# Lap > Measure and improve the token-efficiency of agent-facing APIs (OpenAPI & MCP): a scorer, linter, the LAP profile, and a reproducible token benchmark. - **Type:** MCP server - **Install:** `agentstack add mcp-lcrazyblindl-lap` - **Verified:** Yes — security-reviewed for prompt injection and unsafe behavior - **Seller:** [lCrazyblindl](https://agentstack.voostack.com/s/lcrazyblindl) - **Installs:** 0 - **Category:** [Developer Tools](https://agentstack.voostack.com/c/developer-tools) - **Latest version:** 0.1.0 - **License:** MIT - **Upstream author:** [lCrazyblindl](https://github.com/lCrazyblindl) - **Source:** https://github.com/lCrazyblindl/lap ## Install ```sh agentstack add mcp-lcrazyblindl-lap ``` Requires the [AgentStack CLI](https://agentstack.voostack.com/docs/cli). Works with Claude Code, Cursor, and any MCP-compatible agent. ## About # lap — how many tokens does your API cost an LLM agent? [](https://github.com/lCrazyblindl/lap/actions/workflows/ci.yml) [](profile/llm-api-profile.md) [](https://pypi.org/project/lap-score/) · MIT · [Changelog](CHANGELOG.md) · [Live leaderboard](https://lcrazyblindl.github.io/lap/) Every agent session starts by paying for tool definitions it mostly won't use — and pays again for every call and every response. **lap** measures that, for any OpenAPI spec, live MCP server, or your own agent config: the token cost decomposed (**A** menu / **B** call / **C** result), a **0–100 grade**, the rule violations driving the cost, and an applicable patch for the fixable ones. Free, neutral, reproducible — a measuring stick, not a product. ## Try it (60 seconds) ```bash pip install "lap-score[mcp]" lap stack # YOUR installed MCP servers: tokens paid before you type a word lap score openapi.json # A/B/C decomposition + grade (also --mcp-url for live servers) lap lint openapi.json # the violations driving the cost (also --mcp "cmd" for servers) lap fix openapi.json --apply patched.json # fixable findings as an OpenAPI Overlay patch lap badge openapi.json # shields.io grade badge for your README ``` ``` $ lap stack server kind tools menu tokens compact time stdio 2 283 31 git stdio 12 1418 153 TOTAL 14 1701 184 Your agent pays ~1,701 tokens of tool menus at session start - before you type a word. $ lap fix api/openapi.json --apply patched.json [written] lap-overlay.yaml (6 action(s)) [written] patched.json (lint findings: 15 -> 3) # grade: B (72) -> A (91) ``` Everything is `--json`-able and CI-gateable (`--max-menu-tokens`, `--diff --max-growth`, `--fail-on`; a composite [GitHub Action](action.yml) and a [Spectral ruleset](spectral/README.md) ship in-repo). Full CLI docs: [`lap/README.md`](lap/README.md). ## What the measurements show **[The leaderboard](https://lcrazyblindl.github.io/lap/)** — 50 real public APIs, refreshed monthly. Their naive agent menus total **~11.2M tokens**; rendering the *same operations* compactly recovers **~82%** on average (lazy tool-search: ~86%). Nobody ships the compact form. | API | naive menu (bucket A) | LAP compact | saved | | --- | ---: | ---: | ---: | | Xero Accounting | 4,041,667 | 7,800 | **+99%** | | Kubernetes | 2,864,414 | 45,015 | **+98%** | | Amazon EC2 | 1,046,048 | 86,031 | **+92%** | | _…47 more, sortable, with history_ | | | | **Real tools, not just our own variants** — the same accounting pointed at the ecosystem, live, with billed calls where it matters: - **3 real OpenAPI→MCP generators** all emit menus *heavier* than the naive baseline, 5–28× heavier than compact ([GENERATORS](docs/GENERATORS.md), [MCP-SERVERS](docs/MCP-SERVERS.md)). - **20 popular published MCP servers, scored as installed** — menus from 42 to **21,411** tokens per session (Notion's official server, grade F); ~64k tokens if you connect them all, before the first user message ([MCP-LEADERBOARD](docs/MCP-LEADERBOARD.md), refreshed monthly, incl. a grade cross-check against another grader). - **Anthropic's Tool Search: verified live** — ~90% billed-token cut on a real 290-op API, server-enforced ([TOOL-SEARCH](docs/TOOL-SEARCH.md)). **Their code-execution: