fittok 0.5.1

MCP server that retrieves the most relevant source code for a query, within a token budget

fittok

Retrieve only the relevant source code for a question — instead of the model reading whole files — so an LLM answers codebase questions on a small, focused slice of context. Less input = fewer tokens, lower cost, faster answers.

Works three ways from one install: an MCP server, a CLI, and a Python library — plus a Claude Code plugin that injects context automatically.

📖 Full command reference → docs/HANDBOOK.md

mcp-name: io.github.likhithreddy/fittok

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How it works

codebase ──▶ graphify ──▶ slurp ──▶ readable slice ──▶ LLM answers
             (parse)      (select)   (trim to budget)

1. graphify — parses the repo with tree-sitter into a knowledge graph of functions / classes / methods (Python, JS, JSX, TS, TSX, Java, Go, Rust).
2. slurp — scores every node against the question with **semantic embeddings
   • TF-IDF + PageRank**, then selects only the genuinely relevant nodes via a relevance cliff (no budget-padding with noise).
3. readable output — returns the actual source code of those nodes, top-ranked in full and the supporting tail as signatures, trimmed to a budget. The model answers directly from it.

Graphs and embeddings are cached on disk (~/.cache/fittok), keyed by content — so after a code change only the changed functions re-embed.

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Installation

fittok ships as an MCP server, a CLI, and a Python library. It uses torch for embeddings, so a Python runtime must be present. Pick one runtime below, then follow the section for your client.

Every config below launches fittok as uvx fittok. If you chose Python or pipx, swap that for python -m fittok or pipx run fittok respectively.

Prerequisites — choose a runtime (one of)

A. uv — recommended (no Python needed on the machine)

curl -LsSf https://astral.sh/uv/install.sh | sh   # Linux / macOS
winget install astral-sh.uv                        # Windows
brew install uv                                    # macOS (Homebrew)

Launch command: uvx fittok — uv provisions its own Python + all deps in isolation. One static binary, so it's deployable org-wide via MDM/Intune/winget.

B. Python 3.10+ (already on the machine)

python -m pip install fittok      # Linux / macOS
py    -m pip install fittok       # Windows

Launch command: python -m fittok (Windows: py -m fittok).

Managed Linux may reject pip install with PEP 668 ("externally-managed-environment") — use option A to avoid it.

C. pipx — isolated, no global install

brew install pipx                               # macOS
pip install --user pipx && pipx ensurepath      # Linux / Windows

Launch command: pipx run fittok.

MCP server — Claude Code

claude mcp add fittok -s user -- uvx fittok

Restart Claude Code → /mcp → confirm fittok is connected, then ask codebase questions normally.

MCP server — VS Code / GitHub Copilot Chat

code --add-mcp '{"name":"fittok","command":"uvx","args":["fittok"]}'

Or paste into .vscode/mcp.json (workspace) or your user mcp.json:

{ "servers": { "fittok": { "type": "stdio", "command": "uvx", "args": ["fittok"] } } }

Then in Copilot Chat: Agent mode → enable fittok's tools (Configure Tools).

MCP server — GitHub Copilot CLI

copilot mcp add fittok -- uvx fittok
copilot mcp get fittok          # verify status + tools

MCP server — Cursor / Windsurf / any MCP client

{ "mcpServers": { "fittok": { "command": "uvx", "args": ["fittok"] } } }

Auto-trigger (optional, every MCP client)

To make fittok fire on every codebase question — without naming it — add this one line to your client's instructions file:

"For any codebase question, call fittok first and answer from its output."

Client	Instructions file
Claude Code	CLAUDE.md
GitHub Copilot	.github/copilot-instructions.md
Cursor	.cursor/rules/*.mdc (or .cursorrules)
Windsurf	.windsurfrules

CLI

cd /path/to/your/repo

uvx fittok index                                      # optional pre-warm (~15s, cached)
uvx fittok query "how does auth work"                 # LLM answers from relevant code
uvx fittok query "how does auth work" --budget 1500   # cap the slice at 1500 tokens
uvx fittok query "how does auth work" --code          # raw relevant code, no LLM
uvx fittok graph                                      # interactive browser graph
uvx fittok graph --query "auth"                       # graph with relevant nodes highlighted

query sends the relevant code slice to an LLM and streams the answer. Set one key in your shell and it just works:

export ANTHROPIC_API_KEY="sk-ant-..."   # → claude-haiku-4-5  (recommended)
export OPENAI_API_KEY="sk-..."          # → gpt-4o-mini  (fallback)

Users of Claude Code already have ANTHROPIC_API_KEY set — no extra step needed. If neither key is set, fittok falls back to --code and prints a setup hint.

graph requires pyvis: uv pip install "fittok[ui]".

Python library

uv add fittok            # in a uv project   (or:  uv pip install fittok  in a venv)

from fittok import optimize

result = optimize("/path/to/repo", "how does authentication work", token_budget=1500)
print(result["optimized_context"])   # the relevant code slice
print(result["savings"])             # token reduction stats

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Token savings — honest numbers

On a real Next.js/TS repo (~5k functions), fittok returns a ~1.5–3.5k-token slice instead of the model reading 15–20k+ tokens of files — an ~80–90% reduction on input, deterministic and reported in the savings footer.

On Opus 4.8, a broad question cost ~84k total tokens without fittok vs ~27k with it — because fittok replaced a 58k-token Explore subagent with one tool call.

How to measure it honestly:

• Use the 🪙 saved X% footer or your API bill (total tokens).
• Do not judge by Claude Code's /context Messages number — it excludes subagent tokens and is dominated by model reasoning, which fittok doesn't touch.

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Configuration

Variable	Default	Description
ANTHROPIC_API_KEY	—	Enables LLM answers via claude-haiku-4-5
OPENAI_API_KEY	—	Fallback LLM via gpt-4o-mini
FITTOK_SHOW_SAVINGS	true	🪙 saved X% footer on MCP answers; set false to disable
FITTOK_EMBED_MODEL	all-MiniLM-L6-v2	Embedding model
FITTOK_DEVICE	auto	auto / cuda / mps / cpu
FITTOK_CACHE_DIR	~/.cache/fittok	Cache location

Full reference: docs/HANDBOOK.md

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Requirements

Python ≥ 3.10. First run downloads a ~90 MB embedding model. Optional extras:

• uv pip install "fittok[ui]" — graph visualizer (fittok graph)
• uv pip install "fittok[gpu]" — torch/CUDA for GPU-accelerated embeddings

License

MIT