embedding_similarity
Score two caller-supplied embedding vectors with a supported similarity model.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| left | Yes | ||
| model | No | embeddings.cosine_similarity | |
| right | Yes |
Score two caller-supplied embedding vectors with a supported similarity model.
| Name | Required | Description | Default |
|---|---|---|---|
| left | Yes | ||
| model | No | embeddings.cosine_similarity | |
| right | Yes |
Changes observed during successful MCP inspections.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, and the description fails to disclose behavioral traits: supported similarity models, error handling (e.g., vector length mismatch), or whether the operation is read-only. Bare minimum only.
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, no wasted words, but lacks structuring like bullet points or sections. Acceptable for such a short description.
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 3-parameter tool with no output schema and no annotations, the description is too brief. It omits important context about model support, return value, and edge cases.
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 has 3 parameters with 0% description coverage. The description adds minimal value: it mentions 'two embedding vectors' (left, right) and 'supported similarity model' (model), but provides no details on vector format, model selection, or constraints beyond the schema.
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?
Description uses specific verb 'score' and resource 'embedding vectors', clearly distinguishing from siblings like 'embeddings' (generates vectors) and 'rerank' (reorders results).
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?
Description does not specify when to use this tool versus alternatives such as 'score' or 'rerank', nor does it provide any conditioning for when to choose this similarity comparison.
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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