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aiuluna
by aiuluna

save_resource

Store AI-generated SVG visualizations or Markdown documents in a knowledge graph to preserve visual representations and node-related documentation.

Instructions

Save AI-generated SVG graphics or Markdown documents to the knowledge graph. This tool must be used in conjunction with get_creation_guidelines and list_graphs tools. Use cases:

  1. Save SVG visualization representation of the graph

  2. Save Markdown documents related to nodes

  3. Batch save multiple resource files

Usage recommendations:

  1. First call get_creation_guidelines to get resource creation standards

  2. Use list_graphs to get target graph ID and node ID (if needed)

  3. Create resource content according to standards

  4. Use this tool to save the resource

  5. After saving, use get_node_details to check resource association status

Return data:

  • data: Saved resource information

    • id: Resource ID

    • type: Resource type (svg/markdown)

    • title: Resource title

    • description: Resource description

    • nodeId: Associated node ID (if any)

    • createdAt: Creation time

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
graphIdYesGraph ID, must be obtained from list_graphs return data
resourceTypeYesResource type: - svg: SVG graphics file - markdown: Markdown document
titleYesResource title, must comply with naming rules in get_creation_guidelines
contentYesResource content, must comply with format specifications in get_creation_guidelines
descriptionNoResource description (optional)
nodeIdNoAssociated node ID (optional), if provided must be obtained from nodes array in list_graphs
Behavior4/5

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

With no annotations provided, the description carries full burden and does well by explaining the tool's role in a workflow, mentioning batch saving capability, and detailing the return data structure. However, it doesn't explicitly state that this is a write/mutation operation (though implied by 'save'), nor does it mention error conditions or permissions required.

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 well-structured with clear sections (purpose, use cases, usage recommendations, return data) and every sentence adds value. It's appropriately sized for a complex tool with workflow dependencies, with no redundant or unnecessary information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a mutation tool with no annotations and no output schema, the description provides excellent completeness. It explains the tool's purpose, workflow dependencies, use cases, and detailed return data structure, giving the agent sufficient context to use the tool correctly despite the lack of structured metadata.

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?

With 100% schema description coverage, the baseline is 3. The description adds value by explaining how parameters relate to other tools (graphId from list_graphs, title/content rules from get_creation_guidelines) and clarifying the optional nature of nodeId association. However, it doesn't provide additional semantic context beyond what's in the 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's purpose with specific verbs ('save AI-generated SVG graphics or Markdown documents') and resources ('to the knowledge graph'). It distinguishes from siblings by focusing on saving resources rather than creating/deleting graphs/nodes/edges or updating existing resources.

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?

The description provides explicit usage recommendations with a numbered workflow, naming specific sibling tools (get_creation_guidelines, list_graphs, get_node_details) for prerequisites and follow-up actions. It also lists three specific use cases, giving clear context for when to use this tool.

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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