List all pages and posts on your connected WordPress site with their current schema deployment status. Returns each page with its URL, post type, word count, and whether schema has been deployed.
| Name | Required | Description | Default |
|---|---|---|---|
| page | No | Page number for pagination (default 1) | |
| per_page | No | Results per page (default 50, max 100) |
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 burden of behavioral disclosure. It discloses what is returned (URL, post type, word count, schema status) but does not mention pagination behavior, despite the 'all pages' wording and the pagination parameters. This is a notable transparency gap.
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 a single, focused sentence that front-loads the action and resource, then lists the return fields. Every word contributes 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?
The description lists the return fields, which is helpful since there is no output schema. However, it fails to mention that to truly list 'all' pages, the agent must handle pagination (per_page max 100). This omission could lead to incomplete results if the agent makes a single call.
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 coverage is 100% for the two parameters (page, per_page), and the description adds no additional parameter-level meaning. The baseline of 3 is appropriate because the schema already documents the parameters adequately.
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 ('List') and clearly identifies the resource ('all pages and posts on your connected WordPress site') and the distinguishing scope ('with their current schema deployment status'). This distinguishes it from siblings like citerank_analyze_wp_page, which likely targets a single page.
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 implies use when you need an overview of pages/posts and their schema deployment status. It does not explicitly mention alternatives or exclusions, but the context is clear enough for a listing tool. No when-not-to-use guidance is given.
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.