disputed on our workload** — heavier than naive in 5/5 repeats; its saving is *behavioral*, not structural ([CODE-EXEC](docs/CODE-EXEC.md)). - **A third-party optimizer's self-reported % was mismeasuring** — root-caused in its own source: character counts with an asymmetric formula ([MCP-COMPRESSOR](docs/MCP-COMPRESSOR.md)). - **Compression doesn't cost accuracy** — a 500-run live matrix (2 models × 10 tasks × 5 forms): every compressed form matched or beat the naive menu; the *cheapest correct answer* turned out to be model-dependent ([validation.md](experiments/token-bench/validation.md)). Results that don't flatter the thesis ship as prominently as the ones that do — the verified/disputed registry of the field's headline claims is [docs/FIELD.md](docs/FIELD.md), and the standard objections are priced out in [CACHE-ECONOMICS](docs/CACHE-ECONOMICS.md) ("isn't it cached?") and [TOKENIZERS](docs/TOKENIZERS.md) ("whose tokens?"). ## Who it's for - **You ship an API or MCP server** → `lap score`/`lint`/`fix` give you a number, the violations behind it, and a patch; the Action gates PRs that bloat the menu; `lap badge` shows the grade in your README. - **You build agents** → `lap stack` audits what your own config burns; the leaderboard is due-diligence before wiring up an API. - **You're choosing between MCP / Tool Search / code-execution / a query DSL** → this repo measured all of them on the same tasks with the same accounting ([token-bench](experiments/token-bench/README.md), [LANDSCAPE](docs/LANDSCAPE.md)). - **You design API conventions** → the [LAP profile](profile/llm-api-profile.md) is the rule set behind the linter; every rule cites its measurement, including the two that earned honest caveats. ## Project map - [`lap/`](lap/README.md) — the pip-installable toolkit (start here). - [`profile/`](profile/llm-api-profile.md) — the LAP profile: measured conventions, L1–L4 levels, the grade formula. - [`experiments/`](experiments/token-bench/README.md) — the benchmark ([pet-zoo](pet-zoo/README.md) testbed) + every measurement script behind the docs (leaderboard, spec-#2808 simulation, cache economics, tokenizer matrix, …). - [`docs/`](docs/LEADERBOARD.md) — the receipts: leaderboard (+ its [MCP-server twin](docs/MCP-LEADERBOARD.md)), real-tool tests, [FIELD](docs/FIELD.md) claims registry, [SPEC-2808](docs/SPEC-2808.md) input for the MCP spec discussion. - [`spectral/`](spectral/README.md) — the lint rules for existing Spectral setups. - [`ROADMAP.md`](ROADMAP.md) — the full staged history and what's next. ## Status & contributing **0.6.x**, pre-1.0, actively maintained, MIT — no telemetry, no paid tier, no company. Issues and PRs welcome: [CONTRIBUTING.md](CONTRIBUTING.md) covers dev setup and the house policies (vendor neutrality, claims need receipts), and there's a **"Score my API"** issue template — disputes of our numbers are explicitly invited. ## Source & license This open-source MCP server is cataloged on AgentStack and links to its original source — we do not rehost the code. - **Author:** [lCrazyblindl](https://github.com/lCrazyblindl) - **Source:** [lCrazyblindl/lap](https://github.com/lCrazyblindl/lap) - **License:** MIT Install and usage instructions live in the source repository linked above. ## Pricing - **Free** — Free ## Security capabilities Automated source analysis of v0.1.0 — what this tool can access: - **Network access:** no - **Filesystem access:** no - **Shell / process execution:** no - **Environment & secrets:** no - **Dynamic code execution:** no *"Yes" means the capability is present in the source — more access means more to trust, not that it is unsafe.* ## Versions - **0.1.0** — security scan: passed — Imported from the upstream source. ## Links - Listing page: https://agentstack.voostack.com/l/mcp-lcrazyblindl-lap - Seller: https://agentstack.voostack.com/s/lcrazyblindl - Browse the marketplace: https://agentstack.voostack.com/browse --- Listed on AgentStack — the marketplace for AI agent skills and MCP servers. Every listing is security-reviewed. Creators keep 70%.