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nucleus_sync

Synchronize state and share artifacts across AI agents. Manage triggers, deployment workflows, and cross-session data storage.

Instructions

Coordinate state across multiple AI agents, store and retrieve named artifacts, manage trigger-based automation, and orchestrate deployments. Use this tool when multiple agents need to share data, when you need to persist artifacts for cross-session use, or when managing deployment workflows. Do NOT use for persistent memory (use nucleus_engrams), session state (use nucleus_sessions), or task assignment (use nucleus_tasks). Actions: 'identify_agent' registers the current agent's identity in the brain. 'sync_status' shows sync state. 'sync_now' forces immediate state replication between brains (may overwrite remote data). 'write_artifact' stores a named data blob in .brain/artifacts/ for cross-session sharing (side effect: creates file). 'read_artifact' retrieves a stored artifact. 'list_artifacts' shows all stored artifacts. 'trigger_agent' dispatches an event to another registered agent. 'get_triggers'/'evaluate_triggers' manage automated trigger rules. 'start_deploy_poll' begins monitoring a deployment service for readiness. 'check_deploy' queries deployment status. 'complete_deploy' marks deployment as finished. 'smoke_test' validates a deployed service endpoint by hitting its URL. 'shared_read'/'shared_write'/'shared_list' manage a shared key-value store visible to all agents. Prerequisites: .brain directory. Sync operations require at least two configured brains. Deploy actions require network access. Returns JSON with {success: boolean, data: object}. Example: {action: 'write_artifact', params: {name: 'api_schema', content: '{...}', mime_type: 'application/json'}} returns {success: true, data: {stored: true, path: '.brain/artifacts/api_schema'}}.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
actionYesSelect the synchronization, artifact, trigger, or deployment action. 'identify_agent'/'sync_status'/'read_artifact'/'list_artifacts'/'get_triggers'/'check_deploy'/'shared_read'/'shared_list' are read-only. 'sync_now' forces state replication (may overwrite remote). 'write_artifact'/'shared_write' persist data. 'trigger_agent' dispatches events to other agents. 'evaluate_triggers' runs all trigger rules. 'start_deploy_poll'/'smoke_test' interact with external services.
paramsNoAction-specific parameters as key-value pairs. write_artifact: {name: string (required, unique identifier), content: string (required, artifact data), mime_type: string (optional, default 'text/plain')}. read_artifact: {name: string (required)}. trigger_agent: {agent_id: string (required), event: string (required, event name), payload: object (optional)}. start_deploy_poll: {service: string (required, service name), environment: string (required, e.g. 'production'|'staging')}. smoke_test: {url: string (required, endpoint URL), expected_status: integer (optional, default 200)}. shared_write: {key: string (required), value: any (required)}. shared_read: {key: string (required)}. sync_now: {target: string (optional, target brain path)}. identify_agent/sync_status/list_artifacts/get_triggers/evaluate_triggers/check_deploy/complete_deploy/shared_list/sync_auto/sync_resolve: no parameters needed.
Behavior4/5

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

Annotations provide basic safety hints, and the description adds significant behavioral context: sync_now may overwrite remote data, write_artifact creates a file, deploy actions require network access, and return format is JSON. Does not contradict annotations.

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?

Well-structured: high-level summary, action list, prerequisites, return format, example. Front-loaded with key info. Slightly verbose but effective.

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?

Comprehensive for a multi-action tool: covers all actions, prerequisites, return format, and example. Lacks error handling details but sufficient for agent selection.

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%, baseline 3. Description adds extra context beyond schema: explains each action's purpose, provides parameter examples and requirements (e.g., write_artifact requires name and content).

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 coordinates state across agents, stores artifacts, manages triggers, and orchestrates deployments. It explicitly distinguishes from siblings like nucleus_engrams, nucleus_sessions, and nucleus_tasks.

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?

Explicitly states when to use (multiple agents need to share data, persist artifacts, manage deployments) and when not to use (persistent memory, session state, task assignment), providing alternative tool names.

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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