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Fetch a model

fetch
Read-onlyIdempotent

The full content of one of FridgeBase's fridges (an id returned by search): our data, its filter values, its official data and its address.

Input Schema

NameRequiredDescriptionDefault
idYes
marketNoThe market: us = the United States (Amazon.com, in English), fr = France (Amazon.fr, in French). Each market has its own catalog.us

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • addedInput schema / properties / market
      Added value: +{
      +  "default": "us",
      +  "description": "The market: us = the United States (Amazon.com, in English), fr = France (Amazon.fr, in French). Each market has its own catalog.",
      +  "enum": [
      +    "us",
      +    "fr"
      +  ],
      +  "type": "string"
      +}
  2. First observed

TDQS

B3.4/5.0
Behavior3/5

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

Annotations already declare readOnly, idempotent, non-destructive and closed-world, so the safety profile is covered. The description adds the useful detail that the response contains filter values, official data and an address, but labels like 'our data' and 'official data' are undefined, and the 'fridges' terminology risks confusing the agent about the resource type.

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?

A single front-loaded sentence that identifies the resource and then lists contents; nothing is redundant. The parenthetical insertion and the comma-separated tail make it slightly harder to parse than necessary.

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

Completeness3/5

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

With no output schema, the description must carry the return-value burden, and it does so only at a high level with vague labels ('our data', 'official data', 'its address'). For a simple single-record read with full safety annotations this is adequate, but an agent cannot predict the actual shape or field names of the response.

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?

The description adds real meaning for the required id parameter by stating its provenance (an id returned by search). At 50% schema coverage, the remaining market parameter is fully documented in the schema itself (enum, default, per-market catalog explanation), so the description does not need to repeat it, but it also offers nothing about how market affects the returned content.

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 clear verb and resource: retrieves the full content of a single model identified by an id, enumerating the four content blocks returned (data, filter values, official data, address). It does not, however, distinguish itself from the sibling get_model, and the recurring use of 'fridges' for what the title calls a 'model' muddies what is actually being fetched.

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 parenthetical '(an id returned by search)' implies the search-then-fetch workflow, which is genuinely useful context. But there is no explicit statement of when to use this over get_model or search_models, and no exclusions or prerequisites beyond that hint.

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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