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rlowndes9

Zendesk MCP Server

by rlowndes9

get_ticket_comments

Read-onlyIdempotent

Fetch the comment thread for a Zendesk ticket. Returns paginated comment skeletons with body, author, and timestamps. Enable verbose for attachments and channel info.

Instructions

Return the comment thread for one ticket as paginated skeletons (id, type, author_id, body, html_body, public, created_at). Scope-gated (config_plus_audits or full). Pass verbose: true for via-channel info and attachments. For "what changed on this ticket and why?" use get_ticket_audits instead, comments only carry the textual conversation, audits carry rule attribution.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
idYesTicket ID
limitNoMax items to return. Default 100. The full corpus is fetched and cached server-side; this only limits what the response carries.
cursorNoOpaque pagination token from a previous response. Slices the next page from the cached corpus. Stale cursors (older than the cache TTL) auto-reset to offset 0 and set cursor_invalidated: true.
fieldsNoWhitelist of field names. Overrides the default projection. Use to opt into extra fields (e.g. ["id","title","active","position","category_id","updated_at"]) without going fully verbose.
filterNoStructured filter applied to the cached corpus before slicing. Supported keys: active (bool), category_id (number/string), title_contains (string, case-insensitive), updated_since (ISO timestamp). Unsupported keys are ignored with a note in the response.
refreshNoBypass cache and re-fetch from Zendesk
verboseNoReturn full comment objects instead of the thin projection
instanceNoOverride the sticky instance for this call
Behavior5/5

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

Annotations declare readOnlyHint, openWorldHint, and idempotentHint, which are consistent. The description adds behavioral details: caching of full corpus, cursor auto-reset on staleness, filter behavior for unsupported keys, and scope-gating. No contradictions.

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, with no wasted sentences. It front-loads the core purpose and then adds key details in a structured manner.

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 8 parameters and no output schema, the description covers return format, pagination, caching, filter, and alternative tool. It provides enough context for an AI agent to use the tool correctly.

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

Parameters5/5

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

Despite 100% schema description coverage, the tool description adds significant value: explains 'verbose' effect, gives example for 'fields', lists supported filter keys, and describes cursor behavior. This goes beyond the schema 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?

The description specifically states 'Return the comment thread for one ticket as paginated skeletons' and lists the fields returned. It also distinguishes from the sibling tool get_ticket_audits, making the purpose clear and specific.

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

Usage Guidelines5/5

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

The description provides explicit usage guidance: mentions passing 'verbose: true' for additional data, and directs users to get_ticket_audits for change attribution. It also notes the scope-gating requirement.

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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