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align_stale_code

Read-only

Lists code in the current repository still built on superseded decisions, with line counts and ranges per file, to identify stale implementations. Optionally include AI-suggested, unconfirmed links.

Instructions

List code in the current repository still built on a decision that a later decision replaced, with line counts and ranges per file. Only links a person confirmed, unless include_ai is true. Run it inside the repository; needs full git history.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "align_stale_code"
  }
}

Input Schema

NameRequiredDescriptionDefault
include_aiNoAlso use AI-suggested supersedes links nobody confirmed (labelled in the result)

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.8/5.0
Behavior4/5

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

Annotations already establish the read-only, non-destructive profile, and the description adds meaningful context on top: the default is human-confirmed links only, AI links appear only when include_ai is set, and the call depends on full git history. It does not mention failure behavior in shallow clones or result ordering, which are the remaining gaps.

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?

Three sentences, front-loaded with what the tool returns, then the filtering default, then the execution prerequisite. No padding or repetition.

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 one-parameter read-only tool with no output schema, the description covers the essential ground: what is returned (line counts and ranges per file), how links are filtered, and the environment requirement. Only the labeling details of AI-suggested results and any pagination or size limits are left implicit.

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 the single parameter is fully documented in the schema, so the baseline applies. The description does add value by stating the default explicitly ('Only links a person confirmed'), clarifying what happens when include_ai is omitted, but it does not go beyond that.

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?

The description gives a specific verb ('List') and a precisely scoped resource: code still built on a decision that a later decision replaced, with line counts and ranges per file. That scope is narrow enough that an agent can tell it apart from general checks like align_check_drift or align_check_alignment, though no sibling is named explicitly to reinforce the boundary.

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?

It supplies real preconditions — must be run inside the repository and needs full git history — which is useful execution guidance. It stops short of when-to-use/when-not guidance relative to the alignment-check siblings, so context is implied rather than stated.

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