MCP Servers

A collection of Model Context Protocol servers, templates, tools and more.

A
Agentic MCP Server
by @jsxtech
GitHub Repo(1 stars)

Agentic AI multi-agent application with local LLM (Ollama) and MCP server support. 45 agents, 50 workflows, fully offline. Jaspal 9891156880

Created 5/5/2026
Updated about 6 hours ago
README
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Repository documentation and setup instructions

Multi-Agent AI Application (100% Offline)

A fully local, open-source agentic AI system with 45 specialized agents, 50 predefined workflows, and an MCP server — powered by Ollama. No API keys, no cloud services, no internet required after setup.

Table of Contents

Complete Installation Guide

Prerequisites

  • Python 3.12+
  • 8GB+ RAM (16GB recommended for 13B models)
  • ~5GB disk space (for model + dependencies)
  • No GPU required (but speeds up inference)

Step 1: Install Ollama (Local LLM Runtime)

# Linux
curl -fsSL https://ollama.com/install.sh | sh

# macOS
brew install ollama

# Windows — download installer from https://ollama.com/download

# Verify installation
ollama --version

Step 2: Start Ollama Service

# Start the Ollama daemon
ollama serve

# It runs on http://localhost:11434 by default
# Keep this terminal open, or run as a system service:
# sudo systemctl enable ollama && sudo systemctl start ollama  (Linux)

Step 3: Pull a Local LLM Model

# Recommended: good balance of speed and quality
ollama pull llama3.1:8b

# Alternatives:
ollama pull mistral              # Fast, 7B params
ollama pull codellama:13b        # Best for code tasks
ollama pull qwen2.5:7b           # Good multilingual
ollama pull llama3.1:70b         # Best quality (needs 40GB+ RAM)
ollama pull deepseek-coder:6.7b  # Specialized for code
ollama pull phi3:mini             # Smallest, fastest

# Verify model is available
ollama list

Step 4: Set Up the Application

cd multi-agent-app

# Create Python virtual environment
python3 -m venv .venv

# Activate it
source .venv/bin/activate        # Linux/macOS
# .venv\Scripts\activate         # Windows PowerShell
# .venv\Scripts\activate.bat     # Windows CMD

# Install all dependencies
pip install -r requirements.txt

Step 5: Configure (Optional)

Edit config.py if you changed defaults:

OLLAMA_BASE_URL = "http://localhost:11434"  # Ollama address
MODEL_NAME = "llama3.1:8b"                  # Model you pulled
MAX_TOKENS = 2048                           # Max response length

Step 6: Run

# Interactive CLI mode
python main.py

# Or as MCP server
python main.py --mcp-server

Local LLM Setup (Ollama)

Managing Models

# List installed models
ollama list

# Pull a new model
ollama pull <model-name>

# Remove a model
ollama rm <model-name>

# Show model details
ollama show llama3.1:8b

# Test a model directly
ollama run llama3.1:8b "Hello, how are you?"

Recommended Models by Use Case

| Use Case | Model | RAM Needed | Speed | |----------|-------|-----------|-------| | General (default) | llama3.1:8b | 8GB | Fast | | Code-heavy work | codellama:13b | 16GB | Medium | | Fast responses | mistral or phi3:mini | 4-8GB | Very fast | | Complex reasoning | llama3.1:70b | 40GB+ | Slow | | Multilingual | qwen2.5:7b | 8GB | Fast | | Code + explanation | deepseek-coder:6.7b | 8GB | Fast |

Switch Model at Runtime

No restart needed — switch in the CLI:

/model codellama:13b
/model mistral

Ollama Configuration

# Change Ollama host/port (if needed)
export OLLAMA_HOST=0.0.0.0:11434

# Set GPU layers (for partial GPU offload)
export OLLAMA_NUM_GPU=999

# Set number of threads
export OLLAMA_NUM_THREAD=8

Local MCP Server Setup

This App as an MCP Server

Your multi-agent system IS an MCP server. Start it:

# stdio transport (for Claude Desktop, Cursor, etc.)
python main.py --mcp-server

# SSE/HTTP transport (for web clients or remote access on LAN)
python main.py --mcp-server --transport sse --host 0.0.0.0 --port 8080

What Gets Exposed via MCP

| Type | Count | Description | |------|-------|-------------| | Tools | 47 | run_multi_agent + 45 individual agent tools + list_agents | | Resources | 2 | agents://list, config://system |

MCP Server CLI Arguments

python main.py --mcp-server [OPTIONS]

Options:
  --transport {stdio,sse}   Transport protocol (default: stdio)
  --host HOST               Bind address for SSE (default: 0.0.0.0)
  --port PORT               Port for SSE (default: 8080)

External Local MCP Servers

Your agents can consume tools from OTHER local MCP servers running on your machine.

