list_documentsThe documents your agents have produced: what each is called, which agent wrote it, how long it is and how many versions it has. Pass an id to read_document for the text.
| 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 full behavioral disclosure. It states that the tool returns metadata only and not text, and that ids are meant for read_document. However, it leaves 'how long it is' ambiguous (characters, words, pages?) and says nothing about pagination, ordering, or limits, which is a noticeable gap for an unannotated 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?
Two sentences with no filler: the first states the main purpose with specific output fields, and the second provides a useful routing hint. The most important information is front-loaded, and every sentence contributes value.
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 no-parameter list tool without an output schema, the description covers the essential output fields and points to read_document for content retrieval. Minor omissions exist, such as the exact definition of 'length' and any list ordering/limits, but the tool is simple enough that an agent can 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 input schema has zero parameters and schema description coverage is effectively 100%, so there is no parameter documentation burden. The description adds no parameter-specific info, but it clarifies the output id's role with read_document. The no-parameters case warrants the baseline score of 4.
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 uses a specific verb ('list') and a clear resource ('documents your agents have produced') and enumerates the returned fields: name, author agent, length, and version count. It also differentiates from read_document by noting that text requires a separate call, making the tool's role unambiguous among its siblings.
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 explicitly routes text retrieval to read_document ('Pass an id to read_document for the text'), which tells the agent when not to use this tool. It implies the tool is for metadata overviews, though it doesn't explicitly state 'use this when you need document metadata.' The context is clear and actionable.
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.