Skip to main content
Glama
SShadowS

Zendesk MCP Server

by SShadowS

analyze_ticket_documents

Uses AI to analyze document attachments (PDF, DOCX, TXT, CSV, images) from a support ticket. Customize analysis with prompts and filters to extract key insights.

Instructions

Comprehensively analyze all document attachments from a ticket (PDF, DOCX, TXT, CSV, etc.) using AI. Long documents are truncated to fit the analysis budget. Note: this may take 30-60 seconds for multiple documents.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
idYesTicket ID
max_tokensNoMaximum tokens for response (default: 8192, max: 16000)
quick_modeNoQuick mode: analyze only first 3 documents (default: false)
document_typesNoFilter specific document types to analyze
include_imagesNoAlso analyze image attachments (default: true)
analysis_promptNoCustom prompt for document analysis
Behavior3/5

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

With no annotations, the description discloses truncation and estimated time (30-60 seconds), which are useful behavioral traits. However, it lacks details on output format, error handling, or authentication needs, relying on the schema for parameter descriptions.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise, with two sentences and a note. It front-loads the core purpose and adds relevant details (truncation, time) without redundancy. Minor improvement could integrate the note more smoothly.

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

Completeness2/5

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

Given 6 parameters, no output schema, and no annotations, the description omits critical information such as the output format (e.g., summary, key points) and how analysis is returned per document. It is insufficient for an AI to fully understand the tool's behavior.

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?

All parameters have schema descriptions (100% coverage), so baseline is 3. The tool description does not add additional meaning beyond what the schema provides; parameters like 'quick_mode' and 'include_images' are already explained.

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 tool analyzes document attachments from a ticket using AI, listing supported formats. It distinguishes from sibling 'analyze_ticket_images' by focusing on documents, with optional image analysis via parameter.

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

Usage Guidelines3/5

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

The description implies usage for document analysis and mentions truncation and time delay, but does not specify when not to use or provide alternatives. It hints at limitations for long documents but lacks explicit guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/SShadowS/zendesk-mcp-server'

If you have feedback or need assistance with the MCP directory API, please join our Discord server