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read_work_item_attachment

Read-only

Retrieve the content of a work item attachment for analysis, returning images as vision-readable objects and text files as strings. Supports PNG, JPEG, GIF, WEBP, and text formats up to specified size limits.

Instructions

Fetch an attachment's content so the LLM can read or analyze it.

Supported file types: Images (returned as vision-readable image, max 5 MB): PNG, JPEG, GIF, WEBP Text (returned as a string, max 1 MB): TXT, MD, CSV, HTML, XML, YAML, JSON

Not supported (use get_work_item_attachment_download_url instead): PDF - requires a text-extraction library (not installed) DOCX/XLSX/PPTX - binary Office formats, require extraction Audio / Video - non-textual binary formats Generic binary - executables, archives, etc.

Args: project_id: UUID of the project work_item_id: UUID of the work item attachment_id: UUID of the attachment

Returns: Image object for image files, plain string for text files.

Raises: ValueError: If the file type is unsupported or the file exceeds the size limit for its category.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
project_idYes
work_item_idYes
attachment_idYes
Behavior5/5

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

Annotations indicate readOnlyHint=true and openWorldHint=true, and the description adds valuable behavioral context: returns image objects or strings depending on file type, raises ValueError for unsupported types or size limits. No contradictions with 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?

Well-structured with clear sections (purpose, supported types, unsupported types, args, returns, raises). Front-loaded with main action and file type details. No redundant sentences.

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?

Covers all necessary aspects: supported/unsupported file types, size limits, return types, error conditions. Without an output schema, the description fully explains return values. For a fetch tool, it is comprehensive.

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 has 0% description coverage, but the description compensates by stating each parameter's role ('UUID of the project/work item/attachment'). Although not deeply detailed, it clarifies what each ID refers to, which is sufficient given their intuitive names.

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 'Fetch an attachment's content so the LLM can read or analyze it' and lists supported/unsupported file types. It explicitly references the sibling tool 'get_work_item_attachment_download_url' for unsupported types, differentiating its purpose.

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?

Provides explicit when-to-use (supported file types) and when-not-to-use (unsupported types, with alternative tool named). Also includes size limits and error handling, giving clear guidance on usage boundaries.

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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