freedom-mcp

challenge_as_customer

Run your deliverable past the company's customer truth: REAL Customer Evidence first (when stored), then generated ICP as labeled simulation. Returns honest feedback — what would make them engage, scroll past, or what's missing. Use on customer-impact deliverables before sending.

Input Schema

TableJSON Schema

Name	Required	Description
`context`	No	Optional additional context about what this deliverable is for, who will see it, or what outcome you want
`companyId`	Yes	FreedomOS company id to act within (you must be a member). Required for company-scoped tools.
`target_icp`	No	ICP ID to use (from get_icps), or "auto" to use the first available. Default: auto
`deliverable`	Yes	The content/report/strategy you want the simulated customer to evaluate
`deliverable_type`	Yes	What type of deliverable this is — helps the customer evaluate appropriately

TDQS

A4.1/5.0

Behavior4/5

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

Describes the two-step process (real evidence then simulation) and what feedback is returned (engagement, scroll past, missing). With no annotations, this disclosure is valuable, though missing details on permanence or side effects.

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?

Three sentences, front-loaded with action, no fluff. Efficiently communicates tool purpose, process, and usage.

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

Completeness3/5

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

Adequate but lacks details on return format/structure, which would be helpful given no output schema. The description outlines what feedback covers but not its shape.

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%, so the description adds no additional parameter-level meaning beyond the existing schema descriptions. Baseline score 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 the tool simulates customer feedback using real evidence or ICP, with a specific verb 'run past' and resource 'customer truth'. It is distinct from any 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 advises use on 'customer-impact deliverables before sending', providing clear context. Does not specify when not to use or mention alternative tools, but the guidance is direct.

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.

Resources

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