Compute embedding similarity between two IRIs and return cosine similarity, Poincaré distance, and product score.
Compute embedding similarity between two IRIs — returns cosine similarity (text), Poincaré distance (structural), and product score.
| Name | Required | Description | Default |
|---|---|---|---|
| iri_a | Yes | First IRI | |
| iri_b | Yes | Second IRI |
Changes observed during successful MCP inspections.
v1.0.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are present, so the description carries the full burden of disclosure. It does not state whether the tool is read-only or has side effects. While it likely is a read-only computation, this is not explicitly communicated, leaving behavioral ambiguity.
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 sentence that front-loads the action and return values. Every word earns its place; there is no redundancy or filler.
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?
Without an output schema, the description lists the three return scores, which is helpful. However, it does not explain what 'product score' means, nor does it mention prerequisites (e.g., that embeddings must exist). For a tool with no annotations and no output schema, the description is adequate but not fully complete.
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 100%, with both parameters described as 'First IRI' and 'Second IRI'. The description adds no additional meaning beyond the schema, such as expected format, origin, or constraints. Baseline 3 applies as schema already does the work.
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 computes embedding similarity between two IRIs and specifies the return types (cosine similarity, Poincaré distance, product score). The verb 'compute' plus resource 'embedding similarity' is specific and distinguishes it from sibling tools like onto_align or onto_diff.
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?
No guidance on when to use this tool vs alternatives is provided. The description only states what it does, without mentioning prerequisites, when not to use it, or comparing to siblings like onto_align or onto_diff. This leaves the agent to infer usage from the name alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.