freedom-mcp

ingest_voice_corpus

Build or refresh the company's voice profile from REAL writing. Use this when the operator wants agents to learn their voice from their actual work — pass a URL to their blog / newsletter / posts (or an admired creator's page), or paste sample text. The system fetches it safely, distills the STYLE (cadence, word choice, argument-building — never faith substance), and merges it into the voice profile all drafting agents ground on. For any operator/brand setting up or improving how their content sounds.

[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
`text`	No	A pasted writing sample to learn from.
`urls`	No	Public URLs to learn the voice from (SSRF-guarded fetch).
`companyId`	Yes	FreedomOS company id to act within (you must be a member). Required for company-scoped tools.
`faith_heavy`	No	Mark the sources faith-heavy (style learned, faith substance excluded).
`subject_kind`	No	Whose voice — 'person' (personal brand) or 'brand'.
`subject_name`	No	The person or brand name.

TDQS

A4.4/5.0

Behavior4/5

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

No annotations are provided, so the description carries full burden. It explains the system 'fetches it safely, distills the STYLE (cadence, word choice, argument-building — never faith substance), and merges it into the voice profile.' It also includes a note about sensitive-tier access and approval requirements, which is valuable behavioral context beyond the basic function.

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 two concise paragraphs. The first paragraph delivers the core purpose and usage in a single, front-loaded sentence, followed by actionable guidance. The second paragraph adds necessary behavioral details (approval tiers) without clutter. Every sentence earns its place.

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 6 parameters, no output schema, and no annotations, the description covers the tool's purpose, usage, parameter roles, and behavioral aspects (safety, approval). It could mention potential failure modes or response format, but it is sufficiently complete for an AI agent to understand and invoke the tool correctly.

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% with every parameter described. The description adds value by explaining how parameters relate to real-world usage, e.g., 'pass a URL to their blog / newsletter / posts' maps to urls, 'paste sample text' maps to text. It also clarifies 'faith_heavy' and 'subject_kind' meaning, complementing the schema without redundancy.

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 'Build or refresh the company's voice profile from REAL writing.' It uses specific verbs ('build or refresh') and a resource ('voice profile'), and distinguishes itself from sibling tools like get_voice_profile or update_voice_profile by focusing on ingestion from external sources.

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 explicitly says 'Use this when the operator wants agents to learn their voice from their actual work' and provides concrete examples (blog, newsletter, sample text). It also states 'For any operator/brand setting up or improving how their content sounds.' While it lacks explicit exclusions, the context is clear enough for appropriate use.

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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