Get the verified fact record for a company by domain from the Grounded AEO catalog. Returns the published (approved) facts with verification state and confidence: the data an AI agent should ground answers about this business on.
| Name | Required | Description | Default |
|---|---|---|---|
| domain | Yes | Company domain, e.g. acme.com |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the transparency burden. It discloses that only published (approved) facts are returned and includes verification state and confidence, which is useful behavioral context. However, it does not mention potential errors, permissions, or rate limits, though the operation is a simple read.
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, front-loaded with the core operation, and every sentence adds value. It avoids fluff and clearly communicates both the action and the returned data without unnecessary detail.
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 single-parameter read tool with no output schema, the description adequately explains the returned content (published facts, verification state, confidence) and the intended grounding use case. It does not cover error handling or pagination, but these are likely low-risk for this simple operation.
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 schema already covers the sole parameter 'domain' with a clear description and example, achieving 100% schema coverage. The description adds only that lookup is by domain, which is redundant with the schema, so no additional semantic value is provided.
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 ('Get') and resource ('verified fact record for a company by domain') with a clear source ('Grounded AEO catalog'). It distinguishes the tool from siblings like get_fact by emphasizing the company-level fact record scope and verified/approved nature.
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 provides clear context for when to use the tool: when an AI agent needs grounded, verified facts about a business. It does not explicitly name alternatives or exclusion criteria, but the stated use case implicitly differentiates it from search or citation tools.
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.
Each tool targets a distinct resource or action: checking AEO records, reading SERP data, generating records, retrieving facts, citations, competitor snapshots, engine visibility, keyword gaps, etc. Even similar-sounding tools like get_citations and get_cited_domains are clearly delineated by their descriptions. No two tools appear to overlap in purpose.
All tool names follow a consistent verb_noun convention using snake_case: check_*, get_*, generate_*, propose_*, run_*, search_*. There is no mixing of casing or verb styles, and each name instantly communicates the action and subject.
With 14 tools, the server is well-scoped for the domain of AI citation optimization. Each tool contributes a unique capability, and the count is neither sparse nor bloated. The number fits comfortably within the ideal 3-15 range for a focused server.
The tool surface covers the core lifecycle: checking existing records, generating new records, auditing and fixing sites, retrieving data, analyzing gaps, and proposing briefs. Minor gaps exist such as no direct tool for claiming a domain or managing tracked keywords/competitors, but those may be external to the MCP server. Overall, the set supports the intended workflows well.