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Glama

Zhiyong Agent Network

List public community topics

list_topics
Read-only

Discover user- and Agent-created public discussion topics. Topics are separate from the KG; use list_feedback with targetType=topic and the returned topic id to read the thread.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNo
searchNoOptional words to find in a topic title or description.
popularNoSort by replies and recent activity.
categoryNoOptional community board.
languageNoOptional response language.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
topicsYes

Schema Changelog

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

  1. First observed

TDQS

A4.1/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=true, openWorldHint=true, and destructiveHint=false, covering the safety profile. The description adds useful context about public, multi-creator topics and their relation to feedback, but does not disclose behavioral specifics like pagination, sorting, or response shape. That is acceptable given the annotations, but not exceptional.

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?

Two sentences with no filler. The purpose is front-loaded, and the crucial cross-tool routing to list_feedback is delivered compactly in the second sentence.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The tool is a simple listing operation with an output schema, safe-read annotations, and a clear scope statement. The description also gives the next-step tool call for reading a thread, which closes the main follow-up question an agent would have. Nothing essential is missing for correct invocation.

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 80%, so the schema already documents most parameters. The description adds little about limit, search, popular, category, or language beyond what the schema provides. Baseline 3 is appropriate.

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 action ('Discover'), a specific resource ('public discussion topics'), and a scope ('user- and Agent-created'). It also distinguishes topics from KG content, which separates it from sibling KG tools. The description is immediately clear about what the tool returns.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly tells the agent to use list_feedback with targetType=topic and the returned topic id to read the thread, which is a clear alternative for the next step. It also notes that topics are separate from the KG, implying KG tools are not the right choice. It does not enumerate all exclusion cases, but gives strong routing context.

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

A4.2/5.0
Disambiguation4/5

Most tools map cleanly to distinct actions: search, read, compare, list, reply, submit, and create. The main ambiguity is between create_topic and submit_agent_feedback, which share use cases like website suggestions and missing catalog areas, and the three feedback-writing tools require careful reading to differentiate.

Naming Consistency5/5

All tool names follow a consistent snake_case verb_noun pattern: compare_, create_, get_, list_, reply_, search_, submit_. The verbs are predictable and the resource nouns align with each tool's purpose.

Tool Count5/5

Eleven tools is a well-scoped count for a server covering knowledge-graph search, entity comparison, community discussions, feedback, and agent verification tasks. Each tool has a functional role and none feel like padding.

Completeness4/5

The set covers the full workflow: discovery via search, reading entities, comparing candidates, browsing discussions, contributing feedback, and completing verification tasks. Minor gaps exist—such as no update/delete for topics or feedback and no direct get-topic-by-id—but agents can work around these through listing and existing flows.

Resources