Citation Intelligence MCP

audit_structured_data

Read-onlyIdempotent

Suggest missing JSON-LD schema for a URL by detecting existing types and returning ready-to-paste templates for missing but signalled types like BlogPosting, FAQPage, or HowTo.

Instructions

Suggest missing JSON-LD additions for a URL. Fetches the page, detects existing schema types, and returns ready-to-paste templates for types that are missing but signalled by page content (BlogPosting from og:type=article or bylines, FAQPage from Q&A pairs, HowTo from numbered steps, BreadcrumbList from nested paths, Organization on homepages). Templates are pre-filled from page metadata where possible; fields marked FILL: require manual completion.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`url`	Yes	URL to inspect for missing JSON-LD. The page is fetched and its content signals are used to suggest schema types.

Output Schema

TableJSON Schema

Name	Required	Description
`url`	Yes	URL that was inspected.
`note`	No
`summary`	Yes
`fetched_at`	Yes	UTC ISO-8601 timestamp.
`suggestions`	Yes	Schema additions suggested for this page.
`signals_detected`	Yes	Content signals detected (e.g. og:type=article).
`schema_types_present`	Yes	@type values already present on the page.

Schema Changelog

Changes observed during successful MCP inspections.

Addedv0.1.2Oct 2, 2026

TDQS

A3.7/5.0

Behavior4/5

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

Annotations already cover readOnly, idempotent, openWorld and non-destructive. The description adds genuinely useful behavior beyond that: it fetches the page, detects existing types, returns ready-to-paste templates, pre-fills from metadata, and marks incomplete fields with FILL:. That return-shape and convention context is valuable.

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?

Front-loaded with a one-line purpose, followed by useful specifics. The parenthetical signal-to-type mapping is dense but each example earns its place by clarifying detection behavior.

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 an output schema present and annotations carrying the safety profile, the description need only cover purpose and behavior, which it does. The only residual gap is the unstated relationship to the sibling audit_schema.

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% and there is a single url parameter already documented in the schema, so the description adds little parameter-level meaning beyond noting the page is fetched. Baseline 3 is appropriate.

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 specific verb (suggest) and resource (missing JSON-LD additions) with clear scope, and enumerates exactly which types it can propose from which signals. It does not explicitly distinguish itself from the sibling audit_schema, which likely inspects existing schema markup, so an agent must infer the boundary.

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?

The description implies when the tool is useful (page is missing structured data but content signals suggest types), but gives no explicit when-to-use/when-not or naming of alternatives like audit_schema. Usage is left to inference.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.