Run a full AI visibility audit on any public URL. Returns schema analysis, AI citation score, E-E-A-T signals, agentic readiness, and prioritized issues with specific fixes.
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | The URL to audit (must be publicly accessible) | |
| focus | No | Audit focus area. Defaults to full. |
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, the description carries the full burden. It implies a non-destructive audit by saying 'Returns...' but does not explicitly mention permissions, rate limits, error handling, or side effects. It provides some transparency about the output content but lacks depth on behavioral traits.
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?
A single, well-structured sentence that front-loads the verb and resource, then summarizes the output categories. No wasted words or redundant information.
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?
No output schema exists, but the description enumerates the main return categories (schema analysis, citation score, E-E-A-T signals, agentic readiness, prioritized issues). It could improve by mentioning that there are sibling tools for specific aspects or by describing response format, but it is adequate for a straightforward audit tool.
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 covers both parameters fully (100% coverage) with clear descriptions for 'url' and 'focus'. The description reinforces the meaning by listing audit areas, but does not add extra semantic detail beyond the schema.
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 action ('Run a full AI visibility audit') and the resource ('any public URL'), and lists the main output categories. The word 'full' helps distinguish it from more focused sibling tools like citerank_check_agentic_readiness or citerank_check_brand_citations.
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?
No explicit 'when to use' or alternative guidance is provided. The description implies use for comprehensive URL audits rather than specific checks or WP page audits, but this is not stated. The distinction from citerank_analyze_wp_page is only implicit via 'any public 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.
