Cotforce Mcp
Force step-by-step Chain-of-Thought reasoning for smaller or local models through an MCP layer with auto, direct, or sampling modes.
Overview
cotforce-mcp is a MCP server for the Build phase that enforces step-by-step Chain-of-Thought when agents call small or local LLMs.
What is this MCP server?
- npm @slbdn/cotforce-mcp v1.1.3 with stdio MCP transport
- Enforces step-by-step Chain-of-Thought for weaker local models
- MODE env: auto, direct, or sampling for different LLM call paths
- API_BASE_URL and MODEL for direct HTTP to local servers (e.g. localhost:1234/v1)
- Tagline positioning: give brains to small models without cloud-only frontier APIs
- Version 1.1.3 on npm identifier @slbdn/cotforce-mcp
- Three documented MODE values: auto, direct, sampling
- stdio transport; repository github.com/islobodan/cotforce-mcp
What problem does it solve?
Small and local models skip reasoning steps and break multi-tool coding flows your agent depends on.
Who is it for?
Builders self-hosting OpenAI-compatible LLMs who want MCP-enforced reasoning without upgrading to the largest cloud models.
Skip if: Teams satisfied with frontier-model quality out of the box or workflows that cannot tolerate extra CoT latency and tokens.
What do I get? / Deliverables
After install, MCP-mediated calls follow structured CoT steps so local models produce more dependable agent outcomes.
- CoT-structured LLM responses inside MCP tool workflows
- Configurable routing between sampling and direct HTTP inference
- More stable multi-step agent behavior on smaller models
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Journey fit
Improving agent reasoning stacks is core build-time work when you wire custom models and tool loops before shipping features. Agent-tooling fits because CotForce sits between your MCP client and the model endpoint to structure CoT steps.
How it compares
CoT enforcement MCP middleware—not a model weights package or generic prompt library skill.
Common Questions / FAQ
Who is cotforce-mcp for?
Solo developers routing agents through small or local LLMs who need structured Chain-of-Thought to reduce brittle one-shot answers.
When should I use cotforce-mcp?
Use it while building agent features on localhost inference (LM Studio, mlx, etc.) when reasoning quality blocks reliable tool use.
How do I add cotforce-mcp to my agent?
Install @slbdn/cotforce-mcp from npm (1.1.3), set MODE and optionally API_BASE_URL and MODEL, register stdio in your MCP client, and route model calls through CotForce tools.