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

With no annotations, the description carries the full burden. It lists what data will be returned, indicating a read operation, but does not explicitly state that it is read-only, nor does it disclose potential edge cases (e.g., what happens if neither slug nor id is provided, or if both are given).

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 a single concise sentence that packs in the returned data fields and the required input format. Every word adds value, with no redundancy or fluff.

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 simple tool with two optional parameters and no output schema, the description covers the essential return values and invocation requirement. It lacks context about which market platform or data source this applies to, but that is not critical given the tool's narrow scope.

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?

The input schema already provides descriptions for both parameters (id and slug), achieving 100% coverage. The description adds the important semantic that slug and id are alternatives ('Provide slug or id'), clarifying the expected usage beyond the schema.

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 indicates the tool returns full market information, enumerating specific data fields like outcomes, volume, and spread. It distinguishes itself from search tools by requiring a slug or id, though it doesn't explicitly differentiate from the sibling tool pg_kalshi_market_details.

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 provides a clear usage precondition ('Provide slug or id') and implies this tool is for fetching details by identifier rather than searching. However, it does not explicitly state when to prefer this over alternatives like pg_market_trades or pg_kalshi_market_details.

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