cudaq-docs-mcp
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| CUDAQ_DOCS_MCP_CACHE | No | Set to relocate the cache. | |
| CUDAQ_DOCS_MCP_AUTOBUILD | No | Set to 1 to build automatically on first use. |
Instructions
Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.
This server publishes no instructions, or was last inspected before Glama recorded them.
Capabilities
Features and capabilities supported by this server
Protocol revision2025-11-25
| Capability | Details |
|---|---|
| tools | {
"listChanged": false
} |
| prompts | {
"listChanged": false
} |
| resources | {
"subscribe": false,
"listChanged": false
} |
| experimental | {} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| search_docsA | Search the NVIDIA CUDA-Q documentation and return ranked excerpts. Use this before answering any CUDA-Q question from memory: the platform moves quickly and memorized APIs are often stale. Each result carries a breadcrumb, an excerpt, and the canonical doc URL to cite. Args: query: Natural language or keywords, for example "run kernel on GPU state vector" or "quantinuum credentials". version: Docs version such as "0.15.0" or "latest". Defaults to the installed cudaq version. limit: Maximum number of results (default 5). |
| get_pageA | Fetch one documentation page as clean markdown. Args: path: Page path as returned by search_docs, for example "using/quick_start" or "using/backends/sims/svsims". version: Docs version. Defaults to the installed cudaq version. |
| find_apiA | Resolve a CUDA-Q API symbol to its canonical definition and doc URL. Args: name: Symbol name, full or partial: "sample", "cudaq.observe", "qvector", "set_target". language: "python" or "cpp" to filter; omit for both. version: Docs version. Defaults to the installed cudaq version. |
| search_examplesA | Find complete, runnable CUDA-Q example programs. Sources are the example, snippet, and application files shipped in the CUDA-Q repository at the matching release. Prefer adapting these over writing kernels from memory. Args: query: What the example should show, for example "GHZ state", "VQE", or "noise model". language: "python" or "cpp" to filter; omit for both. version: Docs version. Defaults to the installed cudaq version. limit: Maximum number of examples (default 3). |
| list_targetsA | List CUDA-Q execution targets (backends) with guidance on choosing. Covers simulators (CPU, GPU state vector, tensor network, noisy, dynamics), quantum hardware providers, and cloud aggregators, each with selection snippets and doc URLs. Call this when deciding where to run a kernel or when the user names a provider. Args: category: Optional filter: "simulator", "hardware", or "cloud". |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
No prompts | |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
| versions_resource | Installed cudaq version, indexed docs versions, and the default. |
| llms_resource | The llms.txt published with the default docs version. |
TDQS
Scored across 5 tools
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.
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.
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.
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.