Analyze a specific page on your connected WordPress site. Returns the recommended schema type with confidence + reason, extracted signals (phone, address, price, FAQ pairs, WooCommerce data), author info, and full content for schema generation.
| Name | Required | Description | Default |
|---|---|---|---|
| post_id | Yes | The WordPress post ID to analyze |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It does well by listing what the tool returns: recommended schema type with confidence and reason, extracted signals (phone, address, price, FAQ pairs, WooCommerce data), author info, and full content. It implies a read-only analysis operation, though it does not explicitly state side effects or error handling.
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 long, with the first sentence stating purpose and the second enumerating outputs. It is front-loaded, concise, and every word adds value with no redundancy or filler.
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 one parameter, no output schema, and no nested objects, the description is quite complete. It explains the purpose, the specific WordPress context, and lists all key return values in detail. It lacks explicit mention of limitations or error conditions, but for such a focused analysis tool, it covers the essentials well.
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 input schema already fully describes the single parameter (post_id) with 100% coverage. The description adds little beyond 'specific page' context, and does not enrich the understanding of the parameter beyond what the schema provides. Baseline 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 clearly states the tool analyzes a specific WordPress page, using a specific verb ('Analyze') and resource ('page on your connected WordPress site'). It distinguishes from siblings by specifying the WordPress context and enumerating concrete outputs (schema type, signals, author info, content), making the purpose unmistakable.
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 gives clear context for when to use this tool: when you need to analyze a page on your connected WordPress site to generate schema. It does not explicitly name alternatives or exclusions, but the 'specific page on your connected WordPress site' provides enough context to differentiate from sibling tools like citerank_analyze_url.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Most tools are clearly distinct by target resource and action (analyze vs deploy vs check vs simulate). Minor overlap exists between analyze_url and analyze_wp_page, and between get_wp_health and list_wp_pages, but descriptions clarify their specific intents.
All tools follow a consistent 'citerank_' + verb_noun pattern (analyze_url, check_brand_citations, deploy_schema, simulate_agent_journey). No mixed conventions or vague verbs.
Nine tools fit the domain of AI visibility and schema management well. Each tool maps to a specific workflow step, and the count is neither too sparse nor overwhelming.
The server covers the core lifecycle: analyze, generate, deploy, check health, and simulate. Missing explicit update/delete schema operations, but the health check and deploy log mitigate this, making the surface reasonably complete for its purpose.
