Get usage analytics for an endpoint: total requests, monthly requests, revenue, and success rate. PATs or endpoint API keys improve accuracy. PATs require mcp:read or mcp:*.
| Name | Required | Description | Default |
|---|---|---|---|
| slug | Yes | The unique slug identifier for the endpoint | |
| apiKey | No | Legacy endpoint API key (optional, improves accuracy) | |
| accessToken | No | Your dashboard MCP personal access token (optional) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It discloses that PATs or endpoint API keys improve accuracy and that PATs require mcp:read or mcp:* permission, adding meaningful context about authentication and granularity. However, it does not describe response format or error behavior beyond the listed metrics.
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, front-loaded with the primary purpose and metrics, followed by a concise auth note. Every word earns its place with no redundancy or filler, making it efficient and easily scannable.
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?
Given the tool's simplicity and that the schema documents all parameters, the description covers the core purpose, metrics, and auth requirements. The lack of an output schema is partially mitigated by listing the metrics, but it omits potential details like response format or pagination, which would round out completeness.
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 provides 100% coverage of parameters, giving a baseline of 3. The description adds value by linking accessToken to the required mcp:read permission and explaining that both apiKey and accessToken improve accuracy, which is partly redundant with the schema but clarifies the permission semantics for the token.
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's function with a specific verb ('Get') and resource ('usage analytics for an endpoint'), and lists concrete metrics (total requests, monthly requests, revenue, success rate). This distinguishes it from sibling tools like get_endpoint_details or list_my_endpoints, which focus on other aspects of endpoints.
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 implies use for retrieving analytics but does not explicitly contrast it with alternatives or state when not to use this tool. While the context suggests using it for stats, there is no explicit mention of get_endpoint_details for non-analytics data or exclusion criteria.
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.
Most tools pair a clear verb with a distinct resource noun, so endpoints, agents, datasets, campaigns, products, and vault operations are generally easy to separate. The main risk is the repeated request_*_payment / *_with_payment pairs and the two create_*_with_payment tools, which are only distinguishable by reading descriptions carefully.
The dominant pattern is verb_noun snake_case, such as list_my_endpoints, update_campaign, and finalize_agent_registration, and the payment/request pairs are consistently named. The vault_* tools use a namespace prefix rather than verb-first names, and vault_how_to_backup breaks the action-oriented convention, so it is not perfectly uniform.
48 tools is a very large MCP surface, well beyond the 25-tool threshold where selection becomes a serious burden. Even though the server spans multiple domains, the count feels excessive for a single agent-facing tool set and should likely be split by domain.
The core endpoint, agent, marketplace, and payment flows are well covered, including creation, updates, stats, and two-phase x402 purchases. However, there is no create_product or delete_product, campaigns lack delete, and there is no way to list previously purchased datasets, leaving notable lifecycle gaps.