freedom-mcp

get_stripe_metrics

Get Stripe metrics including average/median LTV, MRR, churn rate, active subscriptions, and an AI-recommended CAC target derived from the company's chosen LTV:CAC strategy (see get_cac_strategy / set_cac_strategy). Returns both blended company-wide metrics and per-plan-tier segments (e.g., Solo vs Team) with segment-specific LTV and CAC targets. Use this to guide customer acquisition spend decisions per customer type. Only works if Stripe is connected.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`companyId`	Yes	FreedomOS company id to act within (you must be a member). Required for company-scoped tools.

TDQS

A4/5.0

Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description must carry the behavioral burden. It implies a read operation ('Get') and mentions the Stripe connection dependency, but does not explicitly state read-only, data freshness, or API call behavior. Adequate but not detailed.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Two clear sentences: first lists output metrics and segmentation, second provides usage guidance and prerequisite. No unnecessary words; well-organized.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given a simple read tool with one parameter and no output schema, the description covers the key metrics, segmentation, prerequisite, and usage scenario. Missing explicit error states or format details, but sufficient for typical use.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% with one parameter (companyId) well-described in the schema. The description adds no additional parameter semantics beyond referencing the tool's purpose. Baseline score of 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states it retrieves Stripe metrics including LTV, MRR, churn rate, active subscriptions, and AI-recommended CAC target. It references per-plan-tier segments. The verb 'Get' plus specific metrics leaves no ambiguity about the tool's function, and it is distinct from sibling tools.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly suggests using it to 'guide customer acquisition spend decisions per customer type' and notes the prerequisite 'Only works if Stripe is connected.' While it lacks when-not-to-use or alternative tool mentions, the context is clear for most use cases.

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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TDQS

A3.6/5.0

Disambiguation4/5

The tool set is heavily disambiguated by detailed routing descriptions, domain prefixes, and lifecycle verbs, so most tools have a clear intended purpose. However, at 297 tools there are still close pairs and overlapping decision surfaces (e.g., approval workflows, 'what should I work on' readers, multiple finance/ads readers) that require careful description reading to avoid misselection.

Naming Consistency4/5

Naming is predominantly consistent snake_case verb_noun with strong domain prefixes like shopify_, x_, posthog_, and list_/create_/update_ patterns. Minor inconsistencies exist, such as several collection-returning tools using get_ (get_team_members, get_icps, get_okrs) instead of list_, and some generate_ vs create_ vs draft_ verbs, but the pattern is still predictable overall.

Tool Count1/5

297 tools is an extreme outlier and far beyond a usable MCP tool surface. Even a large suite has no justification for this count in one server; the agent would struggle to select among hundreds of similarly descriptive tools, and the natural 3-15 tool range is exceeded by nearly 20x.

Completeness4/5

The individual domains represented — OKRs, CRM/leads, Shopify, content pipelines, ads, PostHog, team hiring, knowledge, finance, and session management — are covered remarkably well with full lifecycle patterns. Minor gaps exist, such as no full deal CRUD, no delete for several Google/Shopify artifacts, and some analytical surfaces being read-heavy, but most workflows can be completed without dead ends.

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