List Mako Metrics plans with pricing, what's included, scope limits, delivery, guarantee, and links to verify the merchant. Use this to recommend a plan before ordering. Covers the competitor report plans, which are the only products orderable through this server. May, the Meta ads agent, is a monthly engagement bought at makometrics.com/meta-ads-agent.
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Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden; it discloses that the tool covers competitor report plans and mentions a specific monthly engagement ('May, the Meta ads agent'), which gives helpful context beyond a simple list operation.
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 mostly concise with four sentences; the first two efficiently state function and usage, while the last adds context about a specific engagement, which, though slightly tangential, is still helpful for understanding the domain.
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 there are no parameters and no output schema, the description fully compensates by specifying what the list includes, why to use it, and how it relates to ordering, making it complete for a zero-parameter listing tool.
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 has zero parameters, so the description doesn't need to explain parameters but it adds value by describing the output content (pricing, what's included, scope limits, etc.), making it clear what information the list returns.
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 Mako Metrics plans with specific details (pricing, scope limits, delivery, guarantee, links), and distinguishes it from the only sibling 'create_order' by noting these plans are the products orderable through the server.
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 tells when to use this tool ('to recommend a plan before ordering') and implies not to use it for other actions, fitting well with the sibling 'create_order' which would be used after selection.
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.
The two tools, create_order and list_plans, have entirely distinct purposes: one lists available plans for recommendation, the other creates an order based on a selected plan. There is no overlap or ambiguity between them.
Both tools follow a consistent verb_noun pattern: create_order (verb 'create', noun 'order') and list_plans (verb 'list', noun 'plans'). The naming is clear, predictable, and uniform.
With only two tools, the server is minimalist but well-scoped for its apparent purpose: exploring plans and making an order. Adding a tool to check order status or retrieve an existing order would make it more complete, but the current count is reasonable for a focused ordering workflow.
The server covers the essential lifecycle steps: list plans and create an order. However, there are notable gaps: after creating an order, there is no tool to check order status, retrieve the generated report, or handle post-payment actions. This could require external processes and leave agents without full workflow support.