market_index

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

Annotations already declare readOnlyHint and openWorldHint, but the description adds real behavioral context beyond them: the read is 'instant, no scan' (precomputed source) and it 'never invents a figure that isn't precomputed,' which tells the agent how to interpret missing results. It stops short of discussing coverage gaps per model or empty-result behavior.

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 purpose and routing guidance are front-loaded ahead of the Args block, and every sentence carries information. Slight redundancy in restating each parameter in prose after the summary paragraph keeps it from a 5.

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?

An output schema exists, so return-value explanation is optional, yet the description still names the useful fields (low/25th/median/75th/high, sample size, trend). Combined with the routing guidance, an agent has enough to invoke correctly; only edge cases like combined query+brand are uncovered.

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 0% and the schema carries no enum for `category`, so the description must carry the load — and it does, supplying the three valid values, inference-when-omitted behavior, example values for `query`, and the query/brand alternative. It doesn't state what happens if both `query` and `brand` are supplied.

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 verb+resource — 'Precomputed model-level price index' — and immediately characterizes the data source (ALT/FNDATA hot-stats). It explicitly distinguishes itself from market_stats and price_trend by category scope, so an agent can route without opening a schema.

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?

Gives explicit selection logic: a typed model `query` for one model's band, a `brand` alone for the whole brand set, and names market_stats or price_trend for categories outside handbags/watches/cars. When-to-use and alternatives are both present.

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