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Axiomatic-AI

axiomatic-mcp

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by Axiomatic-AI

AxKnowledgeBase_get_private_paper_asset

Fetch a specific figure or table from a private paper asset using its document ID, kind, and sequence position. Figures return as viewable images, tables as markdown.

Instructions

Download one figure or table from a paper in the organization's private knowledge graph, addressed by its position in the document (seq, from search_private_paper_assets). The private counterpart of get_paper_asset: a figure comes back as an image the model can view directly, a table as markdown. Figures have no size cap, so a large scientific figure can be a large response.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
seqYesThe asset's position in the document, from search_private_paper_assets
kindYesWhich kind of asset to fetch
doc_idYesThe paper's id

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv0.1.21

TDQS

A4.4/5.0
Behavior4/5

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

With no annotations provided, the description carries the full burden of disclosing behavior. It explains the return format (figure as image, table as markdown), that figures are directly viewable, and warns about the lack of size cap and potentially large responses. This is strong behavioral disclosure, though it does not cover failure modes or auth requirements.

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 sentences, all substantive and front-loaded. Each sentence earns its place: what it does, how it relates to a sibling, and an important size warning. No filler.

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?

For a simple retrieval tool with three fully described parameters and no output schema, the description covers the essential context: what is fetched, where the identifier comes from, and what the response looks like. It is complete enough for correct invocation, with only minor gaps around error behavior.

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 coverage is 100%, so the baseline is 3, and the description adds meaningful semantics beyond the schema: seq is specified as coming from search_private_paper_assets, and kind is tied to the output type (image vs markdown). This enriches parameter understanding without redundancy.

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 uses a specific verb ('Download') and names the exact resource ('one figure or table from a paper in the organization's private knowledge graph') and addressing mechanism (position via seq). It also explicitly identifies itself as the 'private counterpart of get_paper_asset,' making sibling differentiation clear.

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?

The description clearly frames when to use this tool: to fetch a private paper asset identified by seq from search_private_paper_assets, and contrasts it with the public get_paper_asset. It does not explicitly state exclusions or alternate conditions, but the private/public distinction supplies enough context.

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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