Generate 768-dimensional dense vector embeddings from text to enable RAG and semantic search. Accepts single strings or batches, converting them into vector representations via BAAI BGE-Base.
Generates 768-dimensional dense vector embeddings for RAG & semantic search via BAAI BGE-Base
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes |
Changes observed during successful MCP inspections.
v1.2.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It only mentions the model and vector dimension, but does not describe output format, input constraints, or side effects. This is insufficient for a generation tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, focused sentence that quickly conveys the core function and model. No filler or redundant information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the lack of annotations and output schema, the description should explain the return format and batch behavior, but it does not. It also omits differentiation from embed_multilingual, making it incomplete for tool selection.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, and the description says nothing about the 'text' parameter or how to use it. The description fails to compensate for the low schema coverage, leaving parameter semantics entirely unexplained.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool generates 768-dimensional dense vector embeddings and identifies the model (BAAI BGE-Base). However, it doesn't explicitly distinguish this from the sibling tool embed_multilingual, so it lacks explicit sibling differentiation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description mentions usage for RAG and semantic search, providing clear use context. But it does not state exclusions or compare with alternatives like embed_multilingual, so usage guidance is implied rather than explicit.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.