compare_modelsCompare 2–5 LLM models side by side on price, context window, and capabilities to identify the cheapest option for input, output, or blended workloads.
Compare 2–5 models side by side on price, context window, and capabilities, with a verdict on which is cheapest for input, output, and a typical blended workload.
| Name | Required | Description | Default |
|---|---|---|---|
| model_ids | Yes | List of 2–5 model ids to compare (from search_models). |
Changes observed during successful MCP inspections.
v0.1.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. The description implies a read-only comparison operation with no side effects, but it does not explicitly state that it does not modify data or require special permissions. The behavior is clear given the nature of the tool (comparison), so a slight deduction for missing explicit safety statement.
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, well-structured sentence that front-loads the core action (compare 2–5 models) and provides key details (price, context, capabilities, verdict). No unnecessary words or repetition.
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 tool with only one parameter and no output schema, the description is sufficiently complete. It specifies the input constraints and the output (verdict on cheapest for different workload types). It could briefly mention the output format (e.g., a side-by-side table), but overall it provides adequate context for an AI agent to select and invoke the tool correctly.
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% and the schema description already explains the parameter (list of 2-5 model ids from search_models). The tool description does not repeat this, which is appropriate. The baseline score of 3 is correct since the schema handles the parameter semantics adequately.
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's purpose: compare 2–5 models on price, context window, and capabilities, and provide a verdict on cheapest for input/output/blended. This specific verb+resource combination distinguishes it from siblings like cheapest_models (which likely lists cheapest models without comparison) and get_model_pricing (which focuses on a single model's pricing).
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 implies usage context: when you want to compare specific models, with a clear constraint of 2-5 models. However, it does not explicitly state when not to use this tool or mention alternative sibling tools (e.g., cheapest_models for a simple list or get_model_pricing for individual pricing). The mention 'from search_models' provides a prerequisite but lacks exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.