Test OpenAI-compatible models with real requests to measure latency, success, streaming, and tool-calling behavior, so you can identify which models work now.
Probe models with real requests: latency, success, streaming, tool calls.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | max models (default 20) | |
| model | No | exact model id | |
| contains | No | substring filter | |
| endpoint | No | optional endpoint name | |
| check_tools | No | also test function calling |
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 full behavioral burden. It discloses that real requests are made and lists measured outcomes, but it omits important operational context such as cost, rate limits, permissions, latency expectations, and whether probing has side effects.
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 description is a single front-loaded sentence with no filler. It immediately communicates the core action and the measured dimensions. Every element earns 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?
For a tool that makes real requests and has no annotations or output schema, the description is too thin. It hints at return dimensions but does not explain result format, cost implications, or operational constraints. The schema covers parameters, but behavioral completeness is weak.
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 100%, so the input schema already documents all five optional parameters. The description adds no parameter-specific meaning beyond the schema and does not clarify ambiguous types such as string-valued limit or check_tools. A baseline 3 is appropriate because the schema does the heavy lifting.
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?
The description states a specific verb and resource: probing models with real requests. It also enumerates what is measured: latency, success, streaming, and tool calls. It does not explicitly distinguish itself from siblings like list_models or ranking, so it falls short of a 5.
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 description implies that the tool is used for live model probing rather than metadata listing, but it gives no explicit when-to-use guidance. It does not mention alternatives, exclusions, or conditions under which probing is preferred over ranking or recommending.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.