Checks whether a website is readable and citable by AI systems (ChatGPT, Claude, Perplexity, etc.)
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Average 4.1/5 across 2 of 2 tools scored.
The two tools are clearly distinct in focus: geo_scan covers broad AI visibility checks while local_business_check targets a specific structured data type. However, there is minor overlap because geo_scan also evaluates Schema.org structured data, which could confuse an agent trying to pick the more specific tool.
Both names use a compound pattern (e.g., 'geo_scan' and 'local_business_check'), but the verb placement differs: one ends with 'scan' and the other ends with 'check'. While readable, the inconsistency in verb choice and word order prevents a strong naming convention.
With only two tools, the server feels thin, though it is focused on a specialized domain. The count is borderline and might be more appropriate if additional audit tools were included, but it is still reasonable for a narrow scanner.
The tools cover the primary GEO scanning and a key local business structured data check, which are clear gaps in many similar servers. However, other schema types and any fix/action capabilities are missing, so the surface is not fully comprehensive.
geo_scanAInspectChecks whether a website is readable and citable by AI systems (ChatGPT, Claude, Perplexity, Google AI Overviews). Returns a 0-100 score across checks like llms.txt, Schema.org structured data, AI bots in robots.txt, content freshness, and heading structure — each with a concrete fix. Use whenever someone asks to check, audit, or improve a website's AI/GEO visibility.
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | Full URL of the website to check, including https:// |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden. It discloses that the tool returns a 0-100 score, lists the types of checks performed, and notes that each check comes with a concrete fix. This gives the agent a realistic expectation of input and output, though it does not describe edge cases like invalid URLs or rate limits.
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?
The description is two sentences, front-loaded with the core purpose, and every phrase adds value. The first sentence explains function and output; the second provides usage guidance. No redundant or extraneous information is present.
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?
Given the simple one-parameter schema and no output schema, the description adequately covers what the tool does, how to invoke it, and what to expect in return (score range and per-check fixes). It is not exhaustive about return structure, but for a simple scan tool, it is sufficiently complete for an agent to select and call it correctly.
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?
The schema already provides 100% coverage for the single parameter (url) with a clear description ('Full URL of the website to check, including https://'). The description does not add additional parameter-specific semantics, so the baseline of 3 is appropriate.
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?
The description states a specific verb ('Checks') and a clear resource ('website readability and citability by AI systems'), and enumerates specific checks (llms.txt, Schema.org, robots.txt, content freshness, heading structure). This clearly distinguishes it from the sibling tool 'local_business_check', which focuses on local business data.
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?
The description explicitly says 'Use whenever someone asks to check, audit, or improve a website's AI/GEO visibility,' giving clear when-to-use conditions. It does not explicitly mention the sibling tool as an alternative or provide when-not-to-use exclusions, but the differentiation is implied by the tool's focus.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
local_business_checkAInspectChecks a website for LocalBusiness Schema.org markup: required fields for Google Rich Results (name, image, address), recommended fields (telephone, opening hours, price range, geo coordinates, ratings), and type-correctness of nested properties (e.g. address must be a PostalAddress). Use whenever someone asks to check, audit, or improve a local business website's structured data / Rich Results eligibility.
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | Full URL of the business website to check, including https:// |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It clearly states the tool checks (implying read-only) and details the scope (required and recommended fields, type correctness), but it does not explicitly state that it does not modify the website, nor does it describe the output format or any network behavior like authentication or rate limits.
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?
The description is two sentences, front-loaded with the primary action, and densely packs useful details (field lists, type-correctness example, and usage trigger). Every clause earns its place without redundancy.
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?
The tool is simple (one parameter, no nested objects), and the description adequately covers purpose and usage. However, because there is no output schema, the description should have hinted at what the agent can expect (e.g., a report of missing/invalid fields), and it does not. Minor limitations (e.g., homepage vs. full site) are also absent.
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 the single 'url' parameter is already fully documented in the schema (including format 'including https://'). The description adds no additional parameter context, which is fine given the high schema coverage.
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?
The description uses a specific verb ('Checks') and resource ('website for LocalBusiness Schema.org markup'), and lists the exact fields and type-correctness checks. This clearly distinguishes it from the sibling tool geo_scan, which likely focuses on geo-location scanning rather than structured data validation.
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?
The description provides explicit guidance: 'Use whenever someone asks to check, audit, or improve a local business website's structured data / Rich Results eligibility.' It does not, however, mention when not to use it or point to alternatives like geo_scan, so it stops short of full exclusionary guidance.
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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