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list_document_comments

Read-only

Retrieve comments from a Polarion document. Supports pagination and reconstructs threads via parent and child comment IDs.

Instructions

List comments attached to a Polarion document.

Comments come back as a flat page; reconstruct threads on the client side via parent_comment_id (set on replies) and child_comment_ids (direct replies). Top-level comments have parent_comment_id of None.

text is returned verbatim, with text_format indicating 'text/html' or 'text/plain'. HTML is NOT sanitized so the body round-trips losslessly; treat it as untrusted input if rendering.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
project_idYesPolarion project ID.
space_idYesSpace ID that contains the document (use '_default' for the default space).
document_nameYesDocument name within the space.
page_sizeNoNumber of comments per page (1-100, default 100).
page_numberNoPage number to retrieve (1-based, default 1).

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
itemsYes
total_countYes
pageYes
page_sizeYes
has_moreNoTrue if more pages follow.
Behavior5/5

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

While the readOnlyHint annotation already indicates read-only behavior, the description goes beyond by warning that HTML is not sanitized (security context) and explaining how threads are reconstructed (parent_comment_id, child_comment_ids). This adds valuable behavioral details beyond annotations.

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 concise (a single, well-structured paragraph). It front-loads the purpose in the first sentence and then efficiently adds structural and security-related information. Every sentence contributes value.

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 the presence of an output schema (reducing the need to describe return values), the description covers the essential behavioral aspects: flat page structure, thread reconstruction, HTML handling. Combined with well-documented input schema and readOnly annotation, the description is complete for an agent to use correctly.

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?

The input schema covers 100% of parameters with descriptions. The tool description adds no additional meaning to the parameters themselves, so the baseline score of 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?

The description clearly states the action ('List comments attached to a Polarion document') and the resource (comments on a document). It distinguishes from sibling tools like 'create_document_comments' and 'update_document_comment' by focusing on listing, and adds structural details about comments (flat page, thread reconstruction) further clarifying the output.

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 implies the tool should be used to retrieve comments attached to a document, but does not explicitly state when not to use it or suggest alternatives. However, the clear purpose and sibling tool names provide enough context for an agent to decide when this tool is appropriate.

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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