Search Scope AI Visibility Auditor

Extract site entity map

extract_site_entities

Read-onlyIdempotent

Build a practical entity map for a public website from a small sample of its pages: the primary entity (brand or organisation), related entities grouped into people, organisation/brand, products, services, locations and topics, explicit relationships (Brand -> offers -> Service, Brand -> located in -> City, Person -> founder of -> Brand), ambiguities, missing entity support, schema.org types found and schema types worth adding. Use it when a user asks what entities a site establishes, whether the brand, people, services and locations are stated clearly enough for search engines and AI assistants, or which schema types fit the business. It does NOT query Google's Knowledge Graph, Wikidata or any external entity database, and it does not hallucinate: every entity carries a confidence level and the source it was detected from. Low-confidence items are pattern matches on visible copy that a human should verify. Parameters: url (required), maxPages (optional, default 8, max 15). Faster and narrower than audit_ai_visibility; use the audit when you need recommendations beyond entities.

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": {
      "url": "<url>"
    },
    "name": "extract_site_entities"
  }
}

Input Schema

Name	Required	Description	Default
`url`	Yes	Public website URL to audit, e.g. https://example.com.au. Bare domains are accepted (https is assumed). Private, loopback, internal and IP-literal hosts are rejected.
`maxPages`	No	Maximum pages to sample, including the homepage. Default 8, hard maximum 15. Pages are prioritised: homepage, about, services/products, pricing, locations, key informational pages.

Output Schema

Name	Required	Description	Default
`target`	Yes
`ambiguities`	Yes
`pagesSampled`	Yes
`primaryEntity`	Yes
`relationships`	Yes
`missingSupport`	Yes
`relatedEntities`	Yes
`schemaTypesFound`	Yes
`suggestedSchemaTypes`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedNov 14, 2026

TDQS

A4.6/5.0

Behavior5/5

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

Annotations already declare readOnly/openWorld/idempotent/non-destructive, so the safety profile is free; the description adds substantive behavior beyond that — sampling-based scope ('a small sample of its pages'), confidence levels and source attribution on every entity, and an explicit warning that low-confidence items are pattern matches a human should verify. The no-hallucination commitment and the relative speed/narrowness versus audit_ai_visibility are exactly the kind of traits annotations cannot express.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Dense but front-loaded: what it produces comes first, then when to use it, then exclusions, then parameters. Every clause adds information, though the closing parameter restatement and the 'faster and narrower' phrasing slightly repeat ground already implied by the audit comparison. Long single paragraph but no filler sentences.

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?

An output schema exists, so return values need not be explained, and the input schema is fully documented; annotations cover the safety profile. Against that backdrop the description supplies the remaining needed context — sampling limitations, confidence semantics, verification caveat, and sibling routing — leaving no material gap for an agent choosing or calling the tool.

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 description coverage is 100%, including URL host restrictions and the prioritised page ordering, so the schema carries the parameter burden and baseline 3 applies. The description only echoes 'url (required), maxPages (optional, default 8, max 15)', which duplicates structured data without adding format or selection advice beyond what the schema states.

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 ('Build a practical entity map for a public website') and enumerates the exact contents of the map: primary entity, grouped related entities, explicit relationships, ambiguities, missing support, schema.org types. It also names the neighbouring tool it is narrower than (audit_ai_visibility), so an agent can distinguish the two 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.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Gives three concrete trigger questions (what entities a site establishes, whether brand/people/services/locations are clear enough for search engines, which schema types fit the business) plus explicit exclusions ('does NOT query Google's Knowledge Graph, Wikidata or any external entity database') and a named alternative with the condition that selects it ('use the audit when you need recommendations beyond entities'). When, when-not and alternative are all covered.

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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