Mako Metrics
Server Details
Competitor Meta ads intelligence reports. List plans, create orders, Stripe checkout for humans.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
- Repository
- loganriebel/mako-metrics-mcp
- GitHub Stars
- 1
- Server Listing
- Mako Metrics MCP Server
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Tool Definition Quality
Average 4.2/5 across 2 of 2 tools scored.
The two tools are clearly distinct: one for listing plans and one for creating an order. There is no overlap in functionality, so an agent can easily differentiate them.
Both tools follow a consistent verb_noun pattern in snake_case ('create_order', 'list_plans'), which is predictable and clear.
With only two tools, the server feels minimal but is focused on the core workflow of listing plans and creating orders. The count is slightly low but still reasonable for a simple service.
The tool set covers the primary actions (list plans, create order) but lacks ancillary features like order status checking or plan detail retrieval, which could be helpful but are not strictly necessary for the stated purpose.
Available Tools
2 toolscreate_orderAInspect
Create a Mako Metrics order brief for a customer and return a Stripe checkout link for them to complete payment. After payment, we email the customer when the report is ready, typically within 24h. They download the PDF report and export ZIP (saved ad images/videos, browsable gallery, raw ad data) at makometrics.com/portal. No payment is taken by this tool. It only returns a pay link the human opens.
| Name | Required | Description | Default |
|---|---|---|---|
| plan | Yes | ||
| Yes | Customer work email (receipt, portal invite, and report-ready notifications). | ||
| notes | No | ||
| competitors | Yes | Brand or Meta Ad Library page names. Snapshot=1, Dominator=up to 5, Agency=up to 10. | |
| company_name | Yes | ||
| revenue_band | Yes | ||
| company_website | No | ||
| industry_vertical | Yes | ||
| client_account_name | No | Required for the agency plan: the client this batch is for. |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden and does well. It discloses that no payment is taken, that emails are sent after payment, delivery timing, and where reports are accessed. This goes beyond basic 'create order' and provides useful workflow details.
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 efficient and front-loaded, with the main action in the first sentence. Subsequent sentences add relevant context about payment, delivery, and access. It is slightly verbose but each sentence earns its place.
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 complexity (9 params, no output schema, no annotations), the description provides a solid overview of the entire order process. It covers the return value (Stripe link) and post-payment behavior. Missing details like plan-specific competitor limits are in the schema, so the description is reasonably complete.
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?
Schema description coverage is only 33%, and the description adds little parameter-level information. It references the customer and plan indirectly but does not explain parameters like company_name, revenue_band, or industry_vertical. The description does not compensate for the low schema coverage.
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 purpose with a specific verb and resource: 'Create a Mako Metrics order brief... and return a Stripe checkout link.' It distinguishes itself from the sibling tool list_plans by focusing on order creation and payment link generation.
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 provides clear context on when to use the tool: to create an order and obtain a checkout link. It also clarifies that no payment is taken, implying the human must complete payment. However, it does not explicitly mention alternatives or exclusions beyond that.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list_plansAInspect
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.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must carry the transparency burden. It lists content of plans but does not disclose behavioral details such as whether the operation is read-only, potential side effects, or external API dependencies. Since 'List' implies a read operation, this is acceptable but not fully transparent about edge cases.
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 long, front-loads the key purpose, and adds a usage directive with no redundant text. Every word 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 parameterless list tool with no output schema, the description provides a good overview of the data returned (pricing, inclusions, scope limits, delivery, guarantee, verification links) and a clear usage context. It could specify the return format, but for this complexity level it's sufficiently complete.
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 has no parameter details to provide. The empty schema is consistent. The description adds no parameter semantics, but none are needed. Baseline for 0 parameters is 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 clearly states the tool's function: listing Mako Metrics plans with specific attributes (pricing, inclusions, scope limits, delivery, guarantee, verification links). The verb 'List' and resource are unambiguous, and it distinguishes itself from the sibling tool create_order by positioning this as a pre-order recommendation step.
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 explicitly directs when to use: 'Use this to recommend a plan before ordering.' This establishes a clear usage context and implies that ordering (create_order) is a separate follow-up action. The sibling tool name reinforces the alternative, making the guidance explicit.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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