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GoldBean

by wuzenghai616-lang
OverviewSchemaSourceRelated ServersDiscussions(4)Admin
JavaScript
Remote

baidu_word_embedding

Convert Chinese words to numerical vector embeddings for NLP tasks.

Instructions

[NLP] 词向量表示 — $0.01/call (free: 5/5 today)

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "text": "<text>"
    },
    "name": "baidu_word_embedding"
  }
}

Input Schema

NameRequiredDescriptionDefault
textYes词语

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv1.0.0

TDQS

C2.7/5.0
Behavior1/5

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

With no annotations, the description carries the full burden of disclosing behavioral traits. It only mentions pricing and free quota, but does not describe what the tool returns, any input constraints, error behavior, or other relevant details. The agent lacks essential information about the tool's behavior.

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?

The description is brief and front-loaded with the NLP tag and purpose. It contains no superfluous information, making it efficient. However, it could be slightly restructured to include more useful information without increasing length excessively.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's low complexity (one parameter, no output schema), the description should at least explain the return value format to help the agent use the embedding correctly. It fails to do so, leaving the agent uncertain about the output, which is critical for a word embedding tool.

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?

The input schema has 100% coverage with a single parameter 'text' described as '词语' (word). The description adds context that it is an NLP word embedding tool, but does not significantly expand on the schema's meaning. The baseline score of 3 is appropriate as the schema is already sufficient.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states the tool does '词向量表示' (word embedding representation), clearly indicating its purpose as an NLP tool for generating word vectors. However, it does not differentiate from sibling tools like 'baidu_embedding' or 'baidu_nlp', which may have overlapping functionality.

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

Usage Guidelines2/5

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

No guidance is provided on when to use this tool versus alternatives. The description only includes a tag '[NLP]' and pricing, but does not explain the specific use case for word embeddings compared to other NLP tasks (e.g., sentiment analysis, summary).

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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