has no static call path.

Precompiled libraries

Nordic SoftDevice, TI BLE stack, precompiled WiFi blobs — no source to index. Calls into them will have missing callee edges.

Proprietary IDEs without compile_commands.json

IAR, Keil MDK, some Segger configurations — these don't generate compile_commands.json natively. Workaround: wrap the build with bear, which intercepts compiler invocations from any IDE that calls a command-line compiler.

Macro-heavy code

If macros generate function names via token pasting (X-Macros), the generated symbols are indexed, but the connection to the macro that produced them is lost. The expanded form is there; the meta-programming is not.

Indirect call sites (invocation side)

The invocation side is now covered — find_indirect_call_sites detects when function pointers are called through fields, variables, or parameters, and find_indirect_targets links them to their assignments. Dynamically-indexed arrays (handlers[i](args)), void*-erased callbacks, and multi-hop pointer chains through intermediate variables remain outside the reach of static analysis.

---

When To Use What

fw-context isn't meant to replace your existing tools. It fills the gap that none of them cover: giving AI assistants a compiler-accurate understanding of your codebase.

Tool	Best for	AI-native?
clangd	Code completion, go-to-def, inline diagnostics in your editor	❌ (LSP)
grep/rg	Quick text searches, finding string literals	❌
ctags/cscope	Legacy index-based navigation in Vim/Emacs	❌
Sourcegraph	Organization-wide code search with server infra	❌ (REST API)
fw-context	AI-assisted code exploration, understanding, and impact analysis	✅ (MCP)

The workflow: clangd in your editor for editing. fw-context in your AI assistant for exploration and understanding. Both use libclang. Both see the same compiled reality. They serve different moments in the development cycle.

---

FAQ

Does this send my firmware to the cloud?

Never. Everything runs locally: libclang, SQLite, Ollama (if used). No telemetry. No API keys. No network required after installation. Your proprietary firmware never leaves your machine.

Do I need a GPU or special hardware?

No. Ollama runs on CPU if no GPU is available. For the embedding model (mxbai-embed-large, ~670 MB) and the analysis model (qwen2.5-coder:14b, ~9 GB), CPU inference is slower but works fine for indexing (done once) and occasional smart_search queries.

Can I use it without Ollama at all?

Yes. All 27 tools work without Ollama. smart_search falls back to word-split FTS5. semantic_search falls back to search_code. explain_symbol returns source + prompt for your AI to process itself. The index and call graph are fully functional without any LLM dependency.

What if my index gets corrupted?

fw-context reset   # delete
fw-context index   # rebuild

The index is just a SQLite file. No permanent state.

Does it work with C++ templates and virtual methods?

Yes. Template instantiations are tracked (get_template_instances), virtual method overrides are mapped (get_method_overrides), and inheritance chains are traversable (get_inheritance_chain). All require the reference index (enabled by default).

How large a project can it handle?

Tested with projects up to 100 000+ symbols. The reference index has been verified with 1.2 million reference edges. Initial indexing time scales roughly linearly with translation unit count. Incremental re-indexing is always fast.

---

The Bottom Line

Your AI assistant is smart enough to understand your firmware. It fundamentally cannot do so by reading source files — because source files are an incomplete, configuration-dependent, macro-obscured shadow of what actually compiles and runs on your board.

fw-context fixes the information problem. It gives your AI the same view of your code that your compiler has. From that foundation, everything changes: answers are precise, conversations are shorter, token costs drop by an order of magnitude, and questions you'd never ask because they'd exhaust your context window become practical.

The model is fine. Give it better data.

---

Documentation

Document	Covers
Technical Overview	Architecture diagrams, directory layout, features, supported ecosystems — the original README
Installation Guide	Prerequisites, install, upgrade, Ollama setup, AI assistant integration
Tools Reference	All 29 MCP tools, 10 CLI commands, search pipeline internals
Configuration	.fw-context/config.toml + local.toml — shared project config and local developer overrides
MCP Server	JSON-RPC protocol, tool schemas, error handling, debugging

---

⭐ Star on GitHub  |  📖 Docs  |  🐛 Issues

Open source (MIT). No cloud. No daemon. No API keys. Your firmware never leaves your machine.