Which integrations are available for this server?

Allows fetching and saving arXiv research papers to the knowledge base. Allows fetching and saving GitHub repository content (e.g., README) to the knowledge base. Allows fetching and saving YouTube video content to the knowledge base.

How do I use idea-basin-mcp?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@idea-basin-mcp search my knowledge base for AI ethics" That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

idea-basin-mcp

by MsFixer101

Overview Schema Related Servers Score Discussions

JavaScript

Remote

idea-basin-mcp

MCP server for Idea Basin — a semantic knowledge graph with vector search, hierarchical organization, and automatic content ingestion.

Gives Claude (and any MCP-compatible AI assistant) the ability to search, browse, and save to your personal knowledge base.

How it works

flowchart LR
    subgraph client["AI Assistant"]
        Claude["Claude / Any MCP Client"]
    end

    subgraph mcp["idea-basin-mcp"]
        direction TB
        T1["search_knowledge"]
        T2["browse_nodes"]
        T3["get_node"]
        T4["save_resource"]
        T5["fetch_and_save"]
    end

    subgraph basin["Idea Basin Server :3500"]
        API["REST API"]
        Embed["Embedder"]
        Scraper["Scraper"]
    end

    subgraph storage["Storage"]
        PG[("PostgreSQL + pgvector")]
    end

    Claude -- "stdio / JSON-RPC" --> mcp
    mcp -- "HTTP" --> API
    API --> Embed
    API --> Scraper
    API --> PG

Related MCP server: Memsolus MCP Server

Tools

Tool	Description
`search_knowledge`	Semantic similarity search across all knowledge chunks
`browse_nodes`	Browse the knowledge graph tree structure (3 levels deep)
`get_node`	Inspect a node with its children and resources
`save_resource`	Save a note, research, link, code, or idea to a node
`fetch_and_save`	Fetch a URL (web page, YouTube, GitHub, arXiv, PDF) and save it

Prerequisites

A running Idea Basin server (default: http://localhost:3500).

Install

Claude Code

claude mcp add idea-basin -- node /path/to/idea-basin-mcp/build/index.js

Or with a custom Basin URL:

claude mcp add idea-basin -e IDEA_BASIN_URL=http://localhost:3500 -- node /path/to/idea-basin-mcp/build/index.js

Claude Desktop

Add to ~/Library/Application Support/Claude/claude_desktop_config.json:

{
  "mcpServers": {
    "idea-basin": {
      "command": "node",
      "args": ["/path/to/idea-basin-mcp/build/index.js"],
      "env": {
        "IDEA_BASIN_URL": "http://localhost:3500"
      }
    }
  }
}

npm (once published)

npx idea-basin-mcp

Configuration

Env var	Default	Description
`IDEA_BASIN_URL`	`http://localhost:3500`	Base URL of your Idea Basin server

Development

npm install
npm run build    # TypeScript → build/
npm run dev      # Watch mode

License

MIT

Available Tools

5 tools

browse_nodesA

Browse the knowledge graph structure. Returns the tree from a starting node (or root) with 3 levels of children. Use this to understand how knowledge is organized before searching or saving.

ParametersJSON Schema

Name	Required	Description	Default
`node_id`	No	Start from this node (omit for root). Well-known: root=00000000-0000-0000-0000-000000000001

TDQS

A4.6/5.0

Behavior4/5

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

No annotations are provided, so the description carries full responsibility. It discloses that the tool returns a tree with 3 levels of children and can start from a specific node or root. This covers key behavioral traits, though it doesn't discuss pagination or limits, which is acceptable for a simple browse operation.

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 extremely concise with two sentences. The first sentence states the purpose and specifics (tree, 3 levels), and the second gives usage guidance. Every word earns its place with no redundancy.

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?

Given the low complexity (one optional parameter, no output schema), the description is complete. It explains the tool's functionality, how to use it (omit node_id for root), and provides a well-known root ID. The return value is described as a tree of children up to 3 levels, which is sufficient for this simple tool.

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?

