Get detail for one provider: models, tier breakdown, price range.
Args:
provider_name: Provider name, e.g. 'DeepInfra', 'Novita', 'Venice'.
Returns: provider detail with model list and pricing.
| Name | Required | Description | Default |
|---|---|---|---|
| provider_name | Yes |
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Without annotations, the description carries the burden and does state the output ('Returns: provider detail with model list and pricing'), implying a read-only getter. It does not discuss not-found or error behavior, but for a simple fetch tool with an output schema this is adequate.
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?
Compact three-part structure: one-line purpose with contents, parameter docs, return summary. No filler, and the parameter example is efficient.
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 single-parameter getter with an output schema, this description provides enough to choose, invoke, and interpret the tool: target scope, parameter format/examples, and return contents.
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 only defines provider_name as a string with no description. The description documents the parameter in Args and gives concrete examples ('DeepInfra', 'Novita', 'Venice'), which is exactly the guidance an agent needs to fill it correctly.
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?
States a specific action ('Get detail') on a specific resource ('one provider') and enumerates the detail payload (models, tier breakdown, price range). Clear enough to distinguish from sibling list_providers or compare_providers.
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 phrase 'for one provider' sets clear selection context: an agent should call this when it needs a single provider's detail rather than a list or comparison. It does not explicitly name alternatives or say when not to use it.
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.
Each tool targets a distinct resource/action, but get_model and compare_providers both surface pricing for a single model, and search_models could overlap with get_model for direct lookups. The descriptions are clear enough to resolve the ambiguity in practice.
All tools follow a consistent verb_noun snake_case pattern: get_* for details and history, list_* for enumeration, search_* for filtering, and compare_* for cross-provider comparison. No mixed naming conventions or vague verbs.
8 tools is well-scoped for an inference pricing/index analytics server: model detail, model history, provider listing, provider detail, cross-provider comparison, and composite index current/history. Each tool has a clear role and none feel redundant.
The read-oriented domain is well covered: models have search/detail/history, providers have list/detail/comparison, and the composite index has current and historical views. Minor gaps such as provider-level price history or direct multi-model side-by-side comparison are not essential and can be worked around with existing tools.