compute_similarity
Compute cosine similarity between two texts. Returns score in [-1,1]. Useful for dedup and relatedness.
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| model | No | ||
| text_a | Yes | ||
| text_b | Yes |
embedding-search
TDQS
A4.1/5.0
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the burden. It discloses the return range and the algorithm, which is sufficient for a non-mutating compute operation. However, it does not describe the behavior of the 'model' parameter or any potential side effects.
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?
Two sentences, no filler, and front-loaded with the action. Every word earns its place.
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?
Without annotations or output schema, the description provides the core function and a use case, but omits details about the model parameter and how it relates to sibling tools. It is adequate for a simple tool but has clear gaps.
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 0%, so the description must compensate. It maps 'two texts' to text_a and text_b, but leaves the optional 'model' parameter unexplained. The parameter names are self-evident, but the description adds minimal value 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 a specific verb ('compute'), resource ('cosine similarity between two texts'), and even specifies the score range. This distinguishes it from siblings like generate_embeddings and semantic_search, which focus on other operations.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
It mentions 'Useful for dedup and relatedness,' providing clear use-case context. However, it does not explicitly state when to prefer this over generate_embeddings or semantic_search, nor any exclusions.
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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TDQS
A4.2/5.0
Disambiguation5/5
Each tool addresses a distinct operation: generate_embeddings creates vectors, compute_similarity compares two texts directly, and semantic_search ranks a list of documents against a query. No overlapping purposes.
Naming Consistency4/5
Two tools follow the verb_noun pattern (generate_embeddings, compute_similarity), but semantic_search uses a noun phrase instead, which is a minor deviation from the otherwise consistent style.
Tool Count5/5
With only three tools, the server is tightly scoped to the embedding-based search domain. Each tool has a clear role, and the count is appropriate for the functionality offered.
Completeness5/5
The server covers the complete workflow: embedding generation, pairwise similarity, and semantic search. There are no obvious missing operations for the stated purpose.