ThinkingCap
# ๐ง ThinkingCap
A multi-agent research MCP server that runs multiple LLM providers in parallel and synthesizes their responses. Built on the Model Context Protocol for seamless integration with Claude Desktop, Cursor, and other MCP-compatible tools.
## ๐ Features
- **๐ Multi-Agent Research**: Deploy multiple AI agents simultaneously for comprehensive analysis
- **๐ฏ Multi-Provider Support**: OpenAI, Anthropic, xAI, Google, OpenRouter, Groq, Cerebras
- **โก Parallel Execution**: All agents run concurrently for maximum speed
- **๐ Intelligent Synthesis**: Combines multiple perspectives into unified, comprehensive answers
- **๐ Built-in Web Search**: DuckDuckGo search integration (no API key required)
- **๐ MCP Native**: Works with any MCP-compatible client via `npx`
## ๐ Quick Start
### Installation
No installation required! Just add to your MCP client configuration.
### Configuration
Add the following to your MCP client configuration (e.g., `~/.cursor/mcp.json`):
```json
{
"mcpServers": {
"thinkingcap": {
"command": "npx",
"args": [
"-y",
"thinkingcap",
"openrouter:moonshotai/kimi-k2-thinking",
"groq:moonshotai/kimi-k2-instruct-0905",
"cerebras:zai-glm-4.6",
"xai:grok-4-fast"
]
}
}
}
```
### Customizing Agents
You can specify any combination of providers and models as arguments:
```json
"args": [
"-y",
"thinkingcap",
"anthropic:claude-sonnet-4-20250514",
"openai:gpt-4o",
"google:gemini-2.0-flash"
]
```
## ๐ Supported Providers
| Provider | Env Variable | Default Model | Example |
|----------|--------------|---------------|---------|
| `openai` | `OPENAI_API_KEY` | gpt-5.1 | `openai:gpt-4o` |
| `openrouter` | `OPENROUTER_API_KEY` | moonshotai/kimi-k2-thinking | `openrouter:anthropic/claude-3.5-sonnet` |
| `groq` | `GROQ_API_KEY` | moonshotai/kimi-k2-instruct-0905 | `groq` |
| `cerebras` | `CEREBRAS_API_KEY` | zai-glm-4.6 | `cerebras` |
| `xai` | `XAI_API_KEY` | grok-4-fast | `xai:grok-4-fast` |
| `anthropic` | `ANTHROPIC_API_KEY` | claude-opus-4-5 | `anthropic` |
| `google` | `GOOGLE_API_KEY` | gemini-3-pro-preview | `google:gemini-2.0-flash` |
## ๐ Environment Variables
API keys are read from environment variables. Add them to your `~/.bashrc` or `~/.zshrc`:
```bash
export OPENROUTER_API_KEY="sk-or-..."
export GROQ_API_KEY="gsk_..."
export CEREBRAS_API_KEY="..."
export XAI_API_KEY="..."
# etc.
```
## ๐ ๏ธ How It Works
1. **Query Decomposition**: Your research query is broken into multiple specialized questions
2. **Parallel Execution**: Each agent (provider/model combo) researches a different angle
3. **Web Search**: Each agent performs web searches to gather current information
4. **Synthesis**: All agent responses are combined into one comprehensive answer
## ๐ฅ OpenRouter Fireworks Routing
When using OpenRouter, requests are automatically routed to Fireworks as the preferred provider with fallbacks enabled for maximum reliability.
## ๐ License
MIT License
## ๐ Acknowledgments
- Built on the [Model Context Protocol](https://modelcontextprotocol.io/)
- Inspired by multi-agent AI research systems
TDQS
Scored across 2 tools
The two tools have distinct names suggesting different purposesโlisting providers versus conducting researchโbut without descriptions, it's unclear if their functions overlap or are complementary. The limited count reduces ambiguity risk, but the lack of detail leaves room for potential confusion.
The tools use snake_case naming, which is consistent, but they follow different patterns: 'list_providers' uses a verb_noun format, while 'research' is a single noun. This mixed convention affects predictability, though it's not chaotic.
With only two tools, the server feels thin and under-scoped for a domain like 'ThinkingCap,' which suggests capabilities beyond basic listing and research. Such a low count limits functionality and may indicate incomplete coverage.
Given the server name 'ThinkingCap,' which implies cognitive or analytical functions, the tool set is severely incomplete. Only listing providers and research are offered, with no descriptions to clarify scope, leaving major gaps in potential operations like analysis, summarization, or querying.