describe_agentWhat one agent does, step by step, and what it needs: the values you must pass to run_agent, what it hands back, how many of its steps reach a vendor that charges, and anything currently stopping it from running.
| Name | Required | Description | Default |
|---|---|---|---|
| agent_id | Yes | From list_agents. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description bears the full burden. It discloses useful behavioral content: step-by-step behavior, returned data, billed vendor steps, and current blockers. However, it does not explicitly state side effects (e.g., whether this tool is read-only or can trigger agent execution), which is a meaningful gap for an agent inspection tool.
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?
All information is packed into one front-loaded sentence, with the primary purpose first and supporting output details after. It is somewhat dense, but every clause adds distinct information.
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, no-output-schema tool, the description covers purpose, needed input, expected return content, cost-relevant steps, and run blockers. It only lacks explicit usage direction and side-effect clarification.
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 documents agent_id as 'From list_agents,' and the description adds the context that these are values needed for run_agent. That is helpful but not substantial; with 100% schema coverage the baseline of 3 is appropriate.
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 names the resource ('one agent') and the specific behavior ('what it does, step by step, and what it needs'), with concrete output categories. It is clearly distinct from siblings like list_agents (all agents) and run_agent (executes the agent).
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?
It implies a pre-run inspection use case by mentioning 'the values you must pass to run_agent' and blockers, but it never explicitly says when to prefer describe_agent over list_agents or run_agent. No alternatives or exclusions are named.
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.