list_monitorsList the standing watches you have saved, newest first, with what each one watches, what it does, and when it last fired. Requires authentication and the monitors:read scope.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Beyond the readOnly and idempotent hints, the description adds concrete behavioral details: ordering (newest first), output fields, and permission requirements.
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 a single, clear sentence with no redundancy or unnecessary words.
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 simple list operation, the description covers the return content and access requirements; the output schema would detail the structure, but this description is complete in context.
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?
There are no parameters, so schema coverage is complete; the description adds no parameter-specific information, which is acceptable per the baseline.
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 the tool lists saved monitors with specific details (newest first, what each watches, what it does, when last fired), distinguishing it from other list tools.
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 mentions the required authentication and scope, which are prerequisites, but does not explicitly compare with alternative list tools; however, the purpose is specific enough for clear usage.
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.
Every tool targets a distinct resource and action, with detailed descriptions that clearly separate overlapping domains (e.g., consulting vs. marketing vs. outreach). Even within the same domain, tools like 'create_consulting_deliverable' and 'create_consulting_document_revision' are unambiguous due to their specific nouns.
All tools follow a consistent verb_noun snake_case pattern (e.g., 'create_invoice', 'get_deal', 'list_agents'). The few exceptions like 'locus_determine_from_scores' still adhere to the verb_noun structure and do not break the pattern.
With 124 tools, the server is massively over-scoped for typical MCP use. The tool count far exceeds the '50+ extreme mismatch' threshold, making it nearly impossible for an agent to efficiently navigate or select the right tool without extensive context. Even a large platform should consolidate or expose fewer tools.
The tool surface covers CRUD and lifecycle operations across at least 10 domains (sales, consulting, marketing, outreach, accounting, workflows, ticketing, API keys, feedback, platform metrics). Each domain appears to have no obvious gaps—e.g., invoicing includes create, update, send, mark paid, void; ticketing includes create, update, archive, dependencies, batch, scenarios, validation.