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request_attention_transfer

Transfer work for THIS operator: push an instruction to a target coding session (or spawn one), optionally close/park the source. Use when they say "move this to a fresh Grok", "hand that off to Claude", or "continue on freedom-ai in a new tab". Composes create_attention_directive or request_attention_spawn + optional request_attention_close. Never invent paste rituals.

[write-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
cwdNoWorking directory for new spawn.
goalNoGoal for new spawn (required when spawning; defaults to first 120 of instruction).
companyIdNoFreedomOS company id to act within (you must be a member). Required for company-scoped tools.
from_hostNoHost of from_session (default grok).
close_fromNoIf true and from_session_id set, queue OS close of the source tab.
spawn_hostNoIf no to_session_id: open new tab with this host (grok | claude-desktop | claude-code).
instructionYesImperative work for the target session (1–4000 chars).
to_session_idNoExisting target session_id (from list). Omit with spawn_host to open a new tab instead.
kill_live_fromNoWith close_from: force-kill source live CLI (default false).
from_session_idNoOptional source session to park/close after transfer.

TDQS

A4.2/5.0
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. It discloses the write-tier approval flow, the optional side effect of closing/parking the source session, and that it composes other tools. It also adds the operational constraint 'Never invent paste rituals.' It does not detail kill_live_from semantics or what 'park' entails operationally.

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?

Four tight sentences plus a bracketed approval note; the action and resource are front-loaded, the trigger phrases are concrete, and the composition/approval context earns its place. There is no filler or redundancy.

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?

For a complex 10-parameter composite tool with no output schema, the description covers purpose, triggers, composition, constraints, and approval model well. Minor gaps: it does not state in prose that to_session_id and spawn_host are mutually exclusive (spawn vs. transfer), nor what the response contains, though the schema partially addresses the first point.

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 description coverage is 100%, so all 10 parameters are documented structurally; the baseline is 3. The description adds high-level orchestration meaning (target existing session vs. spawn new one, optional close source) but no per-parameter detail beyond what the schema already states.

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 opening line 'Transfer work for THIS operator: push an instruction to a target coding session (or spawn one), optionally close/park the source' names a specific verb, resource, and scope. It also distinguishes itself from the sibling sub-tools by explicitly stating it composes create_attention_directive or request_attention_spawn + optional request_attention_close.

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 gives explicit trigger phrases ('move this to a fresh Grok', 'hand that off to Claude', 'continue on freedom-ai in a new tab'), providing clear when-to-use context. It names the composed sibling tools but does not state explicit when-not-to-use conditions (e.g., when to call request_attention_spawn directly instead of this composite).

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