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wisdom_search_fragments

Find knowledge fragments by text query and filter them by tags, state, author, or project to retrieve relevant wisdom network content.

Instructions

Search fragments with text query and filters

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
tagsNoFilter by tag UUIDs
limitNoMaximum results (default: 20)
queryNoText search query
stateNoFilter by fragment state
authorNoFilter by author agent UUID
projectNoFilter by project UUID

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observedv0.1.0

TDQS

C2.9/5.0
Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure, but it only restates the operation. It says nothing about read-only behavior, result ordering or pagination, what happens with an empty query (all parameters are optional), or response characteristics, with no output schema to fill the gap.

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?

The six-word sentence is front-loaded with the verb and resource and contains zero filler; every word earns its place. It is appropriately compact, though the brevity shifts informational burden onto other dimensions.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a 6-optional-parameter search tool with no annotations and no output schema, a single sentence is insufficient. The agent cannot tell how this differs from wisdom_list_fragments, whether filters combine additively, how defaulting works, or what the search returns, leaving meaningful gaps for a tool of this complexity.

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%, with each of the 6 parameters already documented (tags, limit, query, state, author, project), so the schema does the heavy lifting. The phrase 'text query and filters' maps to those parameters but adds no detail beyond what the schema already provides, so the baseline 3 applies.

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 ('Search'), a resource ('fragments'), and the mechanism (text query and filters), so an agent can immediately grasp the core purpose. It implicitly distinguishes from siblings like wisdom_get_fragment (single fetch) and wisdom_list_fragments (plain listing), though it never names them or the distinction explicitly.

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

Usage Guidelines2/5

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

No guidance is given about when to choose this tool over alternatives. With siblings like wisdom_list_fragments, wisdom_get_fragment, and wisdom_find_contradictions, the agent gets no conditions, exclusions, or alternative-tool routing, leaving selection 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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