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get_exam_cases

Not for clinical use. Orinyx checks medication claims against cited FDA label text for testing and evaluation only. Do not use its results to diagnose, treat, prescribe or make patient care decisions. Use synthetic test data only. Sitting an exam: get every case at once (transcripts, patient messages or encounter notes) and how to answer them. Speed is then reported as not measured; prefer get_next_exam_case.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "run_id": "<run_id>"
    },
    "name": "get_exam_cases"
  }
}

Input Schema

NameRequiredDescriptionDefault
run_idYes

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

B3/5.0
Behavior3/5

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

No annotations are supplied, so the description carries the full burden. It does disclose a genuine side effect ('Speed is then reported as not measured') plus safety constraints (synthetic test data only, not for clinical use). It says nothing about read-only semantics, error behavior for an invalid run_id, or payload size for a bulk fetch, which is a meaningful gap for a bulk retrieval tool.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness2/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The functional content is two sentences, preceded by three sentences of boilerplate that is likely duplicated across every sibling tool, so the useful guidance is not front-loaded. The text is also visibly mangled mid-sentence ('or\n encounter notes)'), and the final clause 'prefer get_next_exam_case' reads as a soft retraction of the use case just described.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness1/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

An output schema exists, so return values need not be explained, but the definition still omits how run_id is obtained, what a 'case' comprises beyond a parenthetical list, and how this fits the start_a_test / submit_exam_answers / finish_exam lifecycle. For a bulk-fetch tool in a multi-step exam workflow, that leaves the agent under-informed.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0% and the sole parameter run_id is never mentioned in the description, so the agent gets no guidance on where a run_id comes from (presumably start_a_test) or what a 'run' scopes. Only one parameter keeps this from being worse than a 2.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

Buries the verb-and-object slightly inside 'Sitting an exam: get every case at once (transcripts, patient messages or encounter notes)', but the resource (all exam cases for a run) and its contents are concrete. It also names the sibling it differs from (get_next_exam_case), so an agent can distinguish it without opening schemas. The roughly half of the text spent on the clinical disclaimer dilutes, but does not obscure, the purpose.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Gives a real selection rule: use this when sitting an exam and wanting every case up front, and 'prefer get_next_exam_case' otherwise. The consequence that speed will be 'reported as not measured' explains the tradeoff. It stops short of a crisp when-not statement and never ties itself to start_a_test or finish_exam, leaving some workflow context to inference.

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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