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u9401066

asset-aware-mcp

by u9401066

fetch_document_asset

Retrieve specific document assets (tables, figures, sections) as markdown or images. Specify asset type and ID to extract exact content with optional image resizing.

Instructions

Fetch specific content from a document with precision.

Asset Types:

  • "table": Returns table as markdown (with page number)

  • "figure": Returns image as base64 with page number for verification

  • "section": Returns section text content

  • "full_text": Returns entire document as markdown

Args: doc_id: Document identifier asset_type: One of "table", "figure", "section", "full_text" asset_id: Asset ID from manifest (e.g., "tab_1", "fig_1_1", "sec_methods") Use "full" for full_text type max_size: Maximum image dimension (longest edge) for figures. - None (default): Use default 1024px - 0: Return original size (no resize) - N: Resize to Npx longest edge (e.g., 512, 768, 2048)

Returns: For figures: ImageContent that vision AI can directly analyze For others: TextContent in markdown format

Example: # Get Table 1 from document fetch_document_asset("abc123", "table", "tab_1")

# Get figure with default resize (1024px)
fetch_document_asset("abc123", "figure", "fig_2_1")

# Get figure at specific size (512px for smaller context)
fetch_document_asset("abc123", "figure", "fig_2_1", max_size=512)

# Get original image (no resize)
fetch_document_asset("abc123", "figure", "fig_2_1", max_size=0)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
doc_idYes
asset_typeYes
asset_idNofull
max_sizeNo
max_charsNo
ctxNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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

Without annotations, the description fully explains return types (ImageContent for figures, TextContent for others) and behavior like page numbers and base64 encoding. 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.

Conciseness4/5

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

Well-organized with bullet points, sections, and examples. The purpose is front-loaded and each section is focused. Could be slightly shorter, but structure aids readability.

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 the complexity (6 params, no annotations, but has output schema), the description covers most aspects: purpose, asset types, parameters, return values, and examples. Missing some parameter docs but overall sufficient.

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 description explains four of six parameters (doc_id, asset_type, asset_id, max_size) with good detail and examples. However, it omits 'max_chars' and 'ctx' from the schema, leaving them undocumented.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

Clearly states it fetches specific content from a document, listing asset types and their return formats. However, does not explicitly distinguish from sibling tools like 'document' or 'document_asset', though the description is specific enough.

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?

Provides clear instructions on when to use each asset type and detailed parameter explanations. Lacks explicit when-not-to-use or alternative suggestions, but the context is well covered.

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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