Compare the price of one model across the providers that host it.
Args:
model_id: Canonical model id, e.g. 'meta/muse-spark-1.1'.
Returns: per-provider endpoints with pricing, showing where direct
provider prices diverge (e.g. from OpenRouter's negotiated rate).
| Name | Required | Description | Default |
|---|---|---|---|
| model_id | Yes |
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Changes observed during successful MCP inspections.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the burden of behavioral disclosure. It adds useful context: the return contains per-provider endpoints with pricing and highlights divergence from OpenRouter's negotiated rate. It could mention error cases or authorization needs, but for a read-only comparison tool the disclosed behavior is sufficient.
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 compact and well-structured with a one-sentence purpose followed by an Args/Returns breakdown. Every sentence adds value, and the format is easy to parse.
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 is simple: one parameter, an output schema exists, and the description covers the core behavior and return semantics. Nothing essential for calling the tool correctly is missing, given the single required parameter and available output schema.
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 0%, so the description must compensate. It explains that model_id is a canonical model id and provides a concrete example ('meta/muse-spark-1.1'), giving the agent the format and a template for valid values. This is exactly what is needed beyond the schema's bare title.
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 ('Compare') and resource ('price of one model across the providers that host it'). This clearly distinguishes it from siblings like get_model, get_provider, and list_providers, which focus on individual models or providers rather than cross-provider comparison.
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 when to use this tool: when needing per-provider price comparison for a specific model. However, it does not explicitly list alternatives or exclusions, leaving the agent to infer the boundary with related tools like get_model or get_provider.
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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