get_lc_audit_result
Poll a TallyMesh audit job and fetch the verdict + per-rule findings (each pinned to a field and cited to the UCP600/ISBP article) + report paths.
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| job_id | Yes |
lc-checker
TDQS
A3.8/5.0
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must carry the full burden. It mentions 'poll', indicating non-immediate results, and describes the returned data. However, it does not disclose idempotency, rate limits, or error behavior, leaving some gaps.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, front-loaded sentence that efficiently conveys the tool's purpose and return data, with no unnecessary words. It is appropriately sized for the tool's simplicity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description covers the main action and return elements but lacks details on polling mechanics (e.g., retry logic, timeout), error conditions, or parameter specifics. Given the low schema coverage and no output schema, more context would be beneficial.
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 has 0% description coverage for the single parameter job_id. The description does not elaborate on the parameter's meaning, format, or constraints beyond the schema's type, failing to compensate for the lack of schema documentation.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool polls an audit job and fetches verdict, per-rule findings with field and article citations, and report paths. It uses specific verbs and resource identification, distinguishing it from the sibling tool start_lc_audit.
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?
The description implies the tool is used after starting an audit job, but does not explicitly state prerequisites or when not to use. The sibling tool name suggests a clear workflow sequence, providing contextual usage guidance.
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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TDQS
A4.2/5.0
Disambiguation5/5
The two tools have clearly distinct purposes: one starts an audit and returns a job_id, the other polls for results. No ambiguity or overlap.
Naming Consistency5/5
Both tool names follow the verb_noun pattern with snake_case (start_lc_audit, get_lc_audit_result), providing a predictable and consistent naming convention.
Tool Count5/5
With only two tools, the server is well-scoped for its purpose: initiating an audit and retrieving results. The count reflects a focused workflow without unnecessary tools.
Completeness4/5
The tool surface covers the core audit lifecycle (start and poll). A minor gap is the lack of a cancel or list audits tool, but this is acceptable for a minimal service.