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trigger_agent_activity

Trigger a specific agent to run a specific activity immediately. This dispatches the work and returns — it does not wait for the activity to complete. Use this to direct agents to take action.

[sensitive-tier, initiates a multi-step agent process — 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
reasonNoOptional. A specific instruction for THIS run only — e.g. "only reconcile the X reply queue, skip everything else". When given, it becomes this run's goal and takes priority over the activity's standing description. Omit for a normal run.
companyIdYesFreedomOS company id to act within (you must be a member). Required for company-scoped tools.
agent_nameYesName of the agent to trigger (e.g. "Aiko")
activity_nameYesName of the activity to run (e.g. "weekly_content_report")
resume_run_idNoOptional. UUID of a hung activity_runs row to continue on the SAME job (255s isolate death). Omit to start a new run.

TDQS

A4.1/5.0
Behavior4/5

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

Without annotations, the description carries full burden. It discloses key traits: the tool is asynchronous ('does not wait'), requires a company scope, has a sensitive approval tier, and mentions first-use manager approval flow. This provides rich context beyond what structured fields offer, though it could specify whether the activity runs immediately or is queued.

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 two compact paragraphs with no wasted words. The first paragraph states the core action and async behavior. The second paragraph adds approval context. It's well front-loaded, though the approval bracket could be more concisely integrated.

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 complexity (5 params, async, approval-sensitive) and no output schema, the description covers the input side well (async behavior, reason override, approval tiers). It does not describe what the returned response looks like, but the context does not require it for agent decision-making.

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% and description adds value by clarifying 'reason' overrides the activity's standing description, and 'resume_run_id' continues hung jobs. These details go beyond parameter names and schema descriptions. The agency of the tool is well contextualized.

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 it triggers a specific agent to run a specific activity, dispatches work immediately, and returns without waiting. It uses specific verbs ('trigger', 'direct') and resources ('agent', 'activity'), and the async behavior distinguishes it from synchronous execution tools.

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

Usage Guidelines3/5

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

The description states when to use it ('to direct agents to take action') and mentions sensitive-tier approval flow, but does not explicitly contrast with sibling tools like 'add_agent_activity' or 'deactivate_agent'. No when-not-to-use guidance is given, missing a chance to clarify when other tools are better suited.

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