Score an AI response against a prompt using heuristic metrics (length, relevance, structure, completeness)
| Name | Required | Description | Default |
|---|---|---|---|
| prompt | Yes | The original prompt/question | |
| criteria | No | Optional keywords that should appear in response | |
| response | Yes | The AI response to evaluate |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the transparency burden. It adds some context by noting 'heuristic metrics' (length, relevance, structure, completeness), which implies non-deterministic or approximate scoring. However, it does not disclose the output format, edge cases, or any limitations beyond listing metrics.
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 leads with the primary action and resource, then lists the metrics. Every word adds value, with no redundancy or filler. It is appropriately sized for the tool's simplicity.
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?
The tool has three parameters, no output schema, and no annotations, so the description must explain return values and usage context. It fails to state what the score looks like (numeric range, object, etc.) or how to interpret the result. This leaves a significant gap for the agent to invoke it confidently.
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 description coverage is 100%, so the baseline is 3. The description implicitly references 'prompt' and 'response' but adds no explicit parameter-level detail beyond the schema. The optional 'criteria' parameter is not mentioned in the description, but the schema already documents it.
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 states a specific verb ('Score'), a clear resource ('an AI response against a prompt'), and lists the heuristic metrics used. It distinguishes from sibling tools like 'compare_responses' and 'text_metrics' by emphasizing prompt-relative evaluation.
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?
The description clearly indicates when to use the tool (whenever an AI response needs scoring against a prompt). However, it does not explicitly mention alternatives or situations where a sibling tool would be more appropriate, lacking exclusions or when-not-to-use guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
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.
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.
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.
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.