Waggle-mcp

store_node

Store user facts, preferences, decisions, and concepts as atomic nodes in a persistent memory graph for future retrieval and reasoning.

Instructions

Store a piece of knowledge as a node in the persistent memory graph. Call this whenever you learn something important from the user: facts, preferences, decisions, entities, concepts, or questions. Prefer atomic facts.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "label": "<label>",
      "content": "<content>",
      "node_type": "<node_type>"
    },
    "name": "store_node"
  }
}

Input Schema

Name	Required	Description
`tags`	No	Optional tags for categorization.
`label`	Yes	Short label for the knowledge being stored.
`content`	Yes	Full natural-language description for this node.
`project`	No	Optional project or workspace name used to partition memory.
`agent_id`	No	Optional agent or client identifier used to partition memory.
`node_type`	Yes	Category of knowledge represented by the node.
`session_id`	No	Optional conversation or run identifier used to partition memory.
`source_prompt`	No	Optional original prompt that produced this knowledge.

Schema Changelog

Changes observed during successful MCP inspections.

First observedv1.0.8Apr 25, 2026

TDQS

B3.4/5.0

Behavior2/5

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

No annotations are provided, so the description carries the full burden. It mentions 'persistent' storage but does not disclose behavioral traits like idempotency, overwrite behavior, rate limits, or required permissions. For a write operation, these gaps are significant.

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?

Two sentences, front-loaded with purpose, then usage context. No fluff or redundancy. Every sentence earns its place.

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

Completeness2/5

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

For a tool with 8 parameters, no output schema, and no annotations, the description is too brief. It omits return value, error conditions, and behavioral guarantees. Given sibling tools like 'decompose_and_store' and 'update_node', more context on how this tool fits into the overall memory graph workflow is needed.

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 the schema already documents all parameters. The description adds no additional parameter-specific meaning beyond 'atomic facts' hint, which is insufficient to raise the score above baseline 3.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the purpose: 'Store a piece of knowledge as a node in the persistent memory graph.' It also specifies when to call it: 'whenever you learn something important... Prefer atomic facts.' However, it does not explicitly distinguish from sibling tools like 'store_edge' or 'decompose_and_store', leaving some ambiguity for the agent.

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?

Provides explicit guidance on when to use (learned important facts, preferences, etc.) and suggests atomic facts. However, it lacks explicit when-not-to-use guidance or alternatives, given many sibling tools exist.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.