Generate 1024-dimensional dense vector embeddings for multilingual and long text using BAAI BGE-Large. Accepts single text or array of texts for semantic search and NLP tasks.
Generates 1024-dimensional dense vector embeddings for multilingual & long text via BAAI BGE-Large
| 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?
Without annotations, the description provides some behavioral context (dimensionality, model, multilingual/long support) but lacks details on output format, batch handling, or length limits.
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?
Single sentence, front-loaded, no unnecessary words.
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?
For a one-parameter tool with no output schema, the description is adequate but missing details like return structure and potential text length limits.
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 coverage is 0%, and the description adds some meaning by indicating the text should be multilingual/long, but it does not explicitly describe the parameter's behavior or accepted formats beyond what the schema already shows.
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 it generates 1024-dimensional dense vector embeddings, and specifies multilingual/long text and the BAAI BGE-Large model. This distinguishes it from the sibling embed_text tool.
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 implies the tool is for multilingual and long text but does not explicitly state when to prefer it over alternatives like embed_text, nor does it mention any exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.