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lumishoang

OpenRouter MCP Server

by lumishoang

compare_models

Compare AI models side by side to evaluate performance, pricing, and capabilities for informed selection.

Instructions

Compare multiple models side by side.

Args: model_ids: Comma-separated model IDs

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
model_idsYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior2/5

Does 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. It states the action ('compare') but doesn't describe what the comparison entails (e.g., metrics, visual output, side-by-side table), whether it's read-only or has side effects, authentication needs, rate limits, or error conditions. The description lacks critical behavioral context for a tool with no annotation coverage.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is appropriately concise with two sentences: a clear purpose statement and a brief parameter note. It's front-loaded with the main function. However, the 'Args:' section could be integrated more smoothly, and there's room to add value without verbosity, such as by clarifying the comparison output or usage context.

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?

Given the tool has an output schema (which should detail return values), the description doesn't need to explain outputs. However, with no annotations, 0% schema coverage for the single parameter, and sibling tools present, the description is incomplete. It lacks context on comparison specifics, when to use, and behavioral traits, making it minimally adequate but with clear gaps for effective agent use.

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?

The description adds minimal semantics beyond the input schema. It explains that 'model_ids' are 'comma-separated model IDs,' which clarifies the format (comma-separated string) not evident from the schema alone (schema coverage is 0%). However, it doesn't define what a 'model ID' is, provide examples, or explain how to obtain valid IDs from siblings like 'list_models.' With 0% schema coverage, this partial compensation earns a baseline score.

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: 'Compare multiple models side by side.' This specifies the verb (compare) and resource (models) with the scope of 'multiple' and 'side by side.' However, it doesn't explicitly differentiate from sibling tools like 'get_model' (single model) or 'list_models' (list without comparison), though the 'side by side' aspect implies a comparative analysis not present in siblings.

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?

The description provides no guidance on when to use this tool versus alternatives. It mentions 'multiple models' but doesn't specify thresholds (e.g., use for 2+ models) or contrast with siblings like 'get_model' for single models or 'search_models' for filtered lists. There's no mention of prerequisites, exclusions, or recommended contexts for comparison.

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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