list_agentsThe agents on this account: what each is called, how many steps it has, whether it would run as it stands, and when it last changed. Call describe_agent next to learn what one needs before running it.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of disclosing behavior. It does explain what the response includes, especially the useful 'whether it would run as it stands' readiness indicator. However, it does not explicitly state that the operation is read-only, nor does it mention permissions, rate limits, or response shape beyond field names.
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 with no filler. The first sentence front-loads the tool's purpose and output fields, and the second sentence adds a concise, actionable next step. Every word 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?
For a zero-parameter list operation, the description covers the key returned fields and suggests the next logical call. It is sufficient for an agent to invoke the tool correctly, though it omits potential details like ordering, pagination, or exact output schema, which would matter if the response were complex.
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 takes no parameters, so there is nothing to document. The baseline of 4 applies because the description does not need to compensate for missing parameter guidance.
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 states that the tool lists agents on the account and enumerates the returned attributes: name, step count, runnability, and last modification time. It is distinguishable from describe_agent by positioning that as a follow-up, but it does not explicitly contrast with other list tools such as list_connections or 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 a concrete usage flow: call list_agents to get an overview, then call describe_agent to learn requirements before running an agent. It implies this tool is for surveying agents and checking run-readiness, though it does not state explicit exclusions or alternatives like run_agent.
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.