The schema coverage is 100% with one parameter (node_id). The description adds valuable context beyond the schema: 'Start from this node (omit for root). Well-known: root=00000000-0000-0000-0000-000000000001'. This clarifies usage and provides a concrete example.

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 verb 'browse' and the resource 'knowledge graph structure', and specifies it returns a tree with 3 levels of children from a starting node or root. This distinguishes it from sibling tools like search_knowledge or fetch_and_save.

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?

The description explicitly says 'Use this to understand how knowledge is organized before searching or saving', providing clear context for when to use it. However, it does not explicitly mention when not to use it or name alternatives, though the sibling tools list provides some implicit differentiation.

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

fetch_and_saveB

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.

ParametersJSON 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

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.

get_nodeA

Get detailed information about a specific node including its children and all resources. Use this to inspect what's already stored in a particular area of the knowledge graph.

ParametersJSON Schema

Name	Required	Description	Default
`node_id`	Yes	The node ID to inspect

TDQS

A4.2/5.0

Behavior3/5

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

With no annotations, description carries full burden. It states the tool returns detailed info (children, resources) but does not disclose side effects (none apparent), authentication, or rate limits. Acceptable for a simple read tool but lacks depth.

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 no redundant information. Front-loaded with purpose and usage. Every word adds value.

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 no output schema, description adequately covers return values (detailed info, children, resources). For a single-parameter tool, this is sufficient, though it could mention pagination or depth limits if applicable.

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?

Schema coverage is 100% and describes node_id as 'The node ID to inspect'. Description adds meaning by specifying the returned content includes children and resources, going beyond the schema definition.

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 retrieves detailed information about a specific node including children and resources. It distinguishes from siblings like browse_nodes (browsing) and fetch_and_save (fetching/saving) by focusing on inspecting stored content.

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: 'Use this to inspect what's already stored in a particular area of the knowledge graph.' This implies read-only inspection. However, it does not explicitly mention when not to use it or compare to alternatives, though sibling context is available.

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

save_resourceA

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.

ParametersJSON 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

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.

search_knowledgeA

Search the knowledge base using semantic similarity. Returns ranked chunks with source info. Use this to find relevant information before creating new resources.

ParametersJSON Schema

Name	Required	Description
`query`	Yes	Natural language search query
`node_id`	No	Scope search to a specific node and its descendants
`limit`	No	Max results to return (default 10)

TDQS

A4.2/5.0

Behavior4/5

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

Since no annotations are provided, the description must disclose behavioral traits itself. It explains that the search is semantic and returns ranked chunks with source info, indicating a read-only, retrieval operation. It does not explicitly state that no data is modified, but the context implies safety.

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 concise sentences. The first sentence defines functionality and output; the second provides usage context. No redundant or filler content, making it easy to parse quickly.

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?

For a search tool with 3 simple parameters and no output schema, the description sufficiently covers the core behavior and return type ('ranked chunks with source info'). It could be more complete by mentioning pagination or result format, but is adequate for its complexity.

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 three parameters well. The description adds no extra meaning beyond the schema (e.g., no clarification on query formatting or node_id usage). 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?

The description clearly states the action ('Search'), target ('knowledge base'), method ('using semantic similarity'), and output ('Returns ranked chunks with source info'). It is distinct from sibling tools which are for browsing, fetching, or saving.

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 usage guidance: 'Use this to find relevant information before creating new resources.' This helps the agent understand when to apply the tool, though it does not mention when not to use it or list alternative tools.

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

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.

5 tool updatesv0.1.0May 30, 2026
- First observedbrowse_nodes
- First observedfetch_and_save
- First observedget_node
- First observedsave_resource
- First observedsearch_knowledge

Install Server

RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

TDQS

A4/5.0

Disambiguation5/5

Each tool serves a distinct purpose: navigating structure, ingesting external content, inspecting nodes, saving manual resources, and semantic search. There is no overlap in functionality.

Naming Consistency5/5

All tools use consistent snake_case with verb_noun pattern (browse_nodes, fetch_and_save, get_node, save_resource, search_knowledge), making the API predictable.

