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Search the Asyntai documentation

search_asyntai_docs
Read-onlyIdempotent

Search Asyntai's product documentation to find how a feature works before changing anything. Returns matching pages with excerpts to clarify usage without guessing.

Instructions

Search Asyntai's own product documentation and get back the pages that match, with a short excerpt from each. Use this to find out how a feature works before changing anything, rather than guessing. This searches Asyntai's documentation, NOT the customer's own content: for that, use search_knowledge_base. Matching is by keyword, so if the results look wrong, search again using Asyntai's own feature names, for example data feed, widget pinning, access tags, AI instructions.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoHow many pages to return, 1 to 20. Default 8.
queryYesWhat to look for.

Schema Changelog

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

  1. First observedv1.0.2

TDQS

A4.3/5.0
Behavior4/5

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

Annotations already declare readOnly, idempotent, and non-destructive. The description adds context that results include excerpts and that matching is by keyword, plus a note about re-searching with feature names. This goes beyond the annotations, though it doesn't describe any side effects (none expected).

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 description is a single paragraph with five sentences, each serving a purpose: purpose, usage context, differentiation from sibling tool, and a behavioral tip. It's slightly longer than minimal but well-structured and free of 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 there is no output schema, the description adequately explains the return format (pages with excerpts). It provides examples of feature names to use as keywords and clarifies the scope (Asyntai's own docs vs customer content). Missing details like error behavior are not critical for this simple search tool.

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% for both parameters (query and limit), each with clear descriptions. The description doesn't add parameter-specific details beyond the schema, but it does mention keyword matching which relates to the query parameter. Baseline of 3 is appropriate given full schema coverage.

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 explicitly states the tool searches Asyntai's own product documentation and returns matching pages with excerpts. It clearly distinguishes from customer content, and the verb 'search' plus resource 'Asyntai documentation' is specific. It also differentiates from sibling search_knowledge_base, so no ambiguity.

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?

Provides direct guidance on when to use ('to find out how a feature works before changing anything, rather than guessing'), when not to use (customer content, pointing to search_knowledge_base), and a tip on keyword matching with examples of feature names. This is explicit and actionable.

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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