Analyzes your codebase identifying important files based on dependency relationships. Generates diagrams and importance scores per file, helping AI assistant...
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FileScopeMCP
Your AI already knows how to code. Now it knows your codebase.
FileScopeMCP watches your code, ranks every file by importance, maps all dependencies, and keeps AI-generated summaries fresh in the background. When your LLM asks "what does this file do?" — it gets a real answer without reading the source.
Works with Claude Code, Hermes Agent, Codex, OpenClaw, Cursor AI, or as a standalone daemon. Supports TypeScript, JavaScript, Python, C, C++, Rust, Go, Ruby, Lua, Zig, PHP, C#, and Java.
Key Features
Importance ranking — every file scored 0-10 based on how many things depend on it, what it exports, and where it lives. Your LLM sees the critical files first.
Dependency mapping — bidirectional import tracking across all supported languages. AST-level extraction (tree-sitter) for TS/JS, Python, C, C++, Rust, Go, and Ruby; regex-based for Lua, Zig, PHP, C#, and Java. Finds circular dependencies too.
Symbol intelligence — extracts functions, classes, interfaces, types, enums, consts, modules, and structs from source via tree-sitter. find_symbol resolves names to file + line range. find_callers and find_callees map the call graph for TS/JS so your AI can answer "who calls this function?" before refactoring.
Always fresh — file watcher + semantic change detection means metadata updates automatically. AST-level diffing for TS/JS, LLM-powered analysis for everything else. Only re-processes what actually changed.
LLM broker — a background process coordinates all AI work through llama.cpp's llama-server (or any OpenAI-compatible HTTP API). Priority queue ensures interactive queries beat background processing. Runs on a single GPU.
Nexus dashboard — a web UI at localhost:1234 that lets you visually explore your codebase across all your repos. Interactive dependency graphs, file detail panels, live broker activity, and per-repo health monitoring.
Prerequisites
- Node.js >= 22 and npm (download)
- Build tools for native modules (
better-sqlite3,tree-sitter): - Linux:
sudo apt install build-essential python3 - macOS:
xcode-select --install - Windows: Visual Studio Build Tools with C++ workload
Quick Start
git clone https://github.com/admica/FileScopeMCP.git
cd FileScopeMCP
./build.sh # installs deps, compiles, registers with Claude Code./build.sh registers FileScopeMCP globally via claude mcp add --scope user (idempotent; re-run with npm run register-mcp). If the claude CLI is missing, the build still succeeds — see docs/mcp-clients.md for other MCP clients.
Open a Claude Code session in any project and FileScopeMCP auto-initializes. The MCP tools appear automatically — your AI can call them directly during conversation:
find_important_files(limit: 5)
status()Agent Runtimes (Hermes, Codex, OpenClaw)
Agent runtimes discover FileScopeMCP via the repo's AGENTS.md, which includes MCP registration config, broker/LLM setup, and a pointer to the portable skill file at skills/filescope-mcp/SKILL.md.
Hermes — add to ~/.hermes/config.yaml:
mcp_servers:
filescope:
command: "node"
args: ["/path/to/FileScopeMCP/dist/mcp-server.js"]
timeout: 120Already have a local LLM running? Point the broker at it — edit ~/.filescope/broker.json and set baseURL to your LLM's endpoint. See AGENTS.md for details.
LLM Summaries (Optional)
Run ./setup-llm.sh for a platform-specific guide to setting up llama.cpp's llama-server — see docs/llm-setup.md for details. On Linux you can also sudo ./setup-llm.sh --install-service to register llama-server as a systemd unit (logs flow to journalctl, OOM-protected, auto-restart on boot). The flag is a no-op under WSL2 since llama-server runs on the Windows host there. Without llama-server entirely, everything else still works (file tracking, dependencies, symbols, call graphs — just no LLM-generated summaries). If your agent runtime already has a local LLM, configure the broker to reuse it instead.
Add to your project's .gitignore:
.filescope/
.filescope-daemon.logLLM Monitoring (Optional)
If llama-server is running locally, an optional VictoriaMetrics + vmui stack gives you a single-pane dashboard for VRAM, RAM, swap, throughput, and cumulative work. Total resident footprint ~120 MB, capped via systemd cgroups so a misbehaving exporter can't OOM-kill llama-server.
sudo ./monitoring/install.shBrowse the dashboard at http://:8881/vmui/#/dashboards. See monitoring/ for the layout and uninstall script.
MCP Tools
| Tool | What it does |
|---|---|
status | Broker connection, queue depth, LLM progress, watcher state |
find_important_files | Top files by importance score with dependency counts |
get_file_summary | Everything about a file: summary, concepts, change impact, exports, deps, staleness |
list_files | Full file tree (no args) or flat top-N by importance (with maxItems) |
find_symbol | Resolve a symbol name to file + line range; supports prefix match via trailing * |
find_callers | Find all symbols that call a named symbol (TS/JS call graph) |
find_callees | Find all symbols that a named symbol calls (TS/JS call graph) |
search | Search file metadata across symbols, summaries, purpose, and paths |
list_changed_since | Files changed since a timestamp or git SHA |
get_communities | Louvain-clustered file groups by import coupling |
detect_cycles | Find circular dependency chains |
get_cycles_for_file | Cycles involving a specific file |
scan_all | Queue files for LLM summarization via the broker |
set_base_directory | Point at a different project |
set_file_summary | Manually set or override a file's LLM summary |
set_file_importance | Manually set a file's importance score (0-10) |
exclude_and_remove | Drop files/patterns from tracking (destructive) |
Nexus Dashboard
npm run build:nexus # one-time build (API + UI)
npm run nexus # starts at http://localhost:1234A read-only web dashboard that connects to every FileScopeMCP repo on your machine:
- Project view — file tree with importance heat colors and staleness indicators, click any file for full metadata
- Dependency graph — interactive Cytoscape.js visualization, filter by directory, click nodes to inspect
- System view — live broker status, per-repo token usage, streaming activity log
- Settings — manage which repos appear, remove or restore from blacklist
Auto-discovers repos by scanning for .filescope/data.db directories. No configuration needed.
How It Works
Your code changes
→ file watcher picks it up
→ AST diff classifies the change (exports? types? body only?)
→ symbols extracted (functions, classes, types, etc.)
→ call-site edges resolved (TS/JS: who calls what)
→ importance scores recalculated
→ staleness cascades to dependents (only if exports/types changed)
→ LLM broker regenerates summaries, concepts, change impact
→ your AI's next query gets fresh answersEverything lives in .filescope/data.db (SQLite, WAL mode) per project. The broker coordinates LLM work across all your repos via a Unix socket at ~/.filescope/broker.sock.
Documentation
| Doc | What's in it |
|---|---|
| AGENTS.md | Cross-agent context file — MCP registration, broker config, architecture (read by Hermes, Codex, OpenClaw) |
| FileScopeMCP Skill | Portable skill file — tool reference, workflows, tips for agents using FileScopeMCP |
| LLM Setup | llama.cpp / llama-server installation — Linux/macOS native (default), WSL2+Windows, or remote LAN |
| Configuration | Per-project config, broker config, ignore patterns |
| MCP Clients | Setup for Claude Code, Cursor AI, daemon mode |
| Troubleshooting | Common issues and fixes |
| Internals | Dependency detection, importance formula, symbol extraction, call-site edges, storage |
| LLM Monitoring | Optional VictoriaMetrics + vmui dashboard for the local llama-server |
License
Copyright (c) 2026 admica. All rights reserved. See LICENSE.
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