Lists models advertised by each configured endpoint, with optional per-endpoint limit and substring filtering, so you can identify available OpenAI-compatible models before probing.
List the models each configured endpoint advertises.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | optional max per endpoint | |
| contains | No | optional substring filter |
Changes observed during successful MCP inspections.
v0.1.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are supplied, so the description carries the full disclosure burden, yet it says nothing about contacting remote endpoints, latency, or what happens when an endpoint is unreachable or advertises nothing. "Advertises" hints at live network probing, a significant behavioral trait that is left implicit.
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?
A single well-formed sentence with the resource and scope front-loaded and no filler. It is efficiently sized, though its brevity contributes to the missing usage and return-shape context rather than compensating for it.
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?
The tool is simple (zero required parameters), and the description conveys the essential operation, which is adequate at a minimum-viable level. With no output schema and no annotations, however, the agent still cannot tell what the result looks like (model IDs grouped per endpoint?) or how failures are surfaced.
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% for both optional parameters ("optional max per endpoint", "optional substring filter"), so the schema already does the work and the baseline of 3 applies. The description adds nothing about whether the substring matches model names only, is case-sensitive, or whether it is applied per endpoint or globally.
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 the models") and scopes it to "each configured endpoint advertises," which tells an agent this is a discovery/inventory call rather than an inference call. It does not name or contrast with the abstract siblings (probe, ranking, recommend, report), so differentiation is inferred rather than stated.
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?
No guidance on when this tool should be chosen over probe, ranking, or recommend, and no prerequisites (e.g. that endpoints must already be configured). The agent must guess that this is the pre-flight inventory step before invoking an inference-related sibling.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.