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get_health

Retrieve code-health scores and findings from stored analysis: get a dashboard overview, or ranked file-level findings with refactoring and performance queues.

Instructions

Code-health scores and findings from stored analysis.

No ``targets`` returns a dashboard; targets return ranked files and findings.
Never recomputes health: commit changes, then run ``repowise update``.
Every block and accepted value: docs/agent/MCP_TOOLS.md.

Args:
    targets: file paths or ``module:<name>``. Empty means dashboard;
        unmatched ones land in ``unresolved``, surviving ``only``.
    include: ``biomarkers`` | ``refactoring`` | ``trend`` | ``coverage`` |
        ``accuracy`` | ``signals`` | ``churn_complexity``, or a dimension.
        ``performance`` and ``refactoring`` add their queues.
    only: keys to keep; identity, counts and recovery survive.
        ``biomarkers``, ``accuracy`` and ``refactoring`` alias their block
        key. ``performance``, ``defect`` and ``maintainability`` do not:
        they filter rows and land in ``unknown_only_keys``.
    repo: usually omitted.
    limit: max rows per ranked list, ``0`` for none.
    cursor: zero-based offset into a ranked list.
    finding_id: stable ``id`` from a health finding.
    plan_id: stable ``id`` from a refactoring plan.
    opportunity_id: ``perf...`` or ``refop...`` id from a directive or
        queue: the unit, its steps or plan, and evidence paged by
        ``only=["*_evidence"]``. Excludes the two ids above.
    refactoring_view: ``diversified`` (default) | ``canonical`` |
        ``file_spread``; refactoring_type / _confidence / _effort filter.
    performance_view / _context / _boundary / _confidence / _sort: queue
        projection and filters; the facets list them.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
onlyNo
repoNo
limitNo
cursorNo
includeNo
plan_idNo
targetsNo
finding_idNo
opportunity_idNo
performance_sortNo
performance_viewNo
refactoring_typeNo
refactoring_viewNodiversified
refactoring_effortNo
performance_contextNo
performance_boundaryNo
performance_confidenceNo
refactoring_confidenceNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Install Server

TDQS

A4.8/5.0
Behavior5/5

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

With no annotations, the description carries the full burden, and it reveals key behaviors: read-only over stored data, dashboard vs ranked-file modes, 'unmatched ones land in unresolved, surviving only', 'only' alias/filter effects, and the exclusive nature of opportunity_id. This goes well beyond a generic 'get health data' statement.

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 long but every sentence is information-dense and earns its place. It front-loads purpose and the most important behavioral caveat, then presents parameters in a consistent terse format that avoids filler for an 18-parameter tool.

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

Completeness5/5

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

For a highly complex tool with no annotations, this is remarkably complete: it discloses storage/read behavior, update prerequisite, special identifiers, view modes, filters, and fallback fields like unresolved and unknown_only_keys. Because an output schema exists, the lack of a return-format explanation is not a gap.

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 compensates by explaining every parameter: targets accepts file paths or module:<name>, include lists accepted facets and queue side-effects, only explains key persistence and aliasing, and the refactoring_/performance_ parameters describe projections and filters. The docs pointer 'docs/agent/MCP_TOOLS.md' also fills in accepted values.

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 opening line 'Code-health scores and findings from stored analysis' names a specific domain and return type, and the next sentence sharpens behavior: 'No targets returns a dashboard; targets return ranked files and findings.' This differentiates get_health from sibling tools such as get_overview or get_risk by its health-analytics focus.

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 gives clear context: it reads stored analysis and 'Never recomputes health', with the explicit prerequisite 'commit changes, then run repowise update' when freshness is needed. It does not compare against sibling MCP tools by name, but the stored-analysis caveat is enough to steer an agent away from using this as a computational tool.

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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