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# cudaq-docs-mcp

An MCP server that serves NVIDIA [CUDA-Q](https://github.com/NVIDIA/cuda-quantum) documentation, API reference, and runnable examples to AI agents: version-pinned to the cudaq you actually have installed.

Community project, not affiliated with or endorsed by NVIDIA. CUDA-Q is a trademark of NVIDIA Corporation.

## Why

Quantum SDKs move faster than model training data. Ask an AI assistant to write CUDA-Q code and it answers from whatever it memorized: renamed APIs, retired target names, install steps for a version you do not run. The failure is version skew, and it lands where onboarding matters most: the first ten minutes.

This server gives any MCP-capable agent the current answer instead. Documentation search, exact API symbol resolution, complete runnable examples, and a backend-selection guide, all served from an index of the docs that match your installed cudaq package. No API keys and no embeddings: SQLite full-text search with BM25 ranking, on your machine, offline once the index exists.

## Quick start

Register the server with your client; on first use it downloads a prebuilt index (a couple of megabytes) automatically. Building locally is only needed for versions without a prebuilt asset:

```bash
uvx cudaq-docs-mcp build --version 0.14.0
```

**Claude Code**

```bash
claude mcp add cudaq-docs -- uvx cudaq-docs-mcp
```

**Claude Desktop** (`claude_desktop_config.json`), **Cursor** (`.cursor/mcp.json`), or any client that takes a JSON server map:

```json
{
  "mcpServers": {
    "cudaq-docs": {
      "command": "uvx",
      "args": ["cudaq-docs-mcp"]
    }
  }
}
```

**VS Code** (`.vscode/mcp.json`):

```json
{
  "servers": {
    "cudaq-docs": {
      "type": "stdio",
      "command": "uvx",
      "args": ["cudaq-docs-mcp"]
    }
  }
}
```

Prefer pip? `pip install cudaq-docs-mcp` and use `cudaq-docs-mcp` as the command.

## Tools

| Tool | What it returns |
| --- | --- |
| `search_docs(query, version?, limit?)` | Ranked doc excerpts with breadcrumbs and canonical URLs |
| `get_page(path, version?)` | One full documentation page as clean markdown |
| `find_api(name, language?, version?)` | Exact Python or C++ symbol, kind, doc URL, and an excerpt |
| `search_examples(query, language?, version?, limit?)` | Complete runnable programs from the CUDA-Q repository at the matching release |
| `list_targets(category?)` | All 24 execution targets: simulators, hardware providers, and clouds, with selection snippets and when-to-use guidance |

Resources: `cudaq://versions` (installed and indexed versions) and `cudaq://llms.txt` (CUDA-Q's own llms.txt for the served version).

## Version-pinned answers

Every tool resolves its docs version in this order:

1. An explicit `version` argument ("0.15.0", "latest")
2. The installed cudaq package, detected from distribution metadata (cudaq is never imported)
3. `latest`

Indexes are per-version. When a pinned index is missing the server says so in the response and serves `latest` instead, with the one command that fixes it. Skew becomes visible instead of silent.

## How it works

CUDA-Q publishes the raw material: a Sphinx inventory (`objects.inv`) listing every page and API symbol, markdown mirrors of each docs page, a per-version `llms.txt`, and example sources in the repository. This server builds on that groundwork:

- `objects.inv` is the crawl manifest and the API symbol table: no scraping heuristics
- each markdown mirror is cleaned of theme chrome, code blocks are rebuilt with their language, and heading anchors are preserved for deep links
- pages are chunked by heading and indexed in SQLite FTS5 (porter stemming, BM25 ranking)
- examples, snippets, and application sources are fetched from the GitHub release tag that matches the docs version

The whole index is one SQLite file per version in your cache directory (`cudaq-docs-mcp info` shows where). A nightly workflow rebuilds the `latest` index so refreshes stay a download, not a build.

## CLI

```bash
cudaq-docs-mcp            # serve MCP on stdio (what clients run)
cudaq-docs-mcp build      # build the index for your installed cudaq, else latest
cudaq-docs-mcp build --version 0.15.0
cudaq-docs-mcp info       # cache location, indexed versions, detected cudaq
```

Set `CUDAQ_DOCS_MCP_AUTOBUILD=1` to build automatically on first use, and `CUDAQ_DOCS_MCP_CACHE` to relocate the cache.

## Roadmap

- Prebuilt indexes for pinned release versions, not just `latest`
- An eval set of real developer questions, with published retrieval scores
- CUDA-QX library docs

## Contributing

Issues and PRs are welcome. Commits need a DCO sign-off (`git commit -s`); see [CONTRIBUTING.md](CONTRIBUTING.md). Built in the open with Claude Code.

## License

[Apache-2.0](LICENSE). Documentation content belongs to NVIDIA Corporation & Affiliates, originates from the Apache-2.0 licensed [NVIDIA/cuda-quantum](https://github.com/NVIDIA/cuda-quantum) repository, and every served result links back to the canonical page. See [NOTICE](NOTICE).

<!-- mcp-name: io.github.rogerawong/cudaq-docs-mcp -->

TDQS

A4.5/5.0

Scored across 5 tools

Disambiguation5/5

Each tool targets a distinct resource: docs search, page retrieval, API symbol resolution, example search, and target listing. The two search tools are clearly separated by corpus (docs vs examples), and get_page complements search_docs by fetching full pages.

Naming Consistency5/5

All tool names follow a consistent verb_noun snake_case pattern: search_docs, get_page, find_api, search_examples, list_targets. The verbs and nouns are descriptive and predictable.

Tool Count5/5

Five tools is well-scoped for a documentation server, covering discovery (search), retrieval (get_page), reference (find_api), examples, and backend selection without unnecessary bloat.

Completeness5/5

The tool set covers the primary ways users interact with CUDA-Q docs: searching for answers, reading full pages, looking up APIs, finding runnable examples, and selecting execution targets. There are no obvious gaps that would cause dead ends.

Maintenance

ActivityStale
ResponsivenessNo recent community issues