About Unreasnable-thinker-server MCP Server
Unconventional Thinking Server is a TypeScript MCP server that generates and tracks creative, boundary-breaking solutions. It aims to limit context usage by returning metadata first and loading full thought content on demand.
Key features
- 01generate_unreasonable_thought creates unconventional thoughts
- 02branch_thought supports more_extreme, opposite and tangential directions
- 03search_thoughts filters results on the server side
- 04thought:// resources load full content on demand
- 05Persistent storage in the .thoughts/ directory
Use cases
- 01Exploring unconventional solutions to a problem
- 02Finding thoughts marked as rebellious or challenging assumptions
- 03Keeping a record of ideas across sessions
MCP support
The server exposes tools and resources to MCP clients. Tools return resource_link items and structuredContent, and full thought content is fetched through thought:// resources.
Configuration
Local server config is the same for every client. Copy it into your client’s MCP config file.
We haven’t collected a package or remote endpoint for this server. Follow the instructions in the project README.
Tools
Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.
Details
From the project READMEUnconventional Thinking Server (v0.3.0)
A context-efficient MCP server for bold, unconventional, and boundary-breaking problem-solving.
This is a TypeScript-based MCP server that implements an unconventional thinking system optimized for context space savings based on Anthropic's latest MCP architecture patterns. It generates and tracks creative solutions to problems while maintaining efficiency.
MCP spec 2025-11-25 compliant — uses
@modelcontextprotocol/sdkv1.27.1 with tooltitle,annotations,outputSchema,structuredContentresponses, andresource_linkcontent type.
Architecture: Context-Saving Design
This server demonstrates Anthropic's recommended patterns for reducing context overhead by 98.7%:
Key Context-Saving Features
-
Resources API for On-Demand Data Loading
- Thought content is stored as resources (
thought://id) - Claude loads full content only when explicitly needed
- Metadata is returned by default, saving tokens
- Thought content is stored as resources (
-
Server-Side Filtering
search_thoughtsfilters data locally instead of passing unfiltered sets to Claude- Only matching results returned, not entire dataset
- Reduces context consumption by filtering at the source
-
Metadata-First Returns
- Tools return only essential metadata + resource URIs
- Full thought content accessible via Resources API
- Claude decides whether to fetch full content based on need
-
Persistent File-Based Storage
- Data persists in
.thoughts/directory - No in-memory bloat accumulating across sessions
- Easy to inspect and debug thoughts locally
- Data persists in
Features
Tools (All Context-Efficient, MCP spec 2025-11-25)
Each tool now includes:
title— human-readable display name shown in client UIsannotations— behaviour hints (readOnlyHint,destructiveHint,idempotentHint,openWorldHint)outputSchema— JSON Schema describing the structured resultstructuredContentin responses — machine-readable output conforming to the schemaresource_linkcontent items — explicit links clients can subscribe to or fetch
-
generate_unreasonable_thought— Generate new unconventional thoughts- Returns
resource_link+structuredContent, not raw text blobs - Can build upon or rebel against previous thoughts
- Full thought content available via Resources API
- Returns
-
branch_thought— Create new branches of thinking- Supports directions:
more_extreme,opposite,tangential(now enum-typed) - Returns
resource_link+structuredContentfor the new branch
- Supports directions:
-
search_thoughts— Efficient metadata search- Filters by branchId, isRebellion, challengesAssumption
- Returns
structuredContentwith typed count + thoughts array - Includes limit parameter to control result size
Resources (On-Demand Content Loading)
- Each thought available as a resource:
thought://[thoughtId] - Metadata includes: isRebellion, challengesAssumption, timestamp, branch info
- Full thought content loaded only when Claude explicitly requests it
- Dramatically reduces token usage when many thoughts exist
How This Implements Context Efficiency
1. Progressive Disclosure
Claude doesn't need the full content of 100 thoughts upfront. Instead:
search_thoughtsreturns just IDs and metadata (100 bytes per thought)- Claude selectively fetches full content via Resources API for relevant thoughts
- Similar to how filesystems work: list files, then open specific files
2. Server-Side Filtering
Traditional approach (❌ inefficient):
All 1000 thoughts → Claude → Claude filters → Uses only 10
(costs tokens for all 1000)
This server (✅ efficient):
search_thoughts filter params → Server filters locally → Returns only 10 results
(Claude never sees the unused 990)
3. Metadata-First Pattern
Tool responses contain:
- Thought ID
- Resource URI to access full content
- Brief metadata (2-3 KB each)
- NOT the full 500-character thought (saves ~5KB per thought)
Example savings: With 100 thoughts:
- Old way: 500KB context usage
- New way: ~30KB + fetch only what's needed
Development
Install dependencies:
npm install
Build the server:
npm run build
For development with auto-rebuild:
npm run watch
Installation
To use with Claude Desktop, add the server config:
On MacOS: ~/Library/Application Support/Claude/claude_desktop_config.json
On Windows: %APPDATA%/Claude/claude_desktop_config.json
{
"mcpServers": {
"unconventional-thinking": {
"command": "/path/to/unconventional-thinking/build/index.js"
}
}
}
Usage Example
Claude: Generate an unreasonable thought about scaling problems
→ Tool: generate_unreasonable_thought("scaling problems")
← Returns: resource_link (thought://...) + structuredContent { thoughtId, isRebellion, ... }
Claude: What are all the rebellious thoughts?
→ Tool: search_thoughts(isRebellion=true, limit=5)
← Returns: structuredContent { count, thoughts: [...metadata] }
Claude: I need to see the full content of thought_xyz
→ Resource: Read thought://thought_xyz
← Returns: Full thought content (loaded only when needed)
Debugging
Since MCP servers communicate over stdio, debugging can be challenging. We recommend using the MCP Inspector, which is available as a package script:
npm run inspector
The Inspector will provide a URL to access debugging tools in your browser.
References
This server implements patterns from:
Comments
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An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.