Offload simple coding tasks to local Ollama and reduce Claude API usage.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "ollama-mcp" yet — see the docs or source repo.
Explain this Python code in concise English, including its purpose, inputs/outputs, and key logic, then suggest readability improvements:
python
for i, item in enumerate(items):
if item.get('active'):
result.append(transform(item))
A clear explanation of the code plus actionable readability improvement suggestions.
Write Jest unit tests for the following TypeScript function, covering normal cases, edge cases, and invalid inputs:
ts
export function normalizeEmail(email: string) {
return email.trim().toLowerCase();
}
Ready-to-use Jest test code with multiple test cases and coverage rationale.
Add inline comments and a function comment to this legacy JavaScript code without changing its logic, focusing on key steps and possible risks:
js
function sync(users){return users.filter(u=>u.id).map(u=>save(u))}
A commented version of the code that is easier for teams to understand and maintain.
Connect Claude Code to local llama.cpp for low-cost local LLM testing.
Connect local Ollama to MCP apps for chat, model management, and generation.
Offload token-heavy development tasks to local Ollama models and save API usage.
Offload non-critical LLM tasks to your own model and save premium quota.
Offload non-critical LLM tasks to your own model to save premium quota.
Delegate summarization, classification, extraction, and drafting tasks to a local LLM.