feature-tracker-mcp

gap_scan

Reveals missing goals by reading every recorded objective, done-means, non-goal, constraint, and settled refusal, exposing coverage gaps that feature derivation inside existing goals cannot find.

Instructions

Everything needed to reason about coverage, in one read: every goal's objective, done-means, non-goals and constraints in its own words, what each holds, and every settled refusal. Read-only. This is the input to gap analysis — derive_features asks what ONE goal implies and so can only find work inside a goal somebody already wrote; this asks what the whole set fails to cover, which is the only way a MISSING goal is ever found. Returned unsummarised on purpose: a gap is noticed by reading the actual wording, and does not survive being condensed into counts.

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": "gap_scan"
  }
}

Input Schema

Name	Required	Description	Default
`cwd`	No	Directory identifying the project and the tree. Defaults to this session's working directory, which in a git worktree is that worktree.

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Aug 30, 2026

TDQS

A4.3/5.0

Behavior4/5

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

There are no annotations, so the description carries the full burden of behavioral disclosure. It explicitly states 'Read-only' and explains that results are returned unsummarised on purpose, with the rationale that gaps are noticed by reading actual wording and do not survive condensation into counts. It does not mention volume, size, pagination, or output envelope behavior, but the disclosed traits are meaningful and beyond trivial.

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 longer than a one-liner but every clause earns its place: it defines content, declares read-only behavior, differentiates from a sibling, and justifies unsummarised output. The core content is front-loaded with 'Everything needed to reason about coverage, in one read,' and the rest supports selection and expectations.

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?

With no output schema, the description does a good job explaining what will be returned and why, which covers the most important contextual gap. It also gives usage context through the derive_features contrast. It could more explicitly mention response shape or size expectations, but for a read-only scan tool the definition is largely complete.

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?

The single parameter cwd is already fully documented in the input schema with 100% schema description coverage, so the description does not need to repeat it. The description adds no further parameter-level guidance, which is acceptable given the schema already handles the meaning. Baseline 3 is appropriate.

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 clearly states what the tool does: it returns a full, unsummarised read of all goals' objectives, done-means, non-goals, constraints, holdings, and refusals for coverage reasoning. It also explicitly distinguishes itself from derive_features by contrasting whole-set coverage analysis with single-goal implication. This gives a precise verb-resource-scope identity, not just a restatement of the name.

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

Usage Guidelines5/5

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

The description explicitly says when to use this tool vs derive_features: use gap_scan to analyze what the whole set fails to cover and find MISSING goals, whereas derive_features can only find work inside goals someone already wrote. This is a clear when/alternative pairing, leaving no ambiguity about selection.

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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