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identify_tech_debt

Analyze a codebase to inventory technical debt and propose a refactoring strategy with impact, roadmap, and quick wins.

Instructions

Identify technical debt in a codebase and propose a refactoring strategy. Returns a structured prompt covering debt inventory, business impact, refactor roadmap, and quick wins.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
hotspotsNoKnown problem modules / packages, if any.
symptomsNoSymptoms attributed to debt: slow PRs, on-call pain, flaky tests, regressions.
codebaseSummaryYesHigh-level summary of the codebase: structure, age, languages, scale.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.1

TDQS

B3.4/5.0
Behavior3/5

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

With no annotations, the description carries the full behavioral burden. It does valuable work by disclosing that the return value is a 'structured prompt' covering four named sections (rather than a finished analysis), which is important and not in any schema. However, it omits any detail about permissions, execution cost, or how the four sections are framed.

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?

Two tight sentences with no filler: the first states purpose, the second enumerates the return structure. The function is front-loaded and everything present earns its place.

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?

There is no output schema, so the description must characterize the return, and it does so by naming the four sections of the produced prompt. Combined with the fully documented inputs, this is nearly complete, though it stops short of explaining how the four sections relate or what an agent should do with the returned prompt.

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%, so all three parameters (hotspots, symptoms, codebaseSummary) are already documented in the schema. The description adds no parameter-level syntax, format, or interaction detail beyond what the schema provides, so the baseline of 3 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb and resource ('Identify technical debt in a codebase') plus the secondary action ('propose a refactoring strategy'). It does not explicitly distinguish itself from nearby siblings like review_code or review_architecture, but the purpose is unambiguous without opening the schema.

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

Usage Guidelines2/5

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

The description never says when to reach for this tool versus alternatives such as review_code, review_architecture, or recommend_architecture, nor does it state any prerequisites or exclusions. Usage must be inferred entirely from the tool name and context.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.