Fetch one decided DOHA case in full: what was alleged, the judge's findings per allegation, per-guideline formal findings, outcome, judge, representation, and appeal history. Use case_id from search_cases.
| Name | Required | Description | Default |
|---|---|---|---|
| case_id | Yes | The numeric case id from search_cases |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and destructiveHint=false, covering safety. The description adds that the case is 'decided' and lists return fields, but does not disclose error behavior, auth requirements, or other behavioral traits. It provides some context beyond annotations but not rich detail.
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 two tight sentences. The first delivers the core purpose and return details; the second gives a direct usage hint. No filler or repetition, with the key action front-loaded.
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 fetch tool with no output schema, the description provides a thorough list of return contents (allegations, findings, outcome, judge, representation, appeal history) and the required input source. It is complete enough for an agent to select and call the tool correctly, though it omits edge-case behavior.
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 schema already describes case_id as 'The numeric case id from search_cases' (100% coverage). The description repeats this instruction ('Use case_id from search_cases') but adds no new meaning, so the baseline of 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 opens with 'Fetch one decided DOHA case in full', which clearly identifies the verb (fetch), resource (one DOHA case), and scope (full, decided). The colon list enumerates the content, making it distinct from siblings like search_cases (which finds cases) and similar_cases (which finds related cases).
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 instructs 'Use case_id from search_cases', establishing a prerequisite and workflow. It does not explicitly exclude alternatives or mention when not to use it, but the need for a case_id makes the intended usage clear.
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 targets a distinct function: searching, retrieving a specific case, finding similar cases, and four different statistical analyses (overall, candor-related, conduct recency, and timeline). No two tools overlap in purpose.
All tool names follow a clear verb_noun pattern: get_ for retrieval, search_ for searching, similar_ for similarity. The verbs vary appropriately with the action, and snake_case is used consistently.
Seven tools is well within the ideal range for a search-and-analytics server. Each tool covers a necessary capability without redundancy or bloat.
The tool set covers the full workflow: comprehensive searching (search_cases), detailed case retrieval (get_case), similarity exploration (similar_cases), and four distinct statistical views (candor, conduct recency, overall statistics, timelines). No significant gaps are apparent for the stated purpose.