idea-basin-mcp

save_resource

Save notes, research, links, code, or ideas to a knowledge graph node. Content is automatically chunked and embedded for semantic search.

Instructions

Save a new resource (note, research, link, code, idea) to a specific node in the knowledge graph. The content will be automatically chunked and embedded for future semantic search.

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": {
      "node_id": "<node_id>",
      "type": "<type>",
      "content": "<content>"
    },
    "name": "save_resource"
  }
}

Input Schema

Name	Required	Description
`node_id`	Yes	Target node ID. Use browse_nodes first to find the right location.
`type`	Yes	Resource type
`content`	Yes	The content to save (markdown supported)
`description`	No	Brief description / title for the resource
`why`	No	Why this is being saved — context for future retrieval
`url`	No	Source URL if this came from the web

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0May 30, 2026

TDQS

A3.9/5.0

Behavior4/5

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

With no annotations, the description carries full burden. It discloses automatic chunking and embedding for semantic search, which is a key behavioral trait. However, it does not mention permissions, error states, or reversibility.

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 two sentences, front-loaded with purpose, no superfluous words, and easy to parse.

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

Completeness3/5

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

While the description covers primary purpose and automatic processing, it lacks return value information and error handling hints. Given no output schema, this is a gap for full understanding.

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 coverage is 100% with good parameter descriptions (e.g., node_id suggests browsing first). The tool description adds context about chunking/embedding but does not elaborate on individual parameters beyond what schema provides.

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 saves a new resource (with enumerated types) to a specific node in the knowledge graph, distinguishes from siblings like browse_nodes (browsing) and fetch_and_save (web fetch), and mentions automatic chunking and embedding.

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 implies usage for direct content saving but does not explicitly contrast with fetch_and_save or provide when-not-to-use scenarios. No alternatives or preconditions are stated.

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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