Get empirical judicial intelligence on European patent judges: median days to preliminary injunction, grant rate propensity, language breakdown, and known bench tendencies.
| Name | Required | Description | Default |
|---|---|---|---|
| judge_name | Yes | Full or partial judge name e.g. 'Zigann', 'Grabinski', 'Meade', 'Thomas' |
Changes observed during successful MCP inspections.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden of behavioral disclosure. 'Get' implies a read-only operation, and the listed output fields describe what the agent will receive. However, it does not explicitly state non-destructiveness, data source, or any limitations, leaving some ambiguity about side effects and scope.
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, front-loaded sentence that leads with the tool's purpose and then lists specific output data in a clear colon-delimited series. There is no filler or redundancy; every word earns its place.
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 single-parameter tool with no output schema, the description adequately covers what the agent needs to know: the output fields are enumerated. It does not cover potential edge cases like partial-name matching limitations or error conditions, but the schema and examples handle the input side, so the definition is largely complete.
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 description coverage is 100% and the judge_name parameter already includes format examples and partial-match guidance. The tool description does not add parameter-level detail, so it does not exceed the baseline of 3.
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?
Description states a specific verb ('Get') and resource ('empirical judicial intelligence on European patent judges'), then enumerates concrete output metrics (median days to preliminary injunction, grant rate propensity, language breakdown, known bench tendencies). This clearly distinguishes the tool from siblings like get_case_intelligence or get_patent_asset_risk, which focus on different entities.
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 context: use this tool when you need judge-specific empirical benchmarks, such as median PI timelines or grant rates. It does not explicitly name alternatives or exclusions, but the specificity of the output fields makes the appropriate usage evident.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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