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Zendesk MCP Server

by SShadowS
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JavaScript
Hybrid

analyze_ticket_images

Analyze images attached to Zendesk tickets using AI vision. Includes inline images and file attachments, with options to scope analysis to a specific comment or selected images.

Instructions

Download and analyze images from a ticket using AI vision with comprehensive analysis. Includes both file attachments and inline images embedded in comment bodies. Optionally scope to a single comment/post via comment_id, or to specific images via attachment_ids (discover ids with get_ticket_attachments).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
idYesTicket ID
comment_idNoOnly analyze images from this comment/post. Get IDs via get_ticket_comments or get_ticket_attachments.
max_tokensNoMaximum tokens for response (default: 8192, max: 16000)
attachment_idsNoOnly analyze these specific image ids. File attachment ids are numbers; inline image ids look like 'inline_<commentId>_<index>'. Discover ids via get_ticket_attachments.
include_inlineNoInclude inline images from comment HTML bodies (default: true)
analysis_promptNoCustom analysis prompt (default: general image description)

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changedv1.6.0
    • removedInput schema / properties / attachment_ids / items / anyOf
      Removed value: -[
-  {
-    "type": "number"
-  },
-  {
-    "type": "string"
-  }
-]
    • addedInput schema / properties / attachment_ids / items / type
      Added value: +[
+  "number",
+  "string"
+]
  2. Changed3 schema fields changedv1.4.1
    • addedInput schema / properties / attachment_ids
      Added value: +{
+  "description": "Only analyze these specific image ids. File attachment ids are numbers; inline image ids look like 'inline_<commentId>_<index>'. Discover ids via get_ticket_attachments.",
+  "items": {
+    "anyOf": [
+      {
+        "type": "number"
+      },
+      {
+        "type": "string"
+      }
+    ]
+  },
+  "type": "array"
+}
    • addedInput schema / properties / comment_id
      Added value: +{
+  "description": "Only analyze images from this comment/post. Get IDs via get_ticket_comments or get_ticket_attachments.",
+  "type": "number"
+}
    • changedInput schema / properties / max_tokens / description
      Previous value: -"Maximum tokens for response (default: 4096, max: 4096)"New value: +"Maximum tokens for response (default: 8192, max: 16000)"
  3. First observedv1.3.0

TDQS

A4.2/5.0
Behavior3/5

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

No annotations are provided, so the description must carry the full burden and it does disclose the core behavior (download, AI vision analysis) and the image sources included. It omits other behavioral context an agent might need, such as response shape, token/latency cost, or any read-only/no-side-effect statement, making this adequate but not rich.

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 dense sentences front-load the action first and then progressively refine scope; no filler and no repetition of schema details. Each sentence contributes either the core action or scoping guidance.

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

Completeness4/5

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

Given six parameters and no output schema, the description covers the main decision points: what gets analyzed, how to limit scope, and where to find attachment ids. It does not describe the return format or cost/performance implications, which would make it fully complete for an unannotated AI-vision tool.

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?

Schema description coverage is 100%, so the baseline is 3; the description adds real value by explaining the difference between file attachments and inline images, how comment_id scopes the analysis, and pointing to get_ticket_attachments for id discovery. It doesn't add semantics for analysis_prompt, max_tokens, or include_inline beyond the schema.

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 opens with a specific action and resource—'Download and analyze images from a ticket using AI vision'—and immediately defines scope by including both file attachments and inline images. This cleanly separates it from the sibling analyze_ticket_documents by source modality, even without naming that tool.

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?

It gives clear usage context: use the default to analyze all images, or narrow via comment_id or attachment_ids, with an explicit pointer to get_ticket_attachments for discovery. It does not explicitly state when not to use it versus sibling tools like analyze_ticket_documents, so it stops short of a 5.

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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