Chain of Thought
About
Exposes raw reasoning tokens from language models through Groq's API, allowing users to see the step-by-step thinking process within <think> tags before receiving final answers.
Details
- Author
- beverm2391
- Repository
- beverm2391/chain-of-thought-mcp-server
- GitHub stars
- 5
- Downloads
- 174
- Categories
- Design, AI, API, Infrastructure, Frontend, Other
Jump to
- Exposes raw chain-of-thought tokens from Qwen’s qwq model via Groq.
- Boosts performance on complex tool‑use benchmarks like SWE‑Bench.
- Acts as a scratchpad for rule verification and planning.
- Integrates easily with any MCP‑compatible AI agent.
- Simple installation with uv sync and one environment variable.
Setting up with Highlight
This MCP is not yet compatible with Highlight’s one-click setup. However, you can still use it with Highlight by following these steps:
- Download and install Highlight from highlightai.com/download
- Navigate to the plugins tab and select "Add Custom Plugin"
-
Configure the plugin with the settings below
Plugin Name
Chain of ThoughtCommand (node, npx, python, etc.)uvArguments-
Argument 1
--directory -
Argument 2
path/to/cot-mcp-server -
Argument 3
run -
Argument 4
src/server.py
Environment-
GROQ_API_KEY
your-groq-api-key
Please refer to the README for specific instructions on how to obtain API keys or other required environment variables.
-
Argument 1
- Enable "Start Automatically" if you want the plugin to start when Highlight launches
From the repository
1. Clone this repository to your local machine.
2. Run ``uv sync` to install depencies
3. Get a Groq API key from here.
4. Update your mcp configuration with:
"mcpServers": {
"chain_of_thought": {
"command": "uv",
"args": [
"--directory",
"path/to/cot-mcp-server",
"run",
"src/server.py"
],
"env": {
"GROQ_API_KEY": "your-groq-api-key"
}
}
}
The path should be the local path to this repository. You can get this easily by running pwd` in the terminal from the root of the repository.
Claude Desktop / Cursor
Paste into your MCP client config file to install this server.
{
"mcpServers": {
"chain of thought": {
"env": {
"GROQ_API_KEY": "your-groq-api-key"
},
"args": [
"--directory",
"path/to/cot-mcp-server",
"run",
"src/server.py"
],
"command": "uv"
}
}
}Linux
{
"env": {
"GROQ_API_KEY": "your-groq-api-key"
},
"args": [
"--directory",
"path/to/cot-mcp-server",
"run",
"src/server.py"
],
"command": "uv"
}Macos
{
"env": {
"GROQ_API_KEY": "your-groq-api-key"
},
"args": [
"--directory",
"path/to/cot-mcp-server",
"run",
"src/server.py"
],
"command": "uv"
}Windows
{
"env": {
"GROQ_API_KEY": "your-groq-api-key"
},
"args": [
"--directory",
"path/to/cot-mcp-server",
"run",
"src/server.py"
],
"command": "uv"
}Chain of Thought MCP Server
Anthropic's recent article "The "think" tool: Enabling Claude to stop and think in complex tool use situations" shows that using an external think tool notably increases performance on SWE Bench.
This MCP Server uses Groq's API to call LLMs which expose raw chain-of-thought tokens from Qwen's qwq model.
Installation
1. Clone this repository to your local machine.
2. Run ``uv sync` to install depencies
3. Get a Groq API key from here.
4. Update your mcp configuration with:
"mcpServers": {
"chain_of_thought": {
"command": "uv",
"args": [
"--directory",
"path/to/cot-mcp-server",
"run",
"src/server.py"
],
"env": {
"GROQ_API_KEY": "your-groq-api-key"
}
}
}
The path should be the local path to this repository. You can get this easily by running pwd` in the terminal from the root of the repository.
Instructing The AI To Use This MCP Server
I personally prefer the agent call this tool on every request to increase performance. I add this to my rules for the agent:
<IMPORTANT>
<when_to_use_tool>
You should call the mcp chain_of_thought tool every time you talk to the user, which generates a chain-of-thought stream which you will use to complete the user's request.
</when_to_use_tool>
Before taking any action or responding to the user use the chain of thought tool as a scratchpad to:
- List the specific rules that apply to the current request
- Check if all required information is collected
- Verify that the planned action complies with all policies
- Iterate over tool results for correctness
Here are some examples of what to iterate over inside the think tool:
<cot_tool_example_1>
User wants to cancel flight ABC123
- Need to verify: user ID, reservation ID, reason
- Check cancellation rules:
Is it within 24h of booking?
If not, check ticket class and insurance
- Verify no segments flown or are in the past
- Plan: collect missing info, verify rules, get confirmation
</cot_tool_example_1>
<cot_tool_example_2>
User wants to book 3 tickets to NYC with 2 checked bags each
- Need user ID to check:
Membership tier for baggage allowance
Which payments methods exist in profile
- Baggage calculation:
Economy class × 3 passengers
If regular member: 1 free bag each → 3 extra bags = $150
If silver member: 2 free bags each → 0 extra bags = $0
If gold member: 3 free bags each → 0 extra bags = $0
- Payment rules to verify:
Max 1 travel certificate, 1 credit card, 3 gift cards
All payment methods must be in profile
* Travel certificate remainder goes to waste
- Plan:
1. Get user ID
2. Verify membership level for bag fees
3. Check which payment methods in profile and if their combination is allowed
4. Calculate total: ticket price + any bag fees
5. Get explicit confirmation for booking
</cot_tool_example_2>
</IMPORTANT>
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