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AgentsReady

Get the fix prompt for a site

get_fixes
Read-only

Return a ready-to-run task list for a coding agent that fixes every failed agent-readiness and AI-search check on the site (for example adding llms.txt, unblocking AI crawlers, adding structured data), biggest first, with how to verify each change. Same scan as check_site.

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": {
      "domain": "<domain>"
    },
    "name": "get_fixes"
  }
}

Input Schema

NameRequiredDescriptionDefault
jobNoWhat agents should do on the site: recommend (answer questions about it), buy, signup, api (get an API key) or personal (a person's own site: who they are, their work, how to reach them). Leave out to detect it.
domainYesThe site to check, like example.com

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.6/5.0
Behavior3/5

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

readOnlyHint=true and openWorldHint=true already declare a safe, network-touching read, so the description's main added value is the shape of the response: ordered biggest-impact-first and accompanied by verification steps for each change. It does not disclose what happens when there are zero failures, whether the underlying scan is re-executed or cached, or any rate/scan-cost considerations.

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?

A single dense sentence, front-loaded with the outcome (ready-to-run task list for a coding agent) and followed by the useful qualifiers: scope, illustrative fix types, ordering, and verification. Zero filler.

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 properly compensates by describing the returned artifact — an ordered remediation list with verification guidance. Minor gaps remain around the effect of the optional `job` parameter and the empty-result case, but an agent can invoke it correctly as written.

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 coverage is 100%, so both `domain` and the `job` enum (recommend/buy/signup/api/personal) are fully documented in the schema itself. The description adds nothing about either parameter — notably it never explains how the `job` classification changes which fixes are produced — so the baseline of 3 applies.

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?

States a concrete verb and deliverable: a ready-to-run task list for a coding agent that fixes failed agent-readiness and AI-search checks, with parenthetical examples (llms.txt, unblocking AI crawlers, structured data). The closing note 'Same scan as check_site' relates it to the sibling but only hints at the division of labor (results vs. remediation) rather than stating it clearly.

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?

Usage is implied: reach for this when you want remediation steps rather than raw check results, since it shares the scan with check_site. There is no explicit when-to-use, when-not-to-use, or prerequisite (e.g., that a prior scan exists or costs a scan) statement, so the agent must infer the routing.

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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