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nodes

Create, read, update, delete, and list graph nodes for reusable skills, knowledge, scripts, and environment variables.

Instructions

Manage graph nodes (SKILL, KNOWLEDGE, SCRIPT, ENV).

Supports create, read, update, delete, and list operations.

Args:
    operation: Operation to perform (create, read, update, delete, list)
    node_type: Type of node (SKILL, KNOWLEDGE, SCRIPT, ENV)
    node_id: Node ID (for read, update, delete)
    data: Node data (for create, update) - can be dict or JSON string
    filters: Filter criteria (for list) - can be dict or JSON string

Returns:
    Operation result

SCRIPT Node Best Practices:
    - Do NOT include `if __name__ == '__main__':` blocks - they are
      automatically stripped during execution to prevent unintended side effects
    - Export functions/classes that should be callable from user code
    - Keep example/test code in separate functions, not in __main__ blocks
    - Use PEP 723 metadata for dependencies
    - To use environment variables, create an ENV node and connect it to the
      SCRIPT using a CONTAINS relationship: SCRIPT -[:CONTAINS]-> ENV
      The ENV variables will be automatically loaded during execution

Examples:
    Create a SKILL node:
    ```
    nodes(
        operation="create",
        node_type="SKILL",
        data={
            "name": "data-pipeline",
            "description": "ETL pipeline for data processing",
            "body": "# Data Pipeline\n\nThis skill..."
        }
    )
    ```

    Create a SCRIPT node (note: no __main__ block):
    ```
    nodes(
        operation="create",
        node_type="SCRIPT",
        data={
            "name": "fetch_data",
            "description": "Fetch data from API",
            "function_signature": "fetch_data(url: str) -> dict",
            "body": '''# /// script

requires-python = ">=3.12"

dependencies = ["requests>=2.31.0"]

///

import requests

def fetch_data(url: str) -> dict: response = requests.get(url) response.raise_for_status() return response.json() ''' } ) ```

    List SCRIPT nodes:
    ```
    nodes(
        operation="list",
        node_type="SCRIPT",
        filters={"name": "fetch"}
    )
    ```

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
dataNo
filtersNo
node_idNo
node_typeYes
operationYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior4/5

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

With no annotations, the description carries the transparency burden. It discloses important behaviors: __main__ blocks are automatically stripped from SCRIPT nodes, ENV variables are auto-loaded via CONTAINS relationships, and data parameters accept dicts or JSON strings. These specifics go beyond a generic CRUD description, though it does not cover all edge behaviors (e.g., update merge/replace semantics).

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?

The description is well-structured with clear sections (overview, args, returns, best practices, examples) and front-loaded purpose. Although lengthy, the SCRIPT best practices and examples add significant value and avoid redundancy, earning their place.

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 5 parameters and complex SCRIPT-node handling, the description is nearly complete. It includes all parameter semantics, best practices, and examples for create and list. Since an output schema exists, the vague 'Returns: Operation result' is acceptable, but missing examples for read/update/delete and potential update semantics are minor gaps.

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%, so the description must compensate—and it does thoroughly. It explains each parameter's purpose, enumerates allowed values for operation and node_type, specifies which parameters apply to which operations, and clarifies that data/filters can be dicts or JSON strings.

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 opens with 'Manage graph nodes' and explicitly lists the node types (SKILL, KNOWLEDGE, SCRIPT, ENV) and supported operations (create, read, update, delete, list). This makes the tool's purpose unmistakable and distinct from siblings like 'relationships' or 'env'.

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 provides rich operational details (Args, SCRIPT best practices, examples) but never explicitly states when to choose this tool over alternatives. The usage context is implied by the tool name and node-type scope, but there is no direct when/when-not guidance or mention of sibling tools.

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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