Skip to main content
Glama
avarant

Typesense MCP Server

describe_collection

Retrieve the schema and metadata of a specified collection to understand its structure and properties. Use this tool to manage and analyze collections in Typesense MCP Server efficiently.

Instructions

Retrieves the schema and metadata for a specific collection.

Args:
    ctx (Context): The MCP context.
    collection_name (str): The name of the collection to describe.

Returns:
    dict | str: The collection schema dictionary or an error message string.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
collection_nameYes
Behavior2/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It states this is a retrieval operation, implying read-only behavior, but doesn't mention error handling (returns 'error message string'), performance characteristics, authentication needs, or other behavioral traits beyond the basic operation.

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 appropriately sized and front-loaded with the core purpose in the first sentence. The Args and Returns sections are structured but slightly verbose for a single parameter; every sentence earns its place by clarifying input and output.

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?

Given the tool's low complexity (1 parameter, no output schema, no annotations), the description is minimally complete. It covers the basic operation and parameter semantics but lacks usage guidelines, detailed behavioral context, and output format explanation (only mentions 'schema dictionary' or 'error message').

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 0%, so the description must compensate. It adds meaning by specifying that 'collection_name' is 'the name of the collection to describe,' which clarifies the parameter's purpose beyond the schema's title 'Collection Name.' However, it doesn't provide format constraints, examples, or validation rules.

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 states the tool's purpose: 'Retrieves the schema and metadata for a specific collection.' It specifies the verb ('retrieves') and resource ('collection'), but doesn't explicitly differentiate from siblings like 'list_collections' or 'export_collection' which handle different operations on collections.

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 on when to use this tool versus alternatives. The description doesn't mention prerequisites, context for usage, or comparisons to sibling tools like 'list_collections' (which lists collections) or 'export_collection' (which exports data).

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

Install Server

Other Tools

Related Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/avarant/typesense-mcp-server'

If you have feedback or need assistance with the MCP directory API, please join our Discord server