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NeuralVerge Business Intelligence

Capterra categories

run_capterra_categories

List Capterra software categories: name, alias, and canonical URL. Flat 5 points per successful run.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changed
    • removedInput schema / properties / max_items
      Removed value: -{
      -  "description": "Maximum number of categories to return (1-1500, default 50).",
      -  "maximum": 1500,
      -  "minimum": 1,
      -  "type": "integer"
      -}
    • removedInput schema / properties / query
      Removed value: -{
      -  "description": "Case-insensitive substring filter on the category name, e.g. 'crm'.",
      -  "type": "string"
      -}
  2. Added

TDQS

A3.7/5.0
Behavior3/5

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

With no annotations, the description carries the full burden, and it does disclose one genuinely useful behavioral fact: a flat 5-point cost per successful run. It does not say whether results are paginated, how large the category set is, or whether any auth is needed.

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 short sentences, zero waste, and the purpose is front-loaded ahead of the cost note. Excellent signal density for the space it occupies.

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 zero-param enumeration tool with no output schema and no annotations, the description covers what the tool does, what it returns, and what it costs. Only pagination/result-size behavior is unaddressed, which is minor here.

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 tool takes zero parameters, so there is nothing for the description to disambiguate; baseline is 4. The listed output fields are a bonus but relate to return values, not inputs.

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 and resource ('List Capterra software categories') and enumerates the returned fields (name, alias, canonical URL). The resource is clearly distinct from sibling run_capterra_category_products, though the description never explicitly contrasts them.

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?

Usage is implied: an unparameterized enumeration tool is naturally read as a discovery/seed call before drilling into run_capterra_category_products or run_capterra_products_search. No explicit when-to-use, prerequisites, or alternatives are given.

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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