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Search expert knowledge modules using natural language queries. Returns ranked results with titles, descriptions, and categories for technical guidance, best practices, or domain expertise.

Instructions

Search 500,000+ expert knowledge modules by natural language query. Returns ranked results with titles, descriptions, and categories. Use when the user needs technical guidance, best practices, or domain expertise. Behavior: performs hybrid search (full-text + semantic) across the knowledge base, ranks by relevance, returns top N matches. Example queries: "kubernetes horizontal pod autoscaler", "react hooks best practices", "HIPAA compliance checklist".

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYesNatural-language search query describing the knowledge needed. Be specific for better results. Example: "how to set up PostgreSQL replication"
limitNoMaximum number of modules to return. Default: 10, max: 50. Use lower values (3-5) for focused results, higher (20-50) for broad exploration.
Behavior3/5

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

No annotations are provided, so the description carries full burden. It states 'Behavior: performs hybrid search (full-text + semantic) across the knowledge base, ranks by relevance, returns top N matches.' This adds useful detail beyond the schema but does not disclose auth needs, rate limits, or potential limitations.

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?

Description is compact (three sentences plus examples), front-loaded with purpose, then usage, then behavior. No extraneous words. Examples are placed at the end, which is acceptable. Minor improvement could be to separate examples more clearly.

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?

Given low complexity (2 params, no output schema, no annotations), the description is complete: it covers purpose, usage, behavioral details, and examples. No obvious gaps for a search tool. Slightly more detail on pagination or result format would improve, but not necessary.

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 baseline is 3. Description adds marginal value with example queries and advice to 'Be specific' for query, and default/max values for limit. These are helpful but not essential beyond the schema definitions.

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?

Description clearly states 'Search 500,000+ expert knowledge modules by natural language query' and lists return results. Purpose is specific and actionable. However, it does not differentiate from sibling tool 'research_search' which may have overlapping functionality.

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

Usage Guidelines4/5

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

Explicit guidance: 'Use when the user needs technical guidance, best practices, or domain expertise.' This provides clear context for when to invoke the tool. No alternative tools or exclusion criteria are mentioned, but the guidance is sufficient for a search tool.

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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