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rlm.artifact.store

Store a derived artifact with provenance metadata, including session, type, content, and optional span references for traceable processing.

Instructions

Store a derived artifact with provenance.

Args: session_id: Session to store artifact in type: Artifact type (summary, extraction, classification, custom) content: Artifact content span_id: Optional span ID for provenance span: Optional span reference (doc_id, start, end) - creates span if needed provenance: Optional provenance metadata (model, prompt_hash)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
session_idYes
typeYes
contentYes
span_idNo
spanNo
provenanceNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior3/5

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

With no annotations, the description covers some behavioral aspects, such as proving optional parameters like span_id and span, and noting that span 'creates span if needed.' However, it does not disclose whether storing overwrites existing artifacts, auth requirements, or error handling.

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, with a front-loaded purpose and clear parameter list. Every sentence adds value, and there is no redundancy or wasted words.

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 (6 params, 3 required) and presence of an output schema, the description is mostly complete. It covers all parameters and mentions provenance. It lacks details on idempotency or return value, but the output schema likely fills that gap.

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 0%, so the description compensates by explaining each parameter. It lists required ones and describes optional ones, e.g., 'Optional span ID for provenance' and 'Optional span reference (doc_id, start, end).' However, it does not specify valid values for type beyond examples.

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 starts with a specific verb+resource: 'Store a derived artifact with provenance.' It clearly distinguishes this tool from sibling tools like rlm.artifact.get and rlm.artifact.list, which are for retrieval and listing.

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 does not explicitly state when to use this tool versus alternatives. While the purpose is clear, there is no guidance on when not to use it or mention of sibling tools for different needs.

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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