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Glama

get_topics

Returns a list of JSON objects containing metadata for topics in the database

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

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

  1. First observed

TDQS

B3.2/5.0
Behavior2/5

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

No annotations are provided, and the description only states the return type. It does not disclose behaviors such as pagination, potential rate limits, authentication requirements, or whether the list is exhaustive.

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?

The description is a single clear sentence that front-loads the action ('Returns') and is free of redundant information.

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?

The description is adequate for a simple retrieval tool but lacks detail about the structure of the returned metadata or any limitations. Since there is no output schema, more specificity about the JSON object fields would improve completeness.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The tool has zero parameters, so the schema is fully covered. The description adds context by specifying that the output is JSON metadata for topics, which is helpful but not parameter-specific.

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 returns a list of JSON objects containing metadata for topics, with a specific verb and resource. It is distinct from sibling tools by naming 'topics' as the resource, but it doesn't explicitly contrast alternatives.

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 like search_texts or other get_* tools. There is no context about prerequisites, filtering, or exclusions.

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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TDQS

A3.8/5.0
Disambiguation5/5

Each tool targets a distinct aspect of the library: schedules, text metadata, links, structure, content, TOC, topics, versions, and search. There is no meaningful overlap that would cause an agent to select the wrong tool.

Naming Consistency5/5

All tools follow a consistent verb_noun snake_case pattern, with 'get_' for retrieval operations and 'search_texts' for the search operation. The naming style is uniform and predictable.

Tool Count5/5

With 9 tools, the server is well-scoped for a read-only library API. Each tool earns its place, covering the core retrieval needs without overwhelming the agent with excessive options.

Completeness5/5

The set comprehensively covers the library domain: searching, retrieving text, versions, structure, links, calendars, topics, and the full table of contents. For a read-only library, there are no obvious missing operations or dead ends.

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