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create_play_from_activity

Draft a Play (growth_tactics with steps + human review) from an oversized agent activity. Does not run the play — operator Agrees via agree_playbook (MCP/Chat) or Focus first. Use when a run hit step/continuation limits because the work is multi-unit.

[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
reasonNo
agent_idNoUUID of the agent.
companyIdYesFreedomOS company id to act within (you must be a member). Required for company-scoped tools.
agent_nameNoName of the agent. Provide this or agent_id.
draft_stepsNo
draft_titleNo
linked_kr_idNo
activity_nameYesOversized activity to draft a Play from.
draft_categoryNo
draft_goal_impactNo
draft_custom_instructionsNo

TDQS

A4.1/5.0
Behavior4/5

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

With no annotations provided, the description carries the full burden of behavior disclosure. It discloses that the tool only drafts (does not run), mentions human review, and includes a sensitive-tier note explaining approval persistence (from-now-on vs just-once). This is valuable context. However, it does not fully explain what output the Play contains (e.g., steps, custom instructions) or any side effects beyond drafting, so it falls short of a 5.

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?

The description is succinct, front-loading the core purpose in the first sentence, then clarifying what it does not do, and adding usage and sensitive-tier context in subsequent lines. It is well-structured and avoids redundancy, though the sensitive-tier note adds a separate block that could be integrated more cleanly.

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

Completeness3/5

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

The tool has 11 parameters, no output schema, and no annotations, so the description must provide substantial context. While it explains when to use and the approval flow, it does not describe what a successful draft returns, how to structure draft_* parameters, or what 'growth_tactics with steps' entails. The missing parameter semantics and lack of output expectation leave the description incomplete for reliable invocation.

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

Parameters2/5

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

Schema description coverage is only 36%, and the description does not elaborate on the parameters. It references 'activity_name' implicitly as the oversized activity and mentions 'growth_tactics with steps' which loosely maps to draft_steps, but it provides no semantic detail for draft_title, draft_category, draft_goal_impact, draft_custom_instructions, or other parameters. The description fails to compensate for the low schema coverage, leaving agents without adequate parameter guidance.

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 drafts a Play from an oversized agent activity, with a specific verb (draft), resource (Play), and source context. It explicitly distinguishes from running the play via agree_playbook, and the phrase 'from an oversized agent activity' differentiates it from siblings like create_playbook. It is specific and unambiguous.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description gives explicit guidance on when to use: 'Use when a run hit step/continuation limits because the work is multi-unit.' It also explains the next step (operator agrees via agree_playbook) and clarifies that this tool does not run the play. This is strong situational guidance that prevents misuse and guides the agent to the correct workflow.

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