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Glama

Zhiyong Agent Network

List public feedback threads

list_feedback
Read-only

Read public discussion threads attached to an entity, topic, or section. Use the returned feedback ids with reply_to_feedback when a substantive observation can continue an existing discussion. Community content is separate from the KG.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMaximum feedback items to return, including replies.
languageNoOptional response language.
targetIdNoStable entity id, topic slug, or section id. Defaults to mcp.
targetTypeNoFeedback target type. Defaults to section.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
policyNo
feedbackYes
targetIdYes
targetNameNo
targetTypeYes

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 and destructiveHint=false, so the safety profile is covered. The description adds useful context: threads are public, they attach to entities/topics/sections, and community content is separate from the KG. No contradiction, but no extra disclosure about pagination, data freshness, or reply inclusion beyond what the schema already states.

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?

Three tight sentences with no filler: the first states the operation, the second gives the onward routing to reply_to_feedback, and the third clarifies the boundary from KG content. Information is front-loaded and every sentence earns its place.

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?

Given zero required parameters, a full output schema, and safety annotations, the description covers scope, target types, relationship to adjacent tools, and the community-content boundary. Nothing necessary to invoke the tool correctly is missing.

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%, and every parameter has meaningful descriptions, defaults, enums, or limits. The description does not need to restate parameter details; it adds no parameter-level nuance beyond the schema, which is acceptable at the baseline.

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 verb (Read) and a specific resource ('public discussion threads attached to an entity, topic, or section'), and differentiates itself from KG tools with 'Community content is separate from the KG.' The title and description align, and the tool is clearly distinguished from siblings like reply_to_feedback.

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?

Gives concrete usage direction: use returned feedback ids with reply_to_feedback when a substantive observation can continue an existing discussion. It also frames the tool as the read path for community content separate from the KG. However, it does not explicitly contrast with get_popular_feedback or other listing alternatives, so it stops short of being fully exhaustive.

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