blockrun_price

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

Annotations already mark the tool read-only and non-destructive, and the description adds substantial behavioral context on top: per-category pricing, free list discovery, Pyth as the data source, session being ignored for non-equities, and unix timestamp requirements. This goes well beyond the safety profile conveyed by annotations.

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 well-organized into a summary, action list, pricing section, market list, and examples. Every section earns its place, and the examples are compact yet illustrative. There is no vague filler, despite covering multiple actions, categories, and cost models.

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?

Given the tool's complexity (11 parameters, 5 enums, 3 actions, no output schema), the description covers the required category/action pairing, all market codes, pricing, and example payloads. A minor gap is the lack of response-shape details for the returned quotes or OHLC bars, but this isn't essential for selecting and invoking the tool correctly.

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 real semantic value through examples that show valid parameter combinations, the stocks/market dependency, default resolution behavior, and action-specific fields. It doesn't document every parameter beyond the schema, but it clarifies how to use them together.

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 opens with a precise statement of what the tool returns: realtime quotes and OHLC history for crypto, FX, commodities and 12 stock markets, Pyth-backed. It enumerates three concrete actions (price, history, list) and provides examples, making its scope distinguishable from sibling market/data tools without ambiguity.

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 gives clear conditions for each action, pricing tiers, and the market requirement for stocks, so an agent knows when to invoke which action. It stops short of naming sibling tools to avoid, and doesn't explicitly enumerate when-not-to-use cases, but the action-level guidance is strong.

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