Get AIPI Index Benchmarks

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

While annotations declare readOnlyHint=true and destructiveHint=false, the description adds substantial behavioral context: the return structure (14 indexes across 4 categories), specific pricing components included (input, cached input, output), scope limitations (market-wide vs. individual), and availability ('Fully public — available to all tiers'). Does not mention rate limits or pagination beyond the limit parameter.

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?

Description is well-structured and front-loaded: opens with the AIPI definition, immediately states the 14-index return structure, breaks down the four categories with their analytical purposes, clarifies scope and availability, then provides actionable examples. Every sentence conveys unique information; no redundancy or filler.

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?

Despite lacking an output schema, the description comprehensively details the return values (14 benchmarks across 4 categories with specific pricing components). Given the moderate complexity (4 optional parameters, domain-specific concepts) and presence of clear annotations, the description provides sufficient context for an agent to understand the full tool contract.

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?

With 100% schema coverage, the baseline is 3. The description adds significant value by explaining the taxonomy of index_category (detailing that Modality has 6 types, Channel has 4, etc.) and providing concrete examples for index_code in the examples section ('AIPI TXT GLB', 'AIPI OSS GLB'). This semantic context helps agents understand valid values beyond the schema's type definitions.

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 defines the tool as returning 'AIPI (ATOM Inference Price Index) — chained matched-model price benchmarks for AI inference.' It distinguishes from siblings by explicitly stating these are 'market-wide benchmarks, not individual vendor prices,' contrasting with tools like compare_prices or get_vendor_catalog that likely return specific vendor data.

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?

Provides explicit when-to-use guidance through five concrete examples mapping natural language queries to specific parameter combinations. Also clarifies when NOT to use by stating these are not individual vendor prices, implicitly directing users to sibling tools for specific pricing queries. Includes clear intent: 'Use them to understand where the market is and how it's moving.'

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.