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YGao2005

Scholar Feed MCP Server

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Embed text into a vector for HyDE retrieval: write a hypothetical answer, embed it as a document, and find matching research papers. Also embed user queries directly.

Instructions

Embed a text string into a 768-dim Gemini Flash vector. Use for HyDE-style retrieval: (1) write a hypothetical short paper that would perfectly answer the user's query, (2) embed it with task_type='RETRIEVAL_DOCUMENT' (default — matches the corpus embedding side), (3) pass the resulting embedding back through search-style tools to find real papers nearest to the hypothetical. task_type='RETRIEVAL_QUERY' matches the query side and is useful for direct user-query embedding without HyDE. Pro-only — requires an SF_API_KEY on a Pro account; anonymous and free callers get a 403 pro_required. Cost: ~$0.0001/call; rate-limited at 30/minute per API key.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
textYesText to embed (1-8000 chars). For HyDE flows this is your hypothetical answer/abstract.
task_typeNoRETRIEVAL_DOCUMENT (default) matches paper-side embeddings — use for HyDE. RETRIEVAL_QUERY matches query-side semantic search.RETRIEVAL_DOCUMENT

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
embeddingNoThe embedding vector (768-dim Gemini Flash).
modelNo
task_typeNo
dimensionsNo
dimsNo
Behavior5/5

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

Adds valuable behavioral context beyond annotations: cost ($0.0001/call), rate limits (30/min), auth constraints (403 for free), model (Gemini Flash), and vector dimension. No contradiction with annotations (readOnlyHint=true, destructiveHint=false).

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?

Single dense paragraph with no fluff, front-loaded with core action, and efficiently uses every sentence to convey purpose, use cases, and constraints.

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?

For a 2-param tool with output schema, the description covers purpose, usage, auth, cost, rate limits, and even a workflow example. No gaps identified.

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% with good descriptions, but the description adds context on HyDE workflow and default task_type purpose, enhancing understanding beyond the 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 'Embed a text string into a 768-dim Gemini Flash vector' and explains the HyDE retrieval use case, distinguishing it from sibling tools which are different actions (search, collections, etc.).

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 explains when to use each task_type (RETRIEVAL_DOCUMENT for HyDE, RETRIEVAL_QUERY for direct queries) and mentions authentication requirements (Pro-only, SF_API_KEY). Could be more explicit about alternatives, but sibling tools are clearly different.

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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