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devkanro

knowledgebased

by devkanro

search_knowledge

Retrieve raw knowledge fragment contents by searching exact tags, with graph traversal following related links.

Instructions

Search knowledge fragments by tags with graph traversal. Returns raw fragment contents.

Use this when you know the exact tags and need full, unabridged content for a specific domain. Prefer search_rag over this for answering questions (it summarizes automatically). Prefer search_semantic over this for exploring what the knowledge base covers.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
hopsNoNumber of graph hops to follow related links (default: 1)
tagsYesTags to search for
outputNo'inline' returns results in response. 'file' writes to a temp file and returns the path.inline
Behavior4/5

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

No annotations are provided, so the description carries the full burden. It discloses the key behavior of graph traversal and that it 'Returns raw fragment contents' (as opposed to summaries). However, it does not explicitly state read-only status, potential side effects, or limits on hops, though these are less critical for a search tool. The added behavioral context goes beyond the schema's structural details.

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 three sentences long, with the first sentence front-loading the core purpose, the second giving immediate usage context, and the third providing alternative tool guidance. Every sentence earns its place with no redundancy or fluff.

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 that there is no output schema, the description's statement about returning raw fragment contents is helpful, though it could be more specific about the output structure. It covers purpose, usage, alternatives, and the meaning of 'raw' relative to other tools. The schema covers parameter details, and the description sufficiently integrates with sibling tools for a complete picture.

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 coverage is 100%, so the baseline is 3. The description adds some nuance, such as 'exact tags' implying precision and 'graph traversal' relating to the hops parameter, but it does not substantially enhance understanding beyond what the schema already describes. The schema's descriptions for hops and output are already clear.

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's function: 'Search knowledge fragments by tags with graph traversal. Returns raw fragment contents.' This specifies the verb (search), resource (knowledge fragments), and method (tags + graph traversal). It also distinguishes itself from sibling tools by explicitly contrasting with search_rag (which summarizes) and search_semantic (which explores coverage).

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?

The description provides explicit usage guidance: 'Use this when you know the exact tags and need full, unabridged content for a specific domain.' It also names alternatives and when to prefer them: 'Prefer search_rag over this for answering questions' and 'Prefer search_semantic over this for exploring what the knowledge base covers.' This is exemplary differentiation.

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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