Which integrations are available for this server?

Provides web search capabilities for agents to gather current information during research queries without requiring an API key. Enables parallel execution of Google's Gemini models as research agents for multi-perspective analysis and synthesis of responses. Enables parallel execution of OpenAI's GPT models as research agents for multi-perspective analysis and synthesis of responses.

How do I use ThinkingCap?

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., "@ThinkingCap compare the latest AI safety approaches from OpenAI, Anthropic, and Google" 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.

ThinkingCap

by Infatoshi

Overview Schema Related Servers Score Discussions

TypeScript

Remote

🧠 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.

🌟 Features

🔀 Multi-Agent Research: Deploy multiple AI agents simultaneously for comprehensive analysis
🎯 Multi-Provider Support: OpenAI, Anthropic, xAI, Google, OpenRouter, Groq, Cerebras
⚡ Parallel Execution: All agents run concurrently for maximum speed
🔄 Intelligent Synthesis: Combines multiple perspectives into unified, comprehensive answers
🔍 Built-in Web Search: DuckDuckGo search integration (no API key required)
🔌 MCP Native: Works with any MCP-compatible client via npx

🚀 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):

{
  "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:

"args": [
  "-y",
  "thinkingcap",
  "anthropic:claude-sonnet-4-20250514",
  "openai:gpt-4o",
  "google:gemini-2.0-flash"
]

📋 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`

🔑 Environment Variables

API keys are read from environment variables. Add them to your ~/.bashrc or ~/.zshrc:

export OPENROUTER_API_KEY="sk-or-..."
export GROQ_API_KEY="gsk_..."
export CEREBRAS_API_KEY="..."
export XAI_API_KEY="..."
# etc.

🛠️ How It Works

Query Decomposition: Your research query is broken into multiple specialized questions
Parallel Execution: Each agent (provider/model combo) researches a different angle
Web Search: Each agent performs web searches to gather current information
Synthesis: All agent responses are combined into one comprehensive answer

🔥 OpenRouter Fireworks Routing

When using OpenRouter, requests are automatically routed to Fireworks as the preferred provider with fallbacks enabled for maximum reliability.

📝 License

MIT License

🙏 Acknowledgments

Built on the Model Context Protocol
Inspired by multi-agent AI research systems

Available Tools

2 tools

list_providersD

ParametersJSON Schema

Name	Required	Description	Default
No parameters

TDQS

D1/5.0

Behavior1/5

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

Tool has no description.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness1/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Tool has no description.

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

Completeness1/5

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

Tool has no description.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters1/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Tool has no description.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose1/5

Does the description clearly state what the tool does and how it differs from similar tools?

Tool has no description.

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

Usage Guidelines1/5

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

Tool has no description.

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

researchD

ParametersJSON Schema

Name	Required	Description	Default
`query`	Yes	The research query or question to investigate
`maxSearchResults`	No	Maximum web search results per agent

TDQS

D1/5.0

Behavior1/5

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

Tool has no description.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness1/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Tool has no description.

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

Completeness1/5

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

Tool has no description.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters1/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Tool has no description.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose1/5

Does the description clearly state what the tool does and how it differs from similar tools?

Tool has no description.

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

Usage Guidelines1/5

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

Tool has no description.

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.

2 tool updatesv1.0.0Dec 8, 2025
- First observedlist_providers
- First observedresearch

Install Server

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

TDQS

D1.4/5.0

Disambiguation3/5

The two tools have distinct names suggesting different purposes—listing providers versus conducting research—but 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.

Naming Consistency3/5

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.

Tool Count2/5

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.

Completeness1/5

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.

Resources

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