Skip to main content
Glama

Context Awesome

by io.github.bh-rat

find_awesome_section

Discovers sections/categories across awesome lists matching a search query and returns matching sections from awesome lists.

You MUST call this function before 'get_awesome_items' to discover available sections UNLESS the user explicitly provides a githubRepo or listId.

Selection Process:

  1. Analyze the query to understand what type of resources the user is looking for

  2. Return the most relevant matches based on:

    • Name similarity to the query and the awesome lists section

    • Category/section relevance of the awesome lists

    • Number of items in the section

    • Confidence score

Response Format:

  • Returns matching sections of the awesome lists with metadata

  • Includes repository information, item counts, and confidence score

  • Use the githubRepo or listId with relevant sections from results for get_awesome_items

For ambiguous queries, multiple relevant sections will be returned for the user to choose from.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMaximum sections to return
queryYesSearch terms for finding sections across awesome lists
confidenceNoMinimum confidence score (0-1)

TDQS

A4.3/5.0
Behavior4/5

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

No annotations are provided, so the description carries full burden. It discloses the tool's behavior: searches across awesome lists, returns matching sections with metadata, and for ambiguous queries returns multiple options. It does not mention destructive actions or auth, but that is acceptable for a search tool.

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 well-structured with clear sections (purpose, usage rule, selection process, response format, ambiguous queries). It is appropriately sized, with no redundant sentences, though it could be slightly more concise.

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 the tool's complexity (search/discovery) and lack of output schema, the description adequately explains what is returned (matching sections with metadata) and the selection criteria. It also connects to the sibling tool, covering key contextual needs.

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%, so the schema already documents all three parameters. The description adds limited additional meaning beyond the schema, such as mentioning confidence in the selection process, but does not elaborate on format or constraints beyond what is in the schema.

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 it 'Discovers sections/categories across awesome lists matching a search query' and distinguishes from sibling tool 'get_awesome_items' by positioning it as a prerequisite. The verb 'Discovers' and resource 'sections/categories' are specific and unambiguous.

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 explicit guidance: 'You MUST call this function before get_awesome_items to discover available sections UNLESS the user explicitly provides a githubRepo or listId.' Also outlines the selection process and when multiple results are returned, giving clear context for use.

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

All Context Awesome toolsInspect
Try in Browser
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

A4.4/5.0
Disambiguation5/5

The two tools have clearly distinct purposes: one discovers sections/categories, the other retrieves items. No overlap in functionality.

Naming Consistency5/5

Both tools follow a consistent verb_noun pattern (find_awesome_section, get_awesome_items), making the set predictable.

Tool Count4/5

Two tools is minimal but appropriate for a focused server that covers discovery and retrieval. Could benefit from a listing tool, but not excessive.

Completeness4/5

Covers the core workflow: find section then get items. Missing a direct list-all-sections function, but the search-based discovery is sufficient for most cases.

Resources