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ExpertVagabond

watsonx MCP Server

watsonx_embeddings

Convert text inputs into vector embeddings using IBM watsonx.ai models for semantic search and similarity analysis.

Instructions

Generate text embeddings using watsonx.ai embedding models

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
textsYesArray of texts to embed
model_idNoEmbedding model IDibm/slate-125m-english-rtrvr-v2
Behavior2/5

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

With no annotations, the description would need to disclose behavioral traits such as output format or side effects, but it only states the core action. No additional context is provided.

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?

The description is a single concise sentence with no fluff, making it easy to parse and front-loaded.

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

Completeness3/5

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

The tool is simple with two parameters and no output schema, but the description doesn't explain the return format or provide usage context, leaving some ambiguity for an agent.

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?

Schema coverage is 100% with both texts and model_id having descriptions, so the baseline is 3. The description adds no parameter-specific meaning 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 the tool generates text embeddings using watsonx.ai models, which is a specific verb and resource that distinguishes it from sibling tools like watsonx_chat and watsonx_generate.

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?

The description provides no guidance on when to use this tool versus alternatives, nor any exclusions or prerequisites. It is a bare statement of functionality.

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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