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get_health

Assess code health with scores for defect risk, maintainability, and performance, and receive findings with actionable details, optionally targeting specific files for pre/post-edit checks.

Instructions

Code-health scores and findings — self-check a file before/after editing.

No ``targets`` → repo dashboard (KPIs, worst files, ``high_leverage_files``
ranked by ``weighted_deficit`` = the files that actually move the repo
average). With ``targets`` → per-file scores + findings for those paths.

Three co-equal dimensions per file: ``score`` (defect risk, the headline),
``maintainability_score`` (readability/change-cost smells), and
``performance_score`` (static I/O-in-loop / N+1 RISK, high-precision /
low-recall, never blended into the defect headline). Each finding carries
its ``dimension``; a performance finding's ``details`` name the
``boundary_kind`` (db/network/filesystem/subprocess/lock) and, for
cross-function N+1, the caller→sink ``path``.

Args:
    targets: file paths or ``module:<name>``. Empty → dashboard mode.
    include: opt-in blocks (default stays lean): ``biomarkers`` (findings
        in dashboard mode) | ``refactoring`` (deterministic suggestion per
        finding) | ``trend`` | ``coverage`` | ``accuracy`` (does the score
        rank the buggy files first) | ``signals`` (per-file churn/owners/
        degree, targeted mode) | ``churn_complexity`` (danger-zone
        quadrant) | ``performance``/``defect``/``maintainability``
        (filter findings to one dimension).
    repo: usually omitted.
    limit: max rows in ranked lists (capped at 50).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
repoNo
limitNo
includeNo
targetsNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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

The description discloses key behaviors: the two modes, dimensions of scores, and that performance findings are high-precision/low-recall. It details the include options and the capped limit. However, with no annotations, it could explicitly state it is read-only and non-destructive, which is implied but not stated. It also doesn't mention auth requirements or rate limits, but given the non-destructive nature, the transparency is good.

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

Conciseness4/5

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

The description is well-structured with clear sections and bullet-like formatting for the include list. It front-loads the main purpose. However, it is somewhat verbose and could be streamlined without losing information. Every sentence is informative, but a shorter version might suffice.

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?

Given the tool's complexity (4 params, many include options, output schema present), the description covers all necessary aspects: modes, dimensions, parameter behaviors, and output details. The output schema is supplemented by the description's explanation of findings and details. No gaps left for an AI agent to misinterpret.

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?

Despite 0% schema description coverage, the description comprehensively explains all four parameters: targets (dashboard vs per-file), include (lists all opt-in blocks and their effects), repo (usually omitted), and limit (capped at 50). This adds substantial meaning beyond the schema's type definitions.

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 explicitly states the tool computes code-health scores and findings, distinguishing between dashboard mode (no targets) and per-file mode (with targets). It clearly defines the three dimensions (score, maintainability_score, performance_score) and contrasts with siblings like get_risk and get_overview by focusing on self-check of files.

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 clear context: 'self-check a file before/after editing' and explains when to use targets vs no targets. However, it lacks explicit guidance on when not to use this tool or direct comparisons to sibling tools (e.g., when to use get_risk instead).

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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