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YGao2005

Scholar Feed MCP Server

embed_text

Embed text into a 768-dim vector for HyDE-style retrieval or direct query search. Choose RETRIEVAL_DOCUMENT for hypothetical papers or RETRIEVAL_QUERY for user queries.

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
Behavior5/5

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

Discloses Pro-only requirement, 403 error for non-Pro, cost (~$0.0001/call), and rate limit (30/min per key). Also specifies model (Gemini Flash) and dimensions (768). No annotations provided, so description carries full burden and excels.

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?

Efficiently structured: action, use cases, auth/cost. Every sentence adds value. No fluff.

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?

Covers use cases, parameters, auth, cost, rate limits. Lacks explicit output format (e.g., returns embedding object), but usage context with search tools implies it. Minor gap.

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?

Adds significant meaning beyond schema: explains 'text' as hypothetical answer for HyDE, clarifies task_type enum differences. Schema coverage is 100%, so baseline is 3; description elevates to 5.

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 'Embed a text string into a 768-dim Gemini Flash vector' and specifies two distinct use cases: HyDE-style retrieval and direct embedding. It uniquely identifies the tool's purpose among 24 sibling tools.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly describes when to use each task_type (RETRIEVAL_DOCUMENT for HyDE, RETRIEVAL_QUERY for direct queries) and how to integrate with search tools. Provides complete workflow instructions.

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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