freedom-mcp

get_grain_policy

Read the content-grain (wisdom-layer) publish policy for the current company. For each content grain it returns whether an agent may publish that grain autonomously (gate_mode 'autonomous') or must route to a human (gate_mode 'human_pre_gate'), plus curate_only and source_corpus_ref. Use this to understand which content you may publish on your own vs. send for human pre-approval.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`companyId`	Yes	FreedomOS company id to act within (you must be a member). Required for company-scoped tools.

TDQS

A4.1/5.0

Behavior3/5

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

No annotations are provided, so the description must cover behavioral aspects. It correctly indicates the tool is read-only ('Read') and describes the output, but it does not disclose potential side effects (none expected), rate limits, or authentication details. Since the tool is simple and read-only, a score of 3 is adequate.

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 extremely concise: two sentences with no extraneous words. The first sentence defines the tool and its output, the second provides usage guidance. Every sentence is valuable and front-loaded.

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

Completeness5/5

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

Despite lacking an output schema, the description explicitly lists the returned fields (gate_mode, curate_only, source_corpus_ref) and explains the meaning of gate_mode values. This fully equips the agent to understand the return value. The tool's complexity is low, and the description is complete.

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% with one parameter (companyId) that is well-described in the schema. The description says 'for the current company', which adds minimal additional meaning beyond the schema. According to guidelines, when coverage is high (>80%), baseline is 3, and the description does not exceed that.

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 reads the content-grain publish policy, using a specific verb-resource pair. It distinguishes from the sibling tool set_grain_policy by being read-only, and explains the returned fields (gate_mode, curate_only, source_corpus_ref), making the purpose unambiguous.

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 provides usage guidance: 'Use this to understand which content you may publish on your own vs. send for human pre-approval.' This helps the agent decide when to call this tool. However, it does not mention any exclusions or alternatives (e.g., when not to use it), which prevents a perfect score.

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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