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sumo_qa_generate_qa_report

Compile persisted QA artifacts into a local QA report, returning a compact readiness summary. Optionally write the self-contained HTML page to disk.

Instructions

Compose the persisted .sumo-qa artifacts (repo map, diff impact, risk ledger, context bundle) into the local QA report and return a compact readiness summary. The rendered HTML body never rides back to the host — pass write_to to persist the self-contained static page and open it from disk.

Common natural-language phrasings that map to this tool: "generate the QA report", "build the QA dashboard for this repo", "give me the local QA readiness report", "render qa-report.html".

root is the repository to report on (absolute or relative to the MCP server's working directory). Every artifact is OPTIONAL: a missing, invalid, or stale source renders an explicit honest state. The readiness verdict (ready / ready_with_accepted_residuals / blocked / insufficient_evidence) is derived by the QaScorecard readiness engine from the risk ledger + context bundle — missing data is never reported as passing evidence.

risk_ledger_rows / context_bundle are inline overrides for the chat flow where the ledger/bundle was built in-conversation and never persisted (the same shapes sumo_qa_format_risk_ledger / sumo_qa_format_context_bundle accept). They take precedence over any on-disk file and are validated BEFORE anything is written.

write_to is optional — when set, the page is written there (relative paths land under the target repo; the conventional value is .sumo-qa/qa-report.html). Without it the tool writes nothing.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
rootYes
write_toNo
context_bundleNo
risk_ledger_rowsNo
Behavior4/5

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

The description discloses that the HTML body never rides back to the host, writing only when write_to is set, and overrides are validated. Annotations are minimal, so the description carries the burden well, though it doesn't mention performance implications.

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 well-structured with a summary, natural-language examples, and parameter details, but it is somewhat lengthy and includes redundant explanations (e.g., 'missing data is never reported as passing evidence' is restated).

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

Completeness4/5

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

The description covers the main use case, parameter behavior, and edge cases like missing artifacts. No output schema exists, but the return value is described as a 'compact readiness summary' with readiness verdict. Could detail the summary format more.

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

Parameters5/5

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

Schema coverage is 0%, but the description explains each parameter in detail: root as repository path, write_to as optional output path, and the inline override parameters with their shapes and precedence. This adds significant meaning beyond the schema.

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 composes .sumo-qa artifacts into a local QA report and returns a readiness summary. The verb 'compose' and mention of specific artifacts distinguish it from siblings like sumo_qa_format_risk_ledger, which handle individual pieces.

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 provides natural-language phrasings and explains when to use the tool (after artifacts exist). It details optional parameters and precedence, but does not explicitly state when not to use it or compare directly with alternatives.

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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