Tool Count5/5

With 5 tools, the server is well-scoped for knowledge graph management. Each tool is essential and the count is neither too few nor too many.

Completeness4/5

The set covers browsing, inspecting, adding (manual and fetched), and searching knowledge. Missing update/delete operations for nodes or resources, but these are not critical for core usage.

Maintenance

ActivityInactive

ResponsivenessSyncing

Resources

GitHub Repository

Need Help?

Related Servers

Unclaimed servers have limited discoverability.

Looking for Admin?

If you are the server author, to access and configure the admin panel.

Related MCP Connectors

BrainsOAuth
io.github.brainsforai
Personal wiki and memory layer for AI assistants. Persistent, structured memory across sessions.
Knowledge & Memory Note Taking AI & Machine Learning
2
ContextaOAuth
cc.contexta
Persistent memory and knowledge graph for AI assistants — keyword + vector + graph search.
Knowledge & Memory RAG Systems Vector Databases
2
Agently BrainOAuth
dev.agently
Company brain for AI agents — temporal knowledge graph search, exploration, and durable memory.
Knowledge & Memory Search RAG Systems
1
recordari A
io.recordar.api
Persistent knowledge graph for AI-augmented teams. Store decisions, findings, and standing rules across agent sessions with semantic search and typed connections. Includes cross-session memory, audit trail, workspace isolation, and secret detection. Built for teams running agents that need to remember. Free until launch with team tier as default, anon trial available.
Databases Knowledge & Memory Security
162

Related MCP Servers

Graphiti MCP Server
Knowledge & Memory Databases RAG Systems
chen9z
F
license
Not graded
quality
D
maintenance
Enables AI assistants to build and query temporally-aware knowledge graphs from conversations and data, maintaining persistent memory of entities, relationships, and facts across interactions.
Updated a year ago (2025-06-17 16:53 UTC)
-
Memsolus MCP Serverofficial
Knowledge & Memory RAG Systems Autonomous Agents
memsolus
A
license
A
quality
D
maintenance
Provides persistent long-term memory for AI agents through semantic search and automated knowledge graph extraction. It enables agents to store, recall, and reason over facts, preferences, and relationships across multiple conversations and sessions.
Updated 3 months ago (2026-06-06 18:33 UTC)
14
19
MIT
dejaview-mcp
Knowledge & Memory Search
JakeC77
A
license
Not graded
quality
D
maintenance
Provides persistent knowledge graph memory for AI agents, enabling them to store, recall, and query facts about people, projects, and relationships across sessions.
Updated 6 months ago (2026-03-05 08:23 UTC)
MIT
Contexta MCP
Knowledge & Memory Search
teleengco
A
license
Not graded
quality
D
maintenance
Persistent memory and knowledge graph server that fuses keyword, vector, and graph search into a single query, enabling AI assistants to recall typed entities and relationships across sessions.
Updated 5 months ago (2026-04-09 20:45 UTC)
MIT

Latest Blog Posts

Who's Calling? MCP Hosts Are an Identity Blind Spot (And the Spec Knows It)
By Om-Shree-0709 on July 25, 2026.
mcp
Agent Identity
OAuth 2.1
Your AI Chatbot Just Exposed Your CEO's Salary to an Intern
By Om-Shree-0709 on July 2, 2026.
Agent Identity
MCP Security
OAuth Delegation
Why MCP Servers Need Execution Sandboxing (And Why Your Current Stack Isn't Enough)
By Om-Shree-0709 on June 30, 2026.
Agentic Ai
Prompt Injection
WebAssembly

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/MsFixer101/idea-basin-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server

idea-basin-mcp

How it works

Tools

Prerequisites

Install

Claude Code

Claude Desktop

npm (once published)

Configuration

Development

License

Available Tools

Tool Schema Changelog

Maintenance

Resources

Looking for Admin?

Related MCP Connectors

Related MCP Servers

Memsolus MCP Serverofficial

Latest Blog Posts

MCP directory API