compare_responses
Compare and rank multiple AI responses to the same prompt
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| prompt | Yes | ||
| responses | Yes |
ai-eval
Schema Changelog
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
- First observed
TDQS
C2.4/5.0
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must disclose behavior, but it only says 'compare and rank' without explaining ranking criteria, output format, or side effects. It doesn't clarify whether rankings are returned as scores, an ordered list, or something else.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence and easy to read, but it is under-specified rather than properly concise. It front-loads the core action but omits necessary details at the cost of usefulness.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With no output schema, no annotations, and only a one-sentence description, the tool is insufficiently specified for an agent to understand return values, ranking criteria, or when to invoke it correctly. The description covers the basic purpose but lacks essential operational context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, and the description does not elaborate on the prompt or responses parameters. The property names are self-explanatory, but the description adds no meaning beyond the schema, such as constraints or relationship between parameters.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: comparing and ranking multiple AI responses. The verb 'compare and rank' is specific, and 'multiple AI responses' distinguishes it from score_response, though it doesn't explicitly mention sibling alternatives.
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?
No guidance is provided on when to use this tool versus score_response or text_metrics. The description does not mention alternatives, prerequisites, or typical use cases, leaving the agent to infer context.
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
B3.4/5.0
Disambiguation4/5
The tools are mostly distinct: compare_responses handles multiple responses, score_response handles a single response, and text_metrics provides basic text statistics. There is minor potential for confusion between compare_responses and score_response when evaluating a single response, but descriptions clarify the difference.
Naming Consistency4/5
Tool names follow a consistent snake_case pattern with two components, but while compare_responses and score_response use verb_noun structure, text_metrics uses noun_noun. This minor inconsistency is not disruptive, and the overall pattern is predictable.
Tool Count5/5
With 3 tools covering comparison, scoring, and metrics, the count is well-scoped for an AI evaluation server. Each tool has a clear purpose and earns its place without unnecessary bloat.
Completeness4/5
The core workflows of comparing and scoring responses are covered, and text_metrics adds useful auxiliary data. Minor gaps exist, such as lack of support for rubric-based evaluation or generating detailed reports, but these are not fundamental to the server's stated purpose.