List PyProximal solver names, optionally filtered by module and version, to quickly find available solvers for your optimization tasks.
List PyProximal solver names, optionally filtered by module (e.g. "primal").
pyproximal_version selects the PyProximal version (e.g. "0.10"); default is the latest.
| Name | Required | Description | Default |
|---|---|---|---|
| module | No | ||
| 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 provided, so the description carries the burden. Calling it a "list" implies a read-only operation, and the note that the version defaults to latest is genuinely useful behavioral context, but it never states there are no side effects and gives no indication of result volume or ordering.
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 sentences, the primary purpose and the filter front-loaded, with the version caveat after. No filler, though the line-broken formatting is slightly awkward.
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 not be explained, and both parameters are covered. For a simple two-parameter listing tool this is nearly sufficient; a brief note on how names relate to get_proxsolver would close the last gap.
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 description coverage is 0%, so the description must compensate, and it does: it explains that module filters results (with a concrete example "primal") and that pyproximal_version selects the version with a "0.10" example and a stated default. Only the accepted string formats remain implicit.
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 ("List PyProximal solver names") plus its filtering scope, which separates it from siblings like list_proxperators and get_proxsolver. It does not explicitly contrast with list_solvers, so an agent might hesitate between the two.
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?
The optional module filter and the version-default note imply how to use it, but there is no explicit when-to-use guidance and no pointer to get_proxsolver for fetching details on a returned name. Usage is inferable rather than stated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.