list_connectionsWhich outside tools this account has connected, and what each one is allowed to do here. Call it before offering to file, post or draft anything, so you can say what is actually available instead of guessing.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the behavioral disclosure burden. It explains what the tool returns—connected external integrations and their allowed permissions—and implies a non-mutating lookup. It does not explicitly state read-only behavior, but nothing about the description suggests side effects.
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 sentences carry the full message: what the tool does and when to call it. The core purpose is front-loaded, and there is no filler.
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 parameterless list tool with no output schema, the description sufficiently explains what the tool returns and when it should be used. It does not specify the exact response shape, but the agent knows enough to invoke it correctly.
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 tool has zero parameters, so the baseline for parameter semantics is 4. The description adds no parameter-related confusion, and the schema already documents the empty input contract.
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 clearly identifies the resource ('outside tools this account has connected') and the implied listing/read action. The phrase 'outside tools' distinguishes it from sibling tools like list_agents and list_documents.
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 gives an explicit trigger: call before offering to file, post, or draft anything, so the agent can report what is actually available. It does not mention alternatives or when-not-to-use cases, so it stops just short of a top score.
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.