Retrieve a PyProximal solver's signature, parameters, notes, and examples, optionally for a specific version, to inspect or integrate it without searching source files.
Get signature, parameters, notes and examples of a PyProximal solver.
pyproximal_version selects the PyProximal version (e.g. "0.10"); default is the latest.
| Name | Required | Description | Default |
|---|---|---|---|
| name | Yes | ||
| pyproximal_version | No |
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Changes observed during successful MCP inspections.
v0.1.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are present, so the description carries the burden, but "Get" reasonably implies a read-only lookup and the sibling list_proxsolvers confirms the discovery pattern. It does not state behavior on an unknown solver name (error vs null) or resolve which version is actually consulted, which are the meaningful unknowns for this tool.
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?
Two short sentences, front-loaded with the resource and returned fields, followed by the version note. The phrase "pyproximal_version selects the PyProximal version" is mildly circular but the surrounding format example and default justify its place.
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?
An output schema exists, so return values need no further explanation. However, the required name parameter is undocumented in both schema and description, and no cross-reference to list_proxsolvers tells the agent where valid names come from, leaving a real gap for a lookup tool.
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%, so the description must compensate. It does this well for pyproximal_version, giving a concrete example format ("0.10") and stating that the default is latest, but it says nothing about the required name parameter (expected class name, casing, where to find valid values via list_proxsolvers).
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?
States a specific verb and resource ("Get ... of a PyProximal solver") and enumerates the returned content: signature, parameters, notes, examples. It does not explicitly distinguish itself from the sibling get_solver or get_proxoperator, so the agent must infer the domain split from the tool name alone.
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?
There is no guidance on when to call this versus list_proxsolvers (discovery) or get_solver (a similar lookup), and no prerequisites or failure conditions are mentioned. The only usage-like statement concerns the version parameter's default, which is parameter semantics rather than tool selection guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.