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models-mcp

models-mcp

by QAInsights
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
Hybrid

Get model

get_model

Retrieve full metadata for a single AI model—pricing, context window, capabilities—using provider/model ID or a unique bare model ID.

Instructions

Fetch full metadata for one model. Accepts 'provider/model' (preferred, e.g. 'anthropic/claude-sonnet-4-5') or a bare model id if unambiguous across providers.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "id": "<id>"
    },
    "name": "get_model"
  }
}

Input Schema

NameRequiredDescriptionDefault
idYesModel id, e.g. 'anthropic/claude-sonnet-4-5' or 'gpt-5.2'.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.2.0

TDQS

A4.2/5.0
Behavior3/5

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

No annotations are provided, so the description must carry behavioral context. It usefully discloses accepted id formats and the ambiguity caveat for bare model ids, but it does not describe error behavior, return structure, or any prerequisites such as authentication.

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

Conciseness5/5

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

Two sentences with no wasted words. The core action is front-loaded, and the parameter guidance is compact and directly useful.

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?

For a simple one-parameter fetch tool, the description is largely complete: it states the operation, the parameter format, and the ambiguity rule. Since there is no output schema, a slightly richer statement about what 'full metadata' contains or what happens on ambiguous ids would make it fully complete.

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

Parameters4/5

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

The schema already fully documents the 'id' parameter, so the baseline is 3. The description adds meaningful value by marking 'provider/model' as preferred and explaining when a bare id may be accepted, which helps the agent construct valid inputs.

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

Purpose5/5

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

The description uses a specific verb and resource: 'Fetch full metadata for one model.' It clearly distinguishes from siblings like list_providers, find_models, and compare_models by emphasizing a single model lookup.

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

Usage Guidelines4/5

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

The description provides clear context for when to use this tool: when detailed metadata for one specific model is needed. It does not explicitly name alternatives or state when not to use it, but the 'one model' scope and id-format guidance imply the correct selection.

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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