discover_competitorsFind the domains competing with this one, from both search results and link profile overlap. Returns each candidate with the evidence for it, so a wrong suggestion can be recognised rather than trusted.
| Name | Required | Description | Default |
|---|---|---|---|
| domain | Yes | ||
| keywords | No | Optional. Sharpens the search half. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the disclosure burden. It clearly says results include evidence for each candidate and warns that wrong suggestions can be recognized rather than blindly trusted. This goes beyond the schema and gives agents a useful expectation of output quality and intent.
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?
Two tight sentences with no filler. The core purpose is front-loaded, and the second sentence adds return-value context that helps the agent interpret results. Every phrase earns its place.
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 only two simple parameters and no output schema, so the description covers the essential contract: what gets discovered, from which signals, and that candidates come with evidence. It could say a bit more about what evidence looks like, but it is sufficient for safe invocation.
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?
Schema coverage is 50%: the keywords parameter is described there and in the tool description, but the domain parameter is only implied by 'this one'. The phrase 'sharpens the search half' adds meaningful context for keywords, but the main domain parameter still relies on inference from the tool name.
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 states a specific verb ('Find') and a clear resource ('domains competing with this one'), and it specifies the two methods used ('search results and link profile overlap'). This distinguishes competitor discovery from sibling tools like find_prospects or backlink_gap without needing to inspect schemas.
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 when to use the tool by framing it around competitor overlap and evidence, but it does not explicitly state when not to use it or name alternatives. A note that this is for competitor discovery rather than prospecting, qualification, or gap analysis would improve routing.
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.