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neogeweb3

io.github.neogeweb3/code-health-suite

by neogeweb3

find_hotspots

Identify code hotspots by analyzing git churn and complexity to flag high-risk files for prioritized refactoring. Requires git repository path, returns scores and breakdowns.

Instructions

Find code hotspots — files with high git churn AND high complexity. These are the riskiest files in a project: frequently changed AND hard to understand. Requires a git repository. Returns hotspot scores, risk levels, and churn/complexity breakdown.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
topNoReturn top N hotspots (default: 20).
pathYesGit repository root path.
since_daysNoLook back N days for churn data (default: 180).
Behavior3/5

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

No annotations are provided, so the description carries the full burden. It discloses a key prerequisite (git repository) and the return format (hotspot scores, risk levels, churn/complexity breakdown). However, it does not indicate whether the operation is read-only (though 'Find' suggests this), potential performance implications, or error behavior (e.g., missing git history). It adds some value but leaves several behavioral aspects underexplained.

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 compact and front-loaded with the core purpose in the first sentence. Each subsequent sentence contributes essential context: the risk rationale, a prerequisite, and the output format. No wasted words or redundancy.

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?

For a relatively simple analysis tool with no output schema, the description provides sufficient context: it defines the criteria, explains the purpose, states a prerequisite, and lists the return components. It does not delve into algorithm details or edge cases, but these are not critical for tool selection. The absence of any mention of error conditions or limitations is a minor gap, but overall the description is complete for practical use.

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

Parameters3/5

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

Schema description coverage is 100% and all three parameters are well-documented in the schema (path, top, since_days). The tool description adds no additional parameter-level semantics beyond what the schema already provides. Per rubric, this is the baseline score of 3 when schema covers parameters fully.

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?

Description clearly states what the tool does: finds files with high git churn AND high complexity. The dash construction introduces the specific resource (files) and the criteria, and the second sentence explains why these are risky. This differentiates it from sibling tools like get_hotspot_score, which likely focuses on a single file rather than repository-wide identification.

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

Usage Guidelines3/5

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

The description mentions 'Requires a git repository' as a prerequisite and implies use for identifying riskiest files, but it does not explicitly state when to use this tool versus alternatives like get_hotspot_score or analyze_complexity. No exclusion criteria or when-not-to-use guidance is given, so usage context is implied but not fully specified.

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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