Pyth List Price Feeds

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, destructiveHint=false. The description adds behavioral context (e.g., 'oracle reads as context for pricing, risk checks, and market-aware agent decisions') and a constraint (not settlement proof), adding value beyond 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?

Concise at two sentences plus a brief SAP MCP context line. Front-loaded with purpose and filters. Could be more structured but efficient overall.

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 list tool with optional filters and existing output schema, the description covers purpose, usage, and behavioral notes. It is complete enough given the tool's simplicity.

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 100% with descriptions for both parameters. The description mentions filtering by query or asset type but does not add new meaning beyond what schema provides. Baseline 3 is appropriate.

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 states the verb 'list' and resource 'available Pyth price feeds' with optional filters by query or asset type. It distinguishes from sibling tools like pyth_getPrice and pyth_getPriceHistory which focus on specific price 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 context: 'Use for Pyth oracle price reads and feed discovery' and advises 'do not treat them as settlement proof.' While it doesn't list alternative tools, it gives clear when-to-use guidance and a usage caveat.

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