idea-basin-mcp

fetch_and_save

Retrieve content from any URL, extract it, and store it as a node in your knowledge graph with context for future retrieval.

Instructions

Fetch a URL, extract its content, and save it as a resource in the knowledge graph. The Basin server handles scraping (web pages, YouTube, GitHub, arXiv, PDFs) and automatic ingestion.

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": {
      "url": "<url>",
      "node_id": "<node_id>"
    },
    "name": "fetch_and_save"
  }
}

Input Schema

Name	Required	Description
`url`	Yes	URL to fetch and save
`node_id`	Yes	Target node ID. Use browse_nodes first to find the right location.
`why`	No	Why this URL is being saved — context for future retrieval

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0May 30, 2026

TDQS

B3.2/5.0

Behavior2/5

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

No annotations are provided, so the description must fully disclose behavior. It mentions automatic scraping and ingestion but fails to specify error handling, rate limits, side effects (e.g., overwriting), or return values. Critical gaps for a tool that performs network I/O and data creation.

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 with zero waste: first sentence nails the core purpose, second adds useful context about supported source types. Front-loaded and efficient.

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?

Given moderate complexity (3 params, no output schema), the description omits critical details: return value (likely nothing or resource ID?), error behavior, and relation to sibling tool save_resource. This limits the agent's ability to use it correctly.

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?

Input schema covers all parameters with descriptions, achieving 100% coverage. The description adds no new meaning beyond the schema, so baseline score of 3 is appropriate.

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?

Description clearly states the tool fetches, extracts, and saves URL content into the knowledge graph, covering various source types like web pages and PDFs. This distinguishes it from siblings like browse_nodes (navigation) and save_resource (saving already known data).

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No explicit guidance on when to use this tool vs. alternatives like save_resource or when to avoid using it (e.g., offline URLs, or if content is already in the graph). The context for selecting it is only implied by the action description.

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

Deploy Server