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Bike4Mind — the open-core AI workbench

list_models

Read-only

The live Bike4Mind model catalog — synced from the live Bike4Mind models API (the same models the app offers). Filter by type ('text'|'image'), provider, or a substring query. Returns name, provider, context window, max output, cost tier (text models), and a per-entry source for auditing.

Input Schema

NameRequiredDescriptionDefault
qNosubstring match on model name
typeNo
providerNoe.g. Anthropic, OpenAI, Google, xAI, Amazon Bedrock, Black Forest Labs

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.7/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

readOnlyHint already covers the safety profile, so the description's value-add is provenance: it discloses that the catalog is synced live from the models API and matches what the app offers, and that each entry carries a source for auditing. It omits any pagination, caching, or rate-limit behavior, keeping it out of 5 territory.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Three front-loaded sentences with distinct jobs: identity/provenance, filter dimensions, then returned fields. Tight and well ordered, though the return-value inventory is slightly padded for a description whose output isn't structured.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With no output schema, the description correctly enumerates what comes back (name, provider, context window, max output, cost tier, source), which is exactly the burden it must carry. It leaves open whether the result set is paginated or bounded, a minor gap for a catalog endpoint.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

At 67% schema coverage the schema already documents q and provider, and the description mostly restates those same filter dimensions plus the type enum values. It adds no syntax, matching rules, or behavior for unknown providers beyond what the schema says, so the baseline 3 fits.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb+resource ('list_models' / 'the live Bike4Mind model catalog') and makes the domain clear enough that it can't be confused with the blog/product siblings. It stops short of explicitly contrasting itself with the nearest neighbor (list_products), which is why it isn't a 5.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explains how to narrow the list (by type, provider, or substring query) but never says when to reach for this tool versus a sibling or when not to use it. Usage is implied by the catalog framing rather than stated.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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