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search_wiki

Find pages in your MindBase wiki by querying titles, one-liners, and slugs, returning a ranked list with snippets to locate relevant knowledge.

Instructions

Full-text search across the user's MindBase wiki — matches page titles, one-liners, and slugs. Returns ranked list with snippets.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMax results to return (default 10, max 50)
queryYesSearch query (1+ chars)
Install Server

TDQS

A3.5/5.0
Behavior4/5

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

With no annotations, the description carries the full burden. It discloses what content is matched (titles, one-liners, slugs), the full-text nature, and the return format (ranked list, snippets). It does not discuss rate limits or auth, but for a read-only search tool the main behavioral attributes are clearly articulated.

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?

Two succinct, information-dense sentences. The scope and output are presented immediately with no filler, making it easy for an agent to scan and understand the core behavior quickly.

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

Completeness3/5

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

For a simple two-parameter search tool with complete schema documentation, the description is largely adequate. However, the lack of any filtering guidance or mention of what it searches (project, whole wiki, etc.) and the absence of sibling differentiation mean that in the large tool collection, an agent may not be able to confidently pick this tool over other search tools.

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 input schema already fully documents both 'query' and 'limit' with types, constraints, and defaults. The description adds minimal parameter-specific meaning; it focuses on behavior rather than adding semantic value to the parameters themselves.

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 clearly identifies a specific verb ('search'), a resource ('user's MindBase wiki'), and the matching target fields ('page titles, one-liners, and slugs'). It communicates that this is a full-text search tool, which helps separate it from semantic_search and other smarter search siblings, though it does not explicitly name any sibling.

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 provided about when to use this tool instead of the many nearby search-related alternatives like 'search_in_project', 'search_all_projects', 'semantic_search', or 'ask_question'. The agent must infer usage from the name and broad resource scope.

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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