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RTFD (Read The F*****g Docs)

by aserper

search_gcp_services

Find Google Cloud Platform services and documentation by searching local mappings, official websites, and API repositories to identify relevant GCP tools for specific use cases.

Instructions

        Search for GCP (Google Cloud Platform) services and documentation.

        USE THIS WHEN: You need to find Google Cloud services, APIs, or documentation for a specific GCP topic.

        BEST FOR: Discovering which GCP services exist for a use case or finding service documentation.
        Returns multiple matching services with names, descriptions, API endpoints, and docs URLs.

        Searches:
        1. Local service mapping (exact and partial matches)
        2. cloud.google.com website (fallback for specific queries)
        3. googleapis GitHub repository (API definitions)

        After finding a service, use:
        - fetch_gcp_service_docs() to get full documentation content
        - The docs_url with WebFetch for external documentation

        Note: GitHub API search (fallback) is limited to 60 requests/hour without GITHUB_TOKEN.

        Args:
            query: Service name or keywords (e.g., "storage", "vertex ai", "gke audit", "bigquery")
            limit: Maximum number of results (default 5)

        Returns:
            JSON with list of matching services including name, description, API endpoint, docs URL

        Example: search_gcp_services("vertex ai") → Finds Vertex AI service with docs links
        

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYes
limitNo
Behavior4/5

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

With no annotations provided, the description carries full burden and does well. It discloses the three search sources (local mapping, cloud.google.com, GitHub), mentions the GitHub API rate limit constraint (60 requests/hour without token), and describes the multi-step search process. It doesn't mention error handling or authentication requirements, but covers key behavioral aspects.

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?

Well-structured with clear sections (purpose, usage guidelines, search sources, related tools, parameters, returns, example). Every sentence adds value, though it's slightly verbose with the three search sources enumerated. The information is front-loaded with purpose and usage guidelines first.

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 search tool with 2 parameters, no annotations, and no output schema, the description provides comprehensive context. It covers purpose, usage, behavioral details, parameters, return format, and examples. The only minor gap is not explicitly describing the JSON structure of returned results, though it mentions what fields are included.

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?

With 0% schema description coverage, the description must compensate and does so effectively. It explains both parameters: 'query' with examples ('storage', 'vertex ai', 'gke audit', 'bigquery') and 'limit' with its default value. The description adds meaning beyond the bare schema by showing query format examples and explaining the limit's purpose.

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 clearly states the tool searches for GCP services and documentation with specific resources mentioned (services, APIs, documentation). It distinguishes from siblings like fetch_gcp_service_docs by focusing on discovery/search rather than fetching full documentation content. The verb 'search' is specific and the scope is well-defined.

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?

Explicit 'USE THIS WHEN' and 'BEST FOR' sections provide clear guidance on when to use this tool. It explicitly mentions alternatives like fetch_gcp_service_docs for full documentation and WebFetch for external docs. The description distinguishes this search tool from documentation-fetching siblings.

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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