freedom-mcp

create_feature

Add a new feature to the Feature Index. Use when user says "I built X", "add feature Y", "track this capability", or describes a product feature they want to market. Features can later be pushed to Content Pipeline for marketing content.

[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
`title`	Yes	Display title (e.g., "AI Content Pipeline")
`limits`	No	Current limitations (e.g., "LinkedIn only", "Beta users only")
`solves`	No	Problems/pain points this feature solves (e.g., ["manual posting", "writer's block"])
`category`	No	Category (e.g., "ai", "marketing", "finance", "automation")
`demo_url`	No	URL to a demo video (Screen Studio, Loom, etc.)
`companyId`	Yes	FreedomOS company id to act within (you must be a member). Required for company-scoped tools.
`feature_id`	No	Unique slug for the feature (e.g., "ai-content-pipeline")
`description`	No	Marketing-ready description of the feature

TDQS

A4.1/5.0

Behavior4/5

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

No annotations exist, but the description discloses the write-tier approval process and hints at downstream use (Content Pipeline). Lacks details on idempotency or uniqueness constraints.

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

Conciseness4/5

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

Two paragraphs with clear purpose and behavioral note. Could be more structured (e.g., bullet points for triggers), but no unnecessary words.

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?

Covers purpose, triggers, and a key behavioral aspect (approval). Given no output schema and 8 parameters (2 required), the description adequately supports tool selection and invocation.

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 baseline is 3. The description adds no extra parameter context beyond the schema definitions.

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 adds a new feature to the Feature Index, specifies trigger phrases like 'I built X', and distinguishes from sibling tools (e.g., update_feature, list_features).

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?

Provides explicit use cases and trigger phrases, but does not mention when NOT to use or name alternative tools for related actions like updating or retiring features.

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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