Bro Find AI

get_tool_details

Get full details of a specific AI tool by its slug identifier (e.g. 'chatgpt', 'midjourney'). Returns name, description, URL, category, pricing, votes, and metadata.

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": {
      "slug": "<slug>"
    },
    "name": "get_tool_details"
  }
}

Input Schema

Name	Required	Description	Default
`slug`	Yes	The tool's slug identifier (lowercase, hyphenated)

Schema Changelog

Changes observed during successful MCP inspections.

First observedSep 26, 2026

TDQS

A3.6/5.0

Behavior3/5

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

With no annotations available, the description bears the transparency burden. It does disclose the return contents ('Returns name, description, URL, category, pricing, votes, and metadata'), which is helpful, but it does not mention behavior for invalid or missing slugs, authentication, rate limits, or whether the operation is read-only.

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 a single, efficient sentence that immediately states the action, identifier, examples, and return fields. There is no redundant wording or filler.

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?

There is no output schema, so the description's explicit listing of return fields fills that gap well. Combined with the fully documented slug parameterchers, the description provides enough context to invoke the tool correctly, though it omits error or edge-case behavior.

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 schema already documents the slug parameter with 100% coverage, including the format 'lowercase, hyphenated'. The description adds value with concrete examples like 'chatgpt' and 'midjourney' and clarifies that the slug identifies a specific tool, but it does not significantly extend the schema's meaning.

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?

The description states a specific verb ('Get'), resource ('full details of a specific AI tool'), and identifier ('slug identifier') with concrete examples. It clearly communicates the operation and scope, though it does not explicitly contrast it with sibling tools like search_ai_tools or list_categories.

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 implies when to use the tool: when you already have a slug and need full details for that specific tool. It does not explicitly mention alternatives or when not to use it, so guidance is adequate but left to inference.

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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