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Glama

get_submission_rule

Get CDC specimen collection and submission rules for a disease. Intended for decision support only; verify current official sources, and never input patient data.

Instructions

CDC compatibility alias. decision_support_only=true; verify_current_official_source=true; not_validated_for_hospital_deployment=true; not_pre_submission_storage=true. 不得輸入病人資料。

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
diseaseYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
itemsYes
limitYes
notesYes
queryYes
offsetYes
safetyYes
warningsYes
data_modeYes
operationYes
truncatedYes
provenanceYes
sample_onlyYes
availabilityYes
result_statusYes
source_statusYes
total_matchesYes
returned_countYes
coverage_detailYes
coverage_statusYes
evaluated_as_ofYes
contract_versionNopublic-contract-v1
evaluated_timezoneYes
snapshot_traceableYes
replacement_operationYes
availability_reason_codeYes
historical_truth_supportedYes
currently_reproducible_from_upstreamYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.1

TDQS

C2/5.0
Behavior2/5

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

No annotations are provided, so the description must carry the full behavioral burden. It does disclose several constraints, including not being validated for hospital deployment and not being for pre-submission storage, and it warns not to input patient data. However, these are cryptic boolean-style statements rather than clear behavioral consequences, side effects, or call expectations.

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

Conciseness3/5

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

The description is short and compact, which is good, but it relies on terse flag-like prose and a Chinese phrase without explaining their practical meaning. It is concise but not optimally structured for agent comprehension.

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

Completeness2/5

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

For a simple one-parameter tool with an output schema, the description still omits the core purpose and the meaning of the 'disease' parameter. The constraint flags add some context, but the essential information an agent needs to select and call the tool correctly is missing.

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

Parameters1/5

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

The schema has one required parameter, 'disease', with 0% description coverage. The description never mentions 'disease', valid values, formats, or how to supply it, so it provides no help for parameter usage.

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

Purpose2/5

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

The description labels the tool as a 'CDC compatibility alias' but never states what it does, retrieves, or returns. It does not use a clear verb+resource structure and does not help distinguish it from sibling tools like get_collection_method or get_transport_requirement.

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

Usage Guidelines2/5

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

There is no explicit guidance on when to use this tool versus alternatives. Flags such as 'decision_support_only=true' and 'not_validated_for_hospital_deployment=true' hint at restrictions, but they are not translated into actionable usage direction.

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