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AnswerRail AEO, Schema.org & /llms.txt Audit MCP

by io.github.quitegoodproject
OverviewDiscussionsAdmin

Audit Local Codebase AI Readiness & Manifest Structure

audit_local_codebase
Read-onlyIdempotent

Inspects a local project directory on disk (checking package.json, public/robots.txt, public/llms.txt, and public/.well-known/ai-catalog.json) and returns a Markdown compliance report with per-file pass/fail status, a 6-line /llms.txt preview, and remediation links. Use this tool only when auditing local source files inside an IDE workspace; do not use it to scan a live HTTPS website (use audit_ai_readiness instead) or to synthesize manifests from a remote domain (use generate_ai_manifests instead). If targetDir contains a public/ subfolder, discovery files are checked inside public/ first before falling back to targetDir root. Performs a 100% read-only local check with zero file writes, requires no API key, has no rate limit, and returns an error message if targetDir is unreadable.

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": {
      "targetDir": "<targetDir>"
    },
    "name": "audit_local_codebase"
  }
}

Input Schema

NameRequiredDescriptionDefault
targetDirYesRelative or absolute local workspace directory path to inspect (e.g., "./", ".", or "/workspace/app"). Must point to a readable project folder.
includePreviewNoOptional flag (defaults to true). When true, appends a 6-line preview of the synthesized /llms.txt index derived from package.json metadata; when false, returns only the structural pass/fail checklist.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A4.7/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, destructiveHint=false, idempotentHint and closed-world, yet the description still adds real context: no API key, no rate limit, error on unreadable targetDir, and the public/-first discovery fallback. The '100% read-only, zero file writes' sentence partly restates the annotations, which keeps this below a 5.

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?

One dense paragraph, front-loaded with purpose then exclusions then behavioral facts; every sentence carries routing or operational information and nothing is padding beyond the minor annotation restatement.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With no output schema, the description still specifies the return artifact (Markdown report with per-file pass/fail, 6-line preview, remediation links) and error behavior, which is everything an agent needs to call and interpret it.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so baseline is 3, but the description adds genuine semantics beyond the schema: targetDir resolution order (public/ subfolder checked first, then root fallback) and that the preview is derived from package.json metadata. includePreview is only touched via the preview sentence already in the schema, so meaning added is meaningful but not exhaustive.

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?

States a specific verb and resource ('Inspects a local project directory on disk') and enumerates exactly which files are checked (package.json, robots.txt, llms.txt, ai-catalog.json) plus the return shape. It also names the two siblings it is not (audit_ai_readiness, generate_ai_manifests), so an agent can select it without opening a schema.

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?

Explicit when-to-use ('only when auditing local source files inside an IDE workspace') and two explicit when-not cases each routed to a named alternative. No inference is required to pick the right tool.

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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