Generate production-ready JSON-LD structured data for any URL. Analyzes page content and produces schema markup ready to implement.
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | The URL to generate schema for | |
| schemaType | No | Preferred schema type (e.g. Article, LocalBusiness, Product, FAQPage). Auto-detected if omitted. |
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 must carry the full burden. It discloses that the tool analyzes page content and produces ready-to-implement markup, suggesting a read-only generation operation. However, it does not mention authentication needs, rate limits, or any potential side effects, leaving important behavioral gaps.
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, front-loads the core action, and contains no redundant information. Every word contributes to understanding the tool's purpose and output.
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 tool has no output schema and no annotations, and the description only gives a high-level outcome ('schema markup ready to implement'). Given the simple two-parameter schema, this is minimally adequate, but it lacks detail on return structure, error behavior, or auto-detection nuances, making it incomplete for a more complex scenario.
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 has 100% coverage for both parameters ('url' and 'schemaType'), so the schema already documents them fully. The description adds general context like 'any URL' and 'schema markup' but does not provide parameter-specific details beyond what the schema already offers, meeting the baseline of 3.
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 'Generate' and a clear resource 'JSON-LD structured data for any URL,' making the tool's function explicit. It also distinguishes itself from sibling tools like citerank_analyze_url and citerank_deploy_schema by focusing on schema generation rather than analysis or deployment.
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 the tool should be used when schema markup is needed for a URL, but it does not explicitly state when to use it over alternatives such as citerank_analyze_url or citerank_deploy_schema. No exclusions, prerequisites, or alternative tool references are provided.
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.
