Return source-attributed public biographical facts for a judge, including birth details, computed age, education, professional and judicial careers, provenance, coverage gaps, and verification status. Demographic and political labels are excluded from this public tool.
| Name | Required | Description | Default |
|---|---|---|---|
| judge_slug | Yes | Canonical slug returned by find_judges |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description is transparent about what is included (biographical facts with sources) and explicitly excludes demographic/political labels. With no annotations provided, it covers safety and scope adequately, but does not disclose potential pagination, rate limits, or data freshness beyond source attribution.
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 focused sentence listing the tool's content and exclusions. It front-loads the main purpose and adds efficient detail without redundancy.
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 simple tool (one required parameter, no output schema, no nested objects), the description is fairly complete. It clarifies the tool's scope and exclusions. However, it does not describe the return value structure or format, which would be helpful for a factual biography tool.
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% (one parameter, described as canonical slug from find_judges). The description adds context that the slug is matched to a judge, but does not add meaning beyond what the 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 it returns source-attributed public biographical facts for a judge, and lists specific data categories (birth details, age, education, career, verification status). It distinguishes from siblings by stating it excludes demographic and political labels, and the sibling set includes get_judge_profile, which likely provides other judge info.
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 implies usage for obtaining factual biography without demographics, and mentions a prerequisite (judge_slug from find_judges). However, it does not explicitly state when to use this versus get_judge_profile or how to decide between them, leaving room for confusion.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Each tool has a clearly distinct purpose: finding judges, retrieving biography, profile, presentation rules, AI abuse cases, or recent changes. There is no overlap or ambiguity; descriptions further clarify boundaries.
All tools follow a consistent verb_noun pattern with snake_case (find_*, get_*). The naming is predictable and easy to interpret, aiding agent selection.
With 6 tools, the set is well-scoped for a judge and courtroom information server. Each tool addresses a distinct need without bloat or triviality.
Core workflows (finding judges, retrieving bio/profile, presentation rules, tracking changes) are covered. A potential minor gap is a tool for historical courtroom rules or direct case lookups, but the current set supports common use cases adequately.
