Skip to main content
Glama

Enhanciar: AI company brain

search_wiki

Find wiki pages containing a word or phrase. Exact match, no AI tokens.

Use it to locate pages before reading them with get_page, or when you need the team's exact wording. It matches text literally, so for a plain-English question ("why did we drop Stripe?") use query, which finds pages by meaning and writes a cited answer. Returns up to limit hits as {category, name, title, snippet}; snippet is about 280 characters around the first match, fine to show as a citation preview. An empty list means no page contains it.

Input Schema

NameRequiredDescriptionDefault
limitNoMost hits to return, e.g. 10. Default 20; values below 1 count as 1 and above 50 as 50.
queryYesWord or exact phrase to find in page names and text, matched case-insensitively as a plain substring (no AND/OR, no fuzzy match). Short is best, e.g. "refund policy" or "stripe". Blank returns no hits.

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changed
    • addedInput schema / properties / limit / description
      Added value: +"Most hits to return, e.g. 10. Default 20; values below 1 count as 1 and above 50 as 50."
    • addedInput schema / properties / query / description
      Added value: +"Word or exact phrase to find in page names and text, matched case-insensitively as a plain substring (no AND/OR, no fuzzy match). Short is best, e.g. \"refund policy\" or \"stripe\". Blank returns no hits."
  2. First observed

TDQS

A4.3/5.0
Behavior4/5

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

With no annotations, the description carries the burden and largely meets it: literal/substring matching, deterministic behavior (no AI tokens), snippet size (~280 chars) suitable as a citation preview, and that an empty list means no match. It omits auth/rate-limit details and never explicitly states read-only, but for a search operation the key traits are disclosed.

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?

Purpose is front-loaded in the first sentence, and each paragraph adds a distinct piece of value (usage, return shape, empty-result meaning). Slightly paragraph-heavy for a two-parameter tool but nothing is filler.

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 an output schema exists, return values need not be explained, yet the description helpfully characterizes the snippet. Usage, match semantics, and empty-result interpretation are all covered; only minor gaps like pagination or escaping behavior remain.

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% and the schema already documents the substring/case-insensitive semantics and the limit clamping. The description's 'matches text literally' largely restates the schema rather than adding new syntax or format detail, so baseline 3 applies.

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?

States a specific verb and resource ('Find wiki pages containing a word or phrase') plus the discriminator 'Exact match, no AI tokens.' An agent can immediately distinguish it from query and get_page without opening any schema.

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?

Explicitly names both when to use it ('locate pages before reading them with get_page', 'need the team's exact wording') and when to prefer the sibling ('for a plain-English question ... use query'). The routing condition is spelled out, not inferred.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Try in Browser

Glama MCP Gateway

Add one secure layer between your agents and this server.

Resources