Specky

Generate Metrics Dashboard

sdd_metrics

Idempotent

Produce a metrics dashboard from specification and analysis files to show requirement count, task coverage, compliance score, checklist pass rate, and phase timeline.

Instructions

Generate a self-contained HTML metrics dashboard for a feature. Reads SPECIFICATION.md, ANALYSIS.md, VERIFICATION.md, CHECKLIST.md and .sdd-state.json. Produces metrics-dashboard.html with: requirement count, task coverage, compliance score, checklist pass rate, and a phase timeline with durations.

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": "sdd_metrics"
  }
}

Input Schema

Name	Required	Description	Default
`spec_dir`	No	Spec directory path (relative to workspace root)	.specs
`feature_number`	No	Feature number (zero-padded, e.g. '001')	001

Schema Changelog

Changes observed during successful MCP inspections.

First observedv3.4.0-rc.14May 3, 2026

TDQS

A4.2/5.0

Behavior4/5

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

Annotations already indicate idempotentHint=true and not destructive. Description adds concrete details about reading specific files and producing a dashboard, which aligns with annotations. No contradictions. It goes beyond annotations by listing exact files read and metrics generated.

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?

Description is a single dense paragraph that conveys all necessary information without fluff. Every sentence adds value, and the key points are front-loaded.

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?

Given the simple input schema and presence of annotations, the description adequately covers the tool's function. It could mention that the required files must exist, but overall it is complete enough for an AI agent to use correctly.

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% for both parameters. The tool description does not add significant semantic meaning beyond what the schema already provides. 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?

Description clearly states it generates a self-contained HTML metrics dashboard for a feature, specifying the exact files read and the metrics computed. This is highly specific and distinguishes it from sibling tools, none of which produce dashboards.

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?

Description clearly explains the inputs and output, making it easy to understand when to invoke. However, it does not provide explicit when-not-to-use or alternative tool recommendations, though siblings are quite distinct.

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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