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register_external_artifact

Register external artifacts like code repos, documents, or URLs into Lightbulb for discovery and use by domain agents.

Instructions

Register an artifact you created OUTSIDE Lightbulb so Lightbulb's domain agents can discover, reference, and work on it.

Use this whenever you (Claude, Claude Code, ChatGPT, Codex, Hermes, etc.) produce something external while orchestrating Lightbulb — a code repository, document, slide deck, spreadsheet, report, or any URL — and you want it to become a first-class, discoverable Lightbulb artifact (searchable via list_artifacts and routable to domain agents / code workspaces).

Args: type: Artifact class — one of: codebase, document, slide_deck, spreadsheet, report, url. title: Human-readable title. summary: Short description of what it is and why it matters. uri: External reference (e.g. a GitHub repo URL, Google Doc URL, file link). Provide this OR content. content: Inline text content (for documents/reports) when there is no external URL. project_id: Optional Lightbulb project UUID to associate the artifact with. source_agent: The agent that created it (e.g. "claude_code", "codex", "chatgpt", "hermes"). attach_workspace: For a codebase artifact with a repo uri, also clone it into a Lightbulb Code Workspace so domain agents can work on it (not just discover it).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
typeYes
titleNo
summaryNo
uriNo
contentNo
project_idNo
source_agentNoexternal_agent
attach_workspaceNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior3/5

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

No annotations are provided, so the description carries full burden. It describes the registration action and the purpose, but lacks details on side effects (e.g., idempotency, overwrite behavior, authorization needs). It partially covers behavior with the attach_workspace parameter explanation.

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?

The description is concise and well-structured. It front-loads the purpose, then usage guidelines, then arguments. Every sentence adds value, with no redundant or fluff content.

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 parameter count (8) and low schema coverage, the description adequately covers all parameters and usage context. It does not describe the output schema, but context signals indicate an output schema exists, so the description need not explain return values. Minor gap: no mention of what the tool returns (e.g., artifact ID), but overall sufficient for agent decision-making.

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

Parameters5/5

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

Schema coverage is 0%, but the description provides detailed explanations for each parameter: type (with enum list), uri vs content distinction, project_id, source_agent, and attach_workspace behavior. This fully compensates for the lack of schema 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 registers externally created artifacts into Lightbulb, making them discoverable and routable. It distinguishes from siblings by emphasizing 'OUTSIDE Lightbulb' and listing examples like code repos and documents, contrasting with internal create 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 states when to use the tool: whenever an agent produces something external while orchestrating Lightbulb and wants it as a first-class artifact. It provides examples and mentions discoverability via list_artifacts. It does not explicitly state when not to use, but the context implies alternatives (e.g., create_document for internal artifacts).

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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