qualify_domainJudge whether a domain is worth running link building for at all, from live search data. Returns a verdict, the measured signals behind it, and the honest case against. This is the same free diagnostic the dashboard runs, and for a large share of sites the answer is that the niche has nothing to mine.
| Name | Required | Description | Default |
|---|---|---|---|
| domain | Yes | The domain to judge, for example mentionry.com |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the burden of behavioral disclosure. It states that the tool uses live search data, returns a verdict with measured signals and the honest case against, and is the same free diagnostic as the dashboard. This gives a clear picture of the tool's behavior and output without relying on structured metadata.
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?
Three sentences, each earning its place: the first states the core purpose, the second describes the output, and the third provides useful expectation-setting. No redundant or filler language is present.
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 one-parameter diagnostic tool with no output schema, the description is complete. It covers the purpose, data source, output shape, and the likely outcome, giving an agent enough context to invoke the tool appropriately and interpret its result.
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 fully documents the single parameter with a descriptive example ('mentionry.com'), so schema coverage is 100%. The description adds no additional parameter-level meaning, which keeps this at 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 opens with a specific verb and resource: 'Judge whether a domain is worth running link building for at all'. It clearly distinguishes the tool from sibling tools like find_prospects or backlink_gap by focusing on the qualification verdict rather than prospect discovery or gap 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 description clearly frames when to use the tool—before investing in link building for a domain—and sets expectations by noting that many sites will not be worth pursuing. It does not explicitly name alternative sibling tools or state when not to use it, so it stops short of 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.
Each tool targets a distinct resource or action: agents, documents, prospects, competitors, visibility, and link checks are clearly separated. Even the closely related link-building tools are differentiated by whether they find prospects, find competitor gaps, or verify existing links.
Almost every tool follows a clear verb_noun pattern using lowercase snake_case, such as list_agents, run_agent, and verify_links. The one minor deviation is backlink_gap, which reads as a noun phrase rather than an imperative verb, so the pattern is not perfect.
Fourteen tools is within the ideal range and each tool covers a meaningful part of the Mentionry workflow. The count feels justified given the breadth of features: site analysis, competitor discovery, prospecting, link verification, outreach drafting, agent management, document retrieval, and visibility reporting.
The core workflow is well covered: audit a site, find competitors, discover prospects, verify links, draft outreach, run agents, and read results. The main gaps are agent lifecycle management and document management beyond reading, but those may reasonably live in the Mentionry dashboard rather than the MCP surface.