Check whether a brand/domain is cited in AI-generated answers (Google AI Overviews, Gemini). Returns citation rate, share of voice, top cited queries, and competitor sources.
| Name | Required | Description | Default |
|---|---|---|---|
| domain | Yes | The domain to check (e.g. example.com) | |
| keywords | No | Optional keywords to test (max 10). Auto-derived from domain 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, the description carries the full burden. 'Check' implies a read-only operation, and the list of returned metrics gives some behavioral insight. However, it does not disclose potential rate limits, data freshness, or whether the tool performs live lookups, leaving moderate 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 concise sentences. The first states the core purpose, and the second lists key outputs. No wasted words or redundancy.
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?
For a tool with two parameters and no output schema, the description clearly explains the purpose and return values. The schema fills in parameter details, making the tool well-specified overall. Minor gaps like auto-derived keyword behavior are covered by the schema, not the description.
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 covers both parameters with descriptions, and schema coverage is 100%. The description adds no additional parameter-level context beyond the schema, so it defaults to the baseline 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 ('Check') and resource ('brand/domain cited in AI-generated answers'), and clearly lists return metrics. It differentiates from siblings like citerank_analyze_url by focusing on brand citation presence rather than URL-specific analysis.
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 context is clear: use this to check brand citations in AI overviews and Gemini. There is no explicit mention of alternatives or when not to use it, but the purpose is narrow enough that the intended use case is evident. The lack of explicit sibling comparisons prevents a 5.
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.
