freedom-mcp

create_icp

Create a NEW Ideal Customer Profile (ICP) from scratch and save it — no Customer Hunter UI needed. Use this when get_icps returns hasICPs:false (the company has none yet) or to add another target customer profile. To CHANGE an existing ICP, use update_icp instead.

[write-tier — first use may require a manager's approval; a from-now-on approval makes future calls seamless, a just-once approval re-asks next time.]

Input Schema

TableJSON Schema

Name	Required	Description
`name`	Yes	Persona name. Required. Used to derive the ICP id/filename. INTERNAL targeting label (may be an evocative codename) — never published.
`class`	No	'customer' (default) or 'partner' — partner = a distribution/affiliate ICP, not an end-buyer.
`title`	No	One-line descriptor of the persona.
`channels`	No	Where they spend attention.
`companyId`	Yes	FreedomOS company id to act within (you must be a member). Required for company-scoped tools.
`painThemes`	No	Recurring pain themes.
`publicName`	No	The public-facing audience label to use in published copy — NEVER the internal persona name/codename. Plural noun phrase, e.g. "compounding pharmacy owners". Optional — auto-generated from the persona when omitted.
`agentProfile`	No	How this customer's own AI agent participates in buying: { tier: 'ambient' \| 'assisted' \| 'delegated' \| 'builder', agents: string[], surfacesRead: string[], purchasePath: string, autonomyNotes: string }. tier is required and must be one of the four values.
`demographics`	No	role, companySize, industry, techStack[].
`dreamOutcome`	No	The outcome they dream of.
`techSavviness`	No	Tech comfort level.
`financialProfile`	No	revenueRange, typicalDealSize, budgetAuthority, buyingBehavior, growthStage, priceSensitivity.
`nightmareScenario`	No	The 3am problem / nightmare scenario.

TDQS

A4.4/5.0

Behavior4/5

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

Since no annotations are provided, the description carries the full burden. It adds important behavioral context: the write-tier approval behavior (manager approval required on first use, with options for ongoing or one-time approval). This is valuable beyond the basic purpose. However, it could spell out that this is a mutating operation that creates a permanent record, but that is implied.

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?

The description is concise: two short paragraphs front-loading the main purpose and usage, followed by a note on authorization. No wasted sentences, every line adds value.

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 the tool's complexity (13 parameters, nested objects, no output schema), the description covers the core purpose, usage guidelines, and authorization behavior. It could mention what the return value is (e.g., confirmation, ICP ID) but since no output schema exists, the description is not deficient. The nested object details are covered in the schema.

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?

The schema has 100% coverage with descriptions for all 13 parameters. The description does not add additional parameter-level information beyond the schema; it focuses on overall usage. Therefore, it meets the baseline for schema coverage.

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 explicitly states the verb 'Create' and resource 'Ideal Customer Profile (ICP)', and distinguishes it from update_icp. It also mentions the specific condition (hasICPs:false) and the alternative tool, making the purpose very clear.

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?

The description provides explicit guidance on when to use this tool (when get_icps returns hasICPs:false or to add another profile) and when not (to change existing, use update_icp). This is perfect usage guidance.

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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