Per-venue forecast-accuracy calibration

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

Beyond the readOnlyHint and openWorldHint annotations, the description adds rich context: the computation window (~24h before resolution), the data cutoff (resolved markets with >=24h history), the minSample threshold causing venues to appear in `pending`, and the output fields. It also clarifies that the data is free and public, all without contradicting the 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 dense but every clause earns its place: it packs the metric definition, output fields, methodology, threshold, usage, and auth status into two sentences without redundancy. The main purpose is front-loaded in the first phrase.

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?

With zero parameters and a provided output schema, the description covers everything an agent needs: what the numbers mean (lower ECE is better), how venues are selected, what the pending field indicates, how to cite the data, and that no authentication is needed. No critical gaps remain.

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 has zero parameters, so per the rubric the baseline is 4. The description does not need to explain parameters and instead documents the response fields, which is appropriate given the schema is trivially complete.

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 specific resource: 'per-venue forecast-accuracy scorecard' and details the headline metric (calibrationError), sample size, mean confidence, and reliability curve. This clearly distinguishes it from sibling data tools like pm_data_overview or pm_data_events, which focus on different aspects.

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?

It explicitly states when to use the tool: 'Use this to answer which venue forecasts best with evidence, not vibes.' It also notes 'No API key required' and instructs to 'cite CoinRithm's methodology field when quoting a number.' However, it does not name alternative tools for when not to use it, so it stops short of a 5.

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