lago_analytics_mrrMonthly recurring revenue (MRR) from subscription fees. Lago API: GET /analytics/mrr.
| Name | Required | Description | Default |
|---|---|---|---|
| months | No | Number of months to return. | |
| currency | No | Currency filter (ISO 4217, e.g. USD). |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide readOnlyHint=true. The description adds minimal extra context (MRR from subscription fees, API endpoint) but does not disclose other behavioral traits such as aggregation or response format.
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 sentence with the API endpoint, conveying purpose efficiently with no 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?
While the tool is simple with two optional parameters and no output schema, the description lacks information about the response structure or data format, which would be helpful.
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 coverage is 100%, so the description does not add meaning beyond what is already in the input schema for the two parameters.
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 it provides MRR from subscription fees and includes the API endpoint, distinguishing it from sibling tools like lago_analytics_gross_revenue.
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 usage for MRR analytics from subscription fees but does not explicitly state when to use it over alternatives or provide 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.
Each tool targets a distinct entity and action (e.g., lago_get_customer vs. lago_create_customer, lago_list_invoices vs. lago_get_invoice), with no overlapping purposes. The separation between analytics, customer, invoice, subscription, and other resource types is clear.
All tools follow a consistent verb_noun pattern with lower_snake_case and the 'lago_' prefix. Actions like 'get', 'list', 'create', and 'retrieve-related' (e.g., lago_get_customer_current_usage) are uniform, making the naming predictable.
With 24 tools, the server covers a broad range of billing/analytics operations. While slightly high, each tool serves a distinct purpose for reading or creating core entities and analytics. The count is well-scoped for its domain.
The server overwhelmingly focuses on read operations (list/get) with only two write tools (create_customer, create_event). Missing CRUD for invoices, subscriptions, plans, add-ons, coupons, credit notes, and wallets limits agents to mostly read-only workflows, creating significant gaps for managing billing lifecycles.