freedom-mcp

set_revenue_channels

Declare where this business makes money — stripe, xero, shopify, amazon, ebay, manual invoicing, "none_yet" (pre-revenue), or other (name it). This is OPERATOR TRUTH an agent cannot derive, so it is gated: an autonomous agent CANNOT declare it — only a human (chat) or a graduated MCP operator can. Once declared, agents stop asking to connect Stripe for businesses that don't use it and are routed to the right revenue tool for this company's actual channel(s). Call get_setup_state first — if "Revenue channels" already shows done, only call this again when the operator says it changed.

[sensitive-tier — company managers (executive/gm) run this without a card. Other members ask once; a from-now-on approval makes future calls seamless. Connecting a connector still needs the OAuth/connect card (request≠grant).]

Input Schema

TableJSON Schema

Name	Required	Description
`channels`	Yes	Any that apply: stripe, xero, shopify, amazon, ebay, manual, none_yet, other. "none_yet" is exclusive — if the business is pre-revenue, pass ONLY ["none_yet"].
`companyId`	Yes	FreedomOS company id to act within (you must be a member). Required for company-scoped tools.
`other_label`	No	Required when channels includes "other" — the operator's own words for the revenue channel (e.g. "wholesale invoices").

TDQS

A4.9/5.0

Behavior5/5

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

With no annotations, the description carries the full burden and does so richly: it discloses the OPERATOR TRUTH gating, the human/operator-only restriction, the downstream routing effect, and the sensitive-tier approval/card semantics. It also explains why the value cannot be inferred, making the mutating behavior and its consequences clear.

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?

Purpose is front-loaded in the first sentence and every subsequent sentence adds a distinct fact: gating, downstream routing, prerequisite/check, and auth-tier behavior. The length is justified because annotations are absent; there is no filler or tautology.

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

Completeness5/5

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

For a three-parameter setter with no output schema and no annotations, the description covers the full decision surface: what the tool is for, who may call it, when to call it, what side effects occur, and how the channel parameter behaves. An agent can select and invoke it correctly with the information given.

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

Parameters4/5

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

Schema coverage is 100%, so the baseline is 3. The description adds real meaning on top: it maps 'manual' to manual invoicing, explains 'none_yet' as pre-revenue, and says 'other' should be named — complementing the schema's enum and other_label description rather than repeating it. It could have explicitly tied other_label to 'other', but the schema already does that.

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 opens with a specific verb and resource: 'Declare where this business makes money' and then enumerates exact allowed values. It clearly identifies the tool as the revenue-channel declaration point and even references the prerequisite get_setup_state, so an agent can distinguish it from the broader set_* family.

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

Usage Guidelines5/5

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

It gives an explicit prerequisite ('Call get_setup_state first'), a when-not condition ('if Revenue channels already shows done, only call this again when the operator says it changed'), and a hard access rule (an autonomous agent CANNOT, only human/graduated operator). This is exactly the when/when-not guidance an agent needs.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All freedom-mcp tools Inspect

Try in Browser

RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

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.

Resources

Need Help?