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set_grain_policy

Create or update the wisdom-layer publish policy for ONE content grain in the current company. gate_mode 'human_pre_gate' reserves the grain for human approval; 'autonomous' lets an agent publish it directly. A brand-new grain defaults to human_pre_gate (fail-safe). Because this governs an agent's own publishing autonomy, the change routes to operator approval — it does not take effect silently.

[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
NameRequiredDescriptionDefault
noteNoHuman-readable note on why this grain has this policy.
grainYesThe content grain key, lowercase_with_underscores (e.g. faith_values, harness_education, professional).
companyIdYesFreedomOS company id to act within (you must be a member). Required for company-scoped tools.
gate_modeNo'autonomous' = an agent may auto-publish this grain; 'human_pre_gate' = it must route to a human first.
curate_onlyNoIf true, an agent may only assemble this grain from source_corpus_ref, never originate de-novo content.
source_corpus_refNoFor curate_only grains: the corpus an agent may assemble from (e.g. a knowledge collection key).

TDQS

A4.4/5.0
Behavior5/5

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

With no annotations provided, the description fully carries behavioral disclosure. It reveals the mutable nature, operator approval requirement, default fail-safe mode for new grains, and sensitive-tier approval types. This adds significant transparency beyond the schema.

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 paragraphs with a clear structure: first sentence states the main action, followed by gate_mode details, defaults, and rationale. The bracketed note adds critical approval context without redundancy. Every sentence serves a purpose.

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 the tool's complexity (6 params, no output schema, no annotations), the description covers purpose, gate modes, defaults, and approval behavior. However, it lacks information about the response or return value, which is a minor gap.

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 context for 'gate_mode' (explaining enum values and defaults) and the overall approval implication, but does not elaborate on 'curate_only' or 'source_corpus_ref', which are sufficiently described in the schema.

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 action ('Create or update the wisdom-layer publish policy for ONE content grain in the current company.'), specifies the resource type ('content grain'), and distinguishes from siblings like 'get_grain_policy' by emphasizing the mutative and scope of 'ONE grain' and 'current company'.

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 explains when to use (to set publish policy), the two gate modes with defaults, and the approval routing ('routes to operator approval'). It does not explicitly contrast with read-only alternatives but provides enough context 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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