freedom-mcp

run_quality_check

Evaluate content or media against your ICP persona using Gemini 3.1 Pro vision. Actually SEES images and WATCHES videos. Returns quality scores (1-10) across 6 dimensions + specific ICP feedback. Use after generating media or drafting content to validate quality before delivering to the user.

[sensitive-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
`task`	No	What this deliverable is for (e.g. "X post about Freedom OS launch"). Gives the ICP evaluator context.
`content`	No	Text content to evaluate (X post copy, email draft, newsletter). Can be combined with artifact_id for text + visual evaluation.
`companyId`	Yes	FreedomOS company id to act within (you must be a member). Required for company-scoped tools.
`artifact_id`	No	ID of a specific artifact to evaluate (from generate_image or generate_video result). If omitted, auto-finds the most recent media artifact.

TDQS

A4.5/5.0

Behavior4/5

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

The description discloses that the tool processes visual content and includes an approval note about sensitive-tier access. With no annotations provided, it carries the full burden and addresses authorization behavior, though it does not explicitly state side effects or destructive potential.

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 with two focused paragraphs: first outlining purpose and outcomes, second providing usage guidance and approval details. Every sentence adds value and is front-loaded.

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?

The description explains the return values (quality scores and ICP feedback) but lacks details on the structure of the 6 dimensions. Given no output schema, this is a minor gap. Overall, it is mostly complete for the tool's complexity.

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

Parameters5/5

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

The input schema has 100% coverage with descriptions. The description adds value beyond schema by explaining that artifact_id can be omitted to auto-find recent media, that content can be combined with artifact_id, and that task provides context for evaluation.

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 evaluates content or media against an ICP persona, using Gemini 3.1 Pro vision to see images and watch videos, returning quality scores and feedback. It distinguishes itself from sibling tools like generate_image or generate_video by being a quality check.

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?

The description says 'Use after generating media or drafting content to validate quality before delivering to the user,' providing clear when-to-use context. It does not explicitly mention alternatives or when not to use, but no sibling tool serves a similar purpose.

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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