freedom-mcp

run_playbook

Run a saved Playbook (growth_tactics) for the company operator or agent — dispatch the next unit as a one-off draft activity, or dry-run a Playbook brief with suggest_only. Use when the operator or agent should execute an Agreed playbook this cycle (same owner as Focus “Run play”), or preview cast/steps/cost without spend. Structured playbooks require plan Agree before dispatch; suggest_only does not.

Routing: Run or dry-run a saved Playbook → use this

[sensitive-tier, initiates a multi-step agent process — company managers (executive/gm) run this without a card. Other members ask once; a from-now-on approval makes future calls seamless. Connecting a connector still needs the OAuth/connect card (request≠grant).]

Input Schema

TableJSON Schema

Name	Required	Description
`companyId`	Yes	FreedomOS company id to act within (you must be a member). Required for company-scoped tools.
`tactic_id`	No	UUID of the Playbook to run (use this or tactic_title).
`agent_name`	No	Optional: override which agent runs it (else resolved from the playbook's lane or assignee).
`suggest_only`	No	If true, return Playbook execution brief only (who / steps / readiness / estimate) — no dispatch, no spend, no Agree required. Use for Chat/MCP dry-run before Run.
`tactic_title`	No	Title (or fragment) of the Playbook to run (use this or tactic_id).

TDQS

A4.3/5.0

Behavior5/5

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

With no annotations provided, the description carries the full behavioral burden and does so strongly. It discloses that this is a sensitive-tier, multi-step agent process, explains approval/permission rules for managers vs other members, distinguishes from-now-on approvals, clarifies that connector access still requires OAuth, and states that suggest_only avoids dispatch, spend, and Agree.

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 front-loaded with the main purpose and mode split, then gives usage guidance, then permission context. It is longer than minimal but each sentence adds useful information; the routing line is slightly redundant but not harmful.

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, no annotations, and no output schema, the description covers purpose, preconditions (Agree), permission tiers, and dry-run behavior well. The main gap is that it does not describe the response/return value for the normal dispatch path, only the brief returned by suggest_only.

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 the baseline is 3 even without extra parameter detail in the description. The description reinforces the suggest_only semantics and the general run/dry-run distinction, but does not add meaningfully beyond the schema's already thorough parameter descriptions.

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 runs a saved Playbook or dry-runs it with suggest_only, naming the specific resource and the two modes of operation. It also distinguishes itself from related siblings by mentioning 'Agreed playbook' and previewing cast/steps/cost without spend, so an agent can tell it apart from playbook creation/agreement tools.

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 when to use this tool: when the operator or agent should execute an Agreed playbook this cycle, or preview without spend. It adds the prerequisite that structured playbooks require plan Agree before dispatch, which implies the use of a separate agreement tool, though it does not name that sibling explicitly.

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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