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CompareFairly

by com.comparefairly
OverviewDiscussionsAdmin

search_comparisons

Search CompareFairly's catalog of neutral, sourced comparisons for a given market. Returns matching comparisons (title, slug, url, json_url, popularity). Use this first to find the exact slug, then call get_comparison.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

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

Input Schema

NameRequiredDescriptionDefault
langNo2-letter language code (e.g. en, fr, de, es, it). Default: en.
limitNoMax results (default 20, max 50).
queryNoFree-text query, e.g. 'best electric car' or 'password manager'.
countryNo2-letter country code (e.g. US, FR, DE, GB). Default: US.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.4/5.0
Behavior4/5

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

No annotations are present, so the description carries the full burden. It discloses the return fields ('title, slug, url, json_url, popularity') and the 'neutral, sourced' nature of the results. It does not mention error handling or rate limits, but for a simple search tool, the behavior is adequately transparent and not misleading.

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?

The description is two sentences, front-loaded with the action and resource, followed by return information and a clear usage directive. Every word contributes meaning; there is no fluff or redundancy.

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 4-parameter, low-risk search tool with no output schema and no annotations, the description covers the essential context: what it does, what it returns, and how it connects to get_comparison. It does not detail edge cases (empty results, pagination), but the description is sufficiently complete for an agent to invoke the tool correctly.

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?

Schema description coverage is 100%, so the baseline applies. The description adds context by explaining the output includes a slug used with get_comparison, which makes the query's purpose clearer, but it does not elaborate on parameter usage beyond the schema definitions. The schema already provides sufficient parameter semantics.

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 the specific verb 'Search' with a clear resource ('CompareFairly's catalog of neutral, sourced comparisons') and explicitly differentiates from the sibling tool get_comparison by directing the user to 'use this first to find the exact slug'. This makes the tool's unique role immediately clear.

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

Usage Guidelines5/5

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

Provides explicit usage guidance: 'Use this first to find the exact slug, then call get_comparison.' This clearly indicates when to use the tool and how it fits with a sibling tool, fulfilling the criterion of explicit workflow context even without listing when-not-to-use scenarios.

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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