fittok 0.5.0

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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Install & use

fittok runs through uv — one tool for everything below, with no manual pip install. Install it once:

brew install uv                                  # macOS
# or any OS:  curl -LsSf https://astral.sh/uv/install.sh | sh

MCP server — Claude Code / Cursor / Windsurf

Claude Code:

claude mcp add fittok -s user -- uvx fittok

Restart Claude Code → /mcp → confirm fittok is connected. Then ask codebase questions normally — fittok fires automatically.

Cursor / Windsurf / any MCP client:

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

To make fittok trigger without mentioning it by name, add to CLAUDE.md:

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

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