tactual-mcp

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calibration_report

Compare screen-reader navigation observations against saved Tactual analysis data to generate calibration scores and detect announcement drift.

Instructions

Run a calibration dataset against saved full Tactual analysis JSON and return structured scoring signals. Use this after analyze_url --full-json/format=json artifacts or VM/manual screen-reader observations have been collected. Read-only; file inputs must be inside the current working directory. Returns JSON by default so agents can inspect scoringSignals, announcement drift, and per-target calibration errors.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "datasetPath": "<datasetPath>"
    },
    "name": "calibration_report"
  }
}

Input Schema

Name	Required	Description	Default
`format`	No	Output format. JSON is best for agent workflows; markdown is for human review.	json
`analysisDir`	No	Directory of full analysis JSON files, within the current working directory
`datasetPath`	Yes	Path to calibration dataset JSON, within the current working directory
`allowMissing`	No	Allow observations whose URLs have no matching analysis JSON
`analysisPaths`	No	Full analysis JSON files produced by analyze_url/analyze-url

Schema Changelog

Changes observed during successful MCP inspections.

AddedJun 22, 2026

TDQS

A4.4/5.0

Behavior5/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool is 'Read-only' and describes return values: 'scoringSignals, announcement drift, and per-target calibration errors.' It also mentions file location constraints. This fully informs the agent of behavior beyond the schema.

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 consists of three well-organized sentences: purpose, usage context, and output details. Every sentence adds value without redundancy. It is front-loaded, allowing quick comprehension.

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 tool with five parameters, one required, and no output schema, the description adequately covers inputs, constraints, and output structure. It mentions specific return fields (scoringSignals, etc.) but does not detail the full output. Given the complexity, it is sufficiently complete for agents to understand the tool's role and usage.

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 input schema has 100% description coverage, meaning the schema already explains each parameter. The description adds minimal extra semantics, reinforcing that file inputs must be in the current working directory and noting that JSON is best for agents. This meets the baseline for high schema coverage but does not significantly enhance parameter understanding.

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 the tool's purpose: 'Run a calibration dataset against saved full Tactual analysis JSON and return structured scoring signals.' The verb 'run' and specific resources (calibration dataset, analysis JSON) make the action and target unambiguous. It distinguishes from sibling tools like analyze_url and diff_results by focusing on calibration, a different workflow stage.

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?

The description explicitly says 'Use this after analyze_url --full-json/format=json artifacts or VM/manual screen-reader observations have been collected.' It also notes that inputs must be inside the current working directory, providing clear usage context. It does not explicitly list alternative tools or when not to use, but the guidance is specific and actionable.

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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