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YGao2005

Scholar Feed MCP Server

Ask Library

ask_library
Read-only

Synthesize answers from your saved research papers by asking a natural-language question. Get responses with inline citations, optionally scoped to a collection.

Instructions

Answer a question using ONLY the papers you've saved — a synthesis over your library (or one collection) with inline [arXiv-ID] citations. The inverse of find_gaps (which finds important work you're MISSING): ask_library reasons over what you HAVE. Optionally scope to one collection (collection_name OR collection_id); omit both to use your whole library. Read-only. Requires SF_API_KEY (it reads your saved set). Free accounts get 1 question/month; Pro raises this to 200/day.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
questionYesThe natural-language question to answer from your saved papers.
collection_nameNoScope the answer to one collection by name (resolved by the backend). Omit to use your whole library.
collection_idNoScope the answer to one collection by UUID. Omit to use your whole library.
limitNoHow many of your most-relevant saved papers to ground the answer on (max 20). Default 8.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
answerNoThe synthesized answer with inline [arXiv-ID] citations.
citationsNo
papersNo
okNo
messageNo
Behavior5/5

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

Annotations already indicate readOnly=true and destructive=false. Description adds 'Read-only', API key requirement, usage limits, and citation format. 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?

Description is fairly long but every sentence adds value. Front-loaded with core purpose, then usage details and limitations. Not overly verbose.

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?

With output schema present, description doesn't need return values. It covers purpose, scope, limitations, and contrast well. Could mention citation format earlier, but overall complete.

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?

Schema coverage is 100%, so description adds only marginal value (e.g., clarifying collection options). Baseline of 3 is appropriate; no significant new semantics beyond schema.

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 answers a question using only saved papers with inline citations, distinguishing it from find_gaps. The verb 'answer' and resource 'library/collection' are specific.

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 explicit when to use (synthesis over library) and contrasts with find_gaps. Mentions optional scoping and limitations (free vs Pro). Lacks explicit 'when not to use' but sibling context is clear.

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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