Get exact signatures, parameters, return values, pitfalls, and GPU-verified examples for a fully qualified CuPy or nvmath-python function or class to write correct GPU code.
Get the exact signature, parameters, return value, pitfalls and a GPU-verified example for one function or class, e.g. "cupyx.scipy.sparse.linalg.cg".
Args:
name: Fully qualified dotted name.
| Name | Required | Description | Default |
|---|---|---|---|
| name | Yes |
Changes observed during successful MCP inspections.
v0.1.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and openWorldHint=false, so safety is covered. The description goes beyond them by enumerating the payload (exact signature, parameters, return value, pitfalls, GPU-verified example), which tells the agent what a call yields. It does not cover failure behavior for an unknown or ambiguous name.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The returning-content clause is front-loaded and the whole description is two short sentences plus an args line. The 'Args: name:' block partially restates the schema, a minor redundancy, but it carries the format detail that makes it worthwhile.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
There is no output schema, so the description correctly enumerates what comes back, and the single parameter's expected form is given. Missing only disambiguation from sibling tools and what happens when the name is not found or is ambiguous.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0% – the schema only says name is a string. The description compensates by specifying the required format ('Fully qualified dotted name') and supplying a concrete example ('cupyx.scipy.sparse.linalg.cg'), which is exactly the guidance an agent needs to avoid passing a bare symbol.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Specific verb+resource: it retrieves an API card containing signature, parameters, return value, pitfalls and a verified example for a single named callable, with a concrete example name. It does not explicitly contrast itself with the siblings search_api/get_example/check_snippet, so an agent must infer that this is the exact-name lookup rather than a fuzzy search.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Usage is only implied: the example dotted name and the word 'exact' suggest it should be used when the fully qualified symbol is already known. There is no explicit statement of when to use this instead of search_api (unknown name) or get_example (want only an example), and no error/precondition guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.