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YGao2005

Scholar Feed MCP Server

find_gaps

Identify foundational and frontier research gaps in your library by analyzing a collection or topic, surfacing canonical anchors and recent novel works you haven't saved.

Instructions

Find important work you HAVEN'T saved, for a collection or topic — a 'what am I missing?' analysis. Returns two buckets: foundational_gaps (canonical citation-graph anchors in the niche, not in your library) and frontier_gaps (recent high-novelty work in the niche, not yet saved). Provide exactly one seed: collection_name OR collection_id OR topic. The backend derives the niche, runs lineage + recent-novelty search, and subtracts your saved set. Read-only. Requires SF_API_KEY (it needs your library to subtract) and is a Pro feature — free accounts receive an upgrade prompt.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
collection_nameNoAnalyze gaps for a collection by name (resolved by the backend). Provide exactly one seed.
collection_idNoAnalyze gaps for a collection by UUID. Provide exactly one seed.
topicNoAnalyze gaps for a free-text topic/area. Provide exactly one seed.
scopeNoWhich gaps to surface: 'foundational' (canonical anchors you're missing), 'frontier' (recent novel work you haven't saved), or 'both' (default).both
limitNoMax gaps per bucket (max 50). Default 10.
Behavior5/5

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

Declares read-only nature, explains backend process (lineage + recent-novelty search, subtract saved set), and discloses API key and Pro requirement. Without annotations, this fully covers behavioral expectations.

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?

Single paragraph but well-organized: purpose first, then buckets, then constraints, then technical details. Front-loaded with key information; no wasted words.

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 critical aspects: purpose, buckets, seed requirement, API key, Pro tier, read-only nature. No output schema needed as buckets are described.

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%, baseline 3. Description adds context: 'exactly one seed' constraint for collection_id/name/ topic, and explains scope and limit values meaningfully.

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?

Description clearly states the tool finds unsaved important work ('what am I missing?') and defines two specific buckets: foundational and frontier gaps. This distinguishes it from sibling tools like search_papers and list_library.

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?

Explicitly instructs to provide exactly one seed from collection_name, collection_id, or topic. Mentions prerequisites (SF_API_KEY) and feature tier (Pro). Does not explicitly state when not to use, but constraints are 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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