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retospect

acatome-quest-mcp

by retospect

submit_file

Attach a user-supplied PDF to a paper request. Supports URL or base64 input and can attach to an existing request or create a new one.

Instructions

Attach a user-supplied PDF to a paper request.

Use this when a user drops a PDF (e.g. a Discord attachment) for a paper that Quest could not fetch automatically, or to pre-load a PDF you already have on disk.

Args: url: A direct HTTP(S) link to the PDF. Must resolve to a fresh file (follow-redirects is enabled, so short-lived Discord CDN URLs work). Mutually exclusive with content_base64. content_base64: Base64-encoded PDF bytes, for agents that already have the file in memory. Prefer url when available so the provenance URL is recorded on the request. filename: Optional filename hint, used when naming the file written to the inbox. Falls back to the request's author/year. request_id: Attach to an existing request (preferred). Reopens failed, extract_failed, or needs_user requests. Refuses to overwrite already-closed or cancelled requests. ref: Create a new request from this reference (same shape as :func:submit) and attach the PDF. Use this when the paper isn't already being tracked. created_by: Agent or user id, used when ref is given.

Returns: The full request record, flipped to ingesting. The background runner reconciles with acatome-store once acatome-extract has done its work.

Exactly one of url / content_base64 is required, and exactly one of request_id / ref.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlNo
content_base64No
filenameNo
request_idNo
refNo
created_byNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior5/5

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

No annotations are provided, so the description fully bears the burden. It discloses behavioral traits such as re-opening specific states ('failed', 'extract_failed', 'needs_user'), refusing to overwrite closed/cancelled requests, and the return value being the request record flipped to 'ingesting'.

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?

The description is well-structured with a concise intro, 'Args' section, 'Returns' section, and a final constraint note. Every sentence adds value, with no redundancy or fluff.

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?

Given the tool has 6 parameters, no annotations, and an output schema, the description is complete. It explains parameter usage, return value, and even the background process. No critical information is missing for an AI to use the tool correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, but the description provides detailed semantics for each parameter, including mutual exclusivity (url vs content_base64, request_id vs ref), fallback behavior for filename, and usage of created_by. This fully compensates for the lack of schema descriptions.

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's purpose: 'Attach a user-supplied PDF to a paper request.' It provides specific use cases (user-dropped PDF or pre-loaded file) and distinguishes from siblings by focusing on PDF attachment, not request creation or status.

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 explicitly states when to use the tool (e.g., 'when a user drops a PDF...') and explains the two parameter groups (url vs content_base64, request_id vs ref). It lacks explicit when-not-to-use guidance but provides clear 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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