Scores any URL's AI-search visibility with concrete fixes. $1.50/call via x402.
Scored across 2 tools
audit_geo and audit_geo_lite are clearly distinguished by payment model (paid vs free) and output depth (full evidence vs top-line only). The lite tool is explicitly a subset of the full one, so an agent could occasionally misselect if the user just asks for 'an audit', but the descriptions make the tradeoff explicit.
Both tools use consistent snake_case with a shared audit_geo prefix and a clear lite suffix for the variant. The pattern is predictable and readable.
Two tools for a single-purpose audit service is on the thin side; the lite variant is mostly a convenience wrapper around the same 20 checks. Still, the scope is narrow enough that this does not feel severely mismatched.
The core lifecycle for a GEO audit (triage via lite, full evidence-backed audit) is covered with machine-readable output and prioritized fixes. Minor gaps exist around historical audit retrieval, comparison across URLs, and remediation guidance beyond the top three fixes.
Full GEO (AI-search visibility) readiness audit of a brand/storefront URL. Fetches the site server-side and runs 20 weighted checks across schema.org depth (Organization, Product, Review, FAQPage, aggregateRating), business identity (sameAs, NAP consistency, contact), AI-crawler policy (robots.txt treatment of GPTBot/ClaudeBot/PerplexityBot/CCBot, llms.txt), content extractability (clean text, headings, JS walls, meta description), freshness (sitemap, lastmod, content dates), and authority (about page, credentials, canonical/HTTPS). Returns machine-readable JSON: 0-100 score, A-F grade, per-check pass/partial/fail/unknown with evidence, and the top 3 prioritized fixes. No LLM is queried per call — deterministic checks only. Cost: $1.50 USDC on Base per call, paid via x402 — the caller's agent pays from its owner's approved wallet; no API key needed. Use this to audit a client's site for AI-search visibility, or to generate the audit behind a GEO report for a brand owner.
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | Brand/storefront URL to audit, e.g. https://example-brand.com |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden and does so richly: server-side fetch, deterministic checks with 'No LLM is queried per call', the $1.50 USDC x402 payment flow, and the fact that no API key is needed. Auth/payment requirements, cost, and computation model are all disclosed.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Front-loaded with purpose and output, then the check taxonomy. Dense but nearly every clause earns its place by telling the agent what is inspected; the parenthetical taxonomy is long, which slightly dilutes scannability.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
No output schema exists, so the description must explain return values — and it does: 0-100 score, A-F grade, per-check pass/partial/fail/unknown with evidence, top 3 fixes. Combined with cost/payment details, nothing needed to invoke it correctly is missing.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
One parameter with 100% schema coverage, so the schema already documents 'url'. The description adds only the framing 'brand/storefront URL', which matches the schema description rather than extending it. Baseline 3 applies when the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
States a precise verb+resource+scope: 'Full GEO (AI-search visibility) readiness audit of a brand/storefront URL.' The word 'Full' plus the enumerated 20-check breakdown implicitly separates it from the sibling audit_geo_lite, so an agent can tell them apart without opening either schema.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Gives concrete when-to-use cases ('audit a client's site for AI-search visibility', 'generate the audit behind a GEO report'). It never explicitly names when-not-to-use or routes the agent to audit_geo_lite, leaving the full-vs-lite decision to be inferred from the '20 weighted checks' detail.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
FREE lite GEO readiness audit of a brand/storefront URL. Runs the same 20 checks as audit_geo but returns top-line results only: 0-100 score, A-F grade, check counts, and the titles of the top gaps — no per-check evidence, no detailed recommendations. Use this to triage whether a site needs the full audit_geo report, or call audit_geo directly for the complete evidence-backed audit.
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | Brand/storefront URL to audit, e.g. https://example-brand.com |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the burden and does so well: it discloses that it runs the same 20 checks as audit_geo, that it is free, and exactly what it withholds (no per-check evidence, no detailed recommendations). It does not mention rate limits, auth, or latency, which keeps it short of 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.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, front-loaded with what the tool does before the routing guidance, and the enumeration of returned fields is compact. The field list is slightly dense but each element earns its place by defining the output contract.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
No output schema exists, and the description fully compensates by enumerating the return shape (score, grade, check counts, top gap titles) and the explicit omissions. For a one-parameter read tool, nothing an agent needs to call it correctly is missing.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100% and there is a single required parameter whose semantics the schema already documents with an example. The description adds only the framing 'brand/storefront URL', which mirrors rather than extends the schema, so the baseline 3 applies.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
States a specific verb and resource ('GEO readiness audit of a brand/storefront URL') and distinguishes itself precisely from its sibling by naming audit_geo and describing the reduced output. An agent can tell the two tools apart without opening either schema.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly says to use this to triage whether a site needs the full report, and to call audit_geo directly for the complete evidence-backed audit. The selection condition between the two siblings is unambiguous.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
audit_geoaudit_geo_liteAudit any site's AI visibility from your assistant: crawler access, rendering, and schema.
Scan any URL for on-page, technical & content SEO; 0-100 score with copy-paste fixes.
Audit any website for AI visibility: graded report, findings with fixes, AI crawler access check.
Grades any storefront URL A-F on 20 weighted conversion checks. $1.00/call via x402.
Add one secure layer between your agents and this server.