Install Local MCP Servers

# Install Node.js MCP servers (one-time, cached locally)
npx -y @modelcontextprotocol/server-filesystem /tmp
npx -y @modelcontextprotocol/server-sqlite mydb.sqlite
npx -y @modelcontextprotocol/server-memory

# Or install Python-based MCP servers
pip install mcp-server-fetch
pip install mcp-server-git

Configure External Servers

Edit config.py:

EXTERNAL_MCP_SERVERS = [
    # Filesystem access — agents can read/write local files
    {
        "name": "filesystem",
        "transport": "stdio",
        "command": "npx",
        "args": ["-y", "@modelcontextprotocol/server-filesystem", "/home/jaspal/projects"]
    },

    # SQLite — agents can query local databases
    {
        "name": "sqlite",
        "transport": "stdio",
        "command": "npx",
        "args": ["-y", "@modelcontextprotocol/server-sqlite", "/home/jaspal/data/app.db"]
    },

    # Memory/Knowledge base — persistent agent memory
    {
        "name": "memory",
        "transport": "stdio",
        "command": "npx",
        "args": ["-y", "@modelcontextprotocol/server-memory"]
    },

    # Git — agents can interact with local repos
    {
        "name": "git",
        "transport": "stdio",
        "command": "python",
        "args": ["-m", "mcp_server_git", "--repo", "/home/jaspal/projects/myapp"]
    },

    # Custom local MCP server (running on localhost)
    {
        "name": "custom-tools",
        "transport": "sse",
        "url": "http://localhost:9090/sse"
    },
]

How It All Connects (100% Local)

┌─────────────────────────────────────────────────────────┐
│                    YOUR MACHINE                          │
│                                                         │
│  ┌─────────────┐     ┌──────────────────────────────┐  │
│  │   Ollama    │     │   Multi-Agent App            │  │
│  │ (Local LLM) │◄───►│   45 agents + supervisor     │  │
│  │ :11434      │     │   MCP server (stdio/SSE)     │  │
│  └─────────────┘     └──────────┬───────────────────┘  │
│                                  │                      │
│                    ┌─────────────┼─────────────┐        │
│                    ▼             ▼             ▼        │
│  ┌──────────────┐ ┌───────────┐ ┌───────────────┐     │
│  │ MCP Server:  │ │MCP Server:│ │ MCP Server:   │     │
│  │ filesystem   │ │ sqlite    │ │ memory        │     │
│  │ (local files)│ │ (local db)│ │ (local store) │     │
│  └──────────────┘ └───────────┘ └───────────────┘     │
│                                                         │
│  ┌──────────────────────────────────────────────────┐  │
│  │ MCP Clients: Claude Desktop / Cursor / VS Code   │  │
│  └──────────────────────────────────────────────────┘  │
│                                                         │
│  Network: ZERO external traffic                         │
└─────────────────────────────────────────────────────────┘

Quick Start

# Terminal 1: Start Ollama
ollama serve

# Terminal 2: Run the app
cd multi-agent-app
source .venv/bin/activate
python main.py

Then type:

🧑 You: write a Python REST API with Flask for a todo app

The supervisor automatically routes to the best agent(s) and returns the result.

CLI Commands Reference

Agent Execution

| Command | Description | Example | |---------|-------------|---------| | (just type) | Auto-route via supervisor | write a REST API for users | | /ask <agent> msg | Run specific agent | /ask coder write binary search | | /chain <a\|b\|c> msg | Chain agents sequentially | /chain coder\|reviewer\|tester build calculator | | /parallel <a,b> msg | Run concurrently | /parallel coder,security write login | | /compare <a,b> msg | Compare outputs | /compare coder,refactorer implement sort | | /workflow <name> msg | Predefined pipeline | /workflow full_dev build todo app | | /auto msg | Auto-select workflow | /auto fix the login bug | | /feedback <a> <r> msg | Iterative refinement | /feedback coder reviewer write parser | | /batch <agent> t1;;t2 | Batch process | /batch coder sort;;search;;hash | | /stream msg | Stream response | /stream explain monads |

File Context

| Command | Description | Example | |---------|-------------|---------| | /file <path> msg | Single file context | /file src/app.py review this | | /files <p1,p2> msg | Multiple files | /files api.py,db.py find bugs |

Session Management

| Command | Description | |---------|-------------| | /save [name] | Save session | | /load <name> | Load session | | /export | Export as markdown | | /sessions | List sessions | | /history | Show history | | /clear | Clear history |

Memory

| Command | Description | |---------|-------------| | /remember <key> note | Store persistent note | | /recall [key] | Recall notes | | /forget [key] | Clear memory |

System

| Command | Description | |---------|-------------| | /agents | List all 45 agents | | /workflows | List all 50 workflows | | /tokens | Token usage stats | | /model <name> | Switch model | | /health | Check Ollama | | /retry | Re-run last request | | /help | Show help | | quit / exit | Exit |

Agents (45)

| Agent | Description | Temp | |-------|-------------|------| | researcher | Gathers information and provides summaries | 0.7 | | coder | Writes clean, production-quality code | 0.3 | | reviewer | Reviews code/content for quality and correctness | 0.4 | | planner | Breaks down complex tasks into actionable steps | 0.5 | | debugger | Diagnoses errors and suggests targeted fixes | 0.2 | | writer | Writes documentation, emails, and reports | 0.6 | | tester | Writes test cases and testing strategies | 0.3 | | optimizer | Performance optimization and bottleneck analysis | 0.3 | | security | Security analysis and vulnerability detection | 0.2 | | data_analyst | Data analysis, SQL queries, and data modeling | 0.4 | | devops | CI/CD, Docker, Kubernetes, and infrastructure | 0.3 | | translator | Translation and localization between languages | 0.5 | | architect | System architecture design and trade-off analysis | 0.4 | | mentor | Explains concepts and guides learning | 0.6 | | summarizer | Condenses content into key points and summaries | 0.3 | | api_designer | API design, OpenAPI specs, and contracts | 0.3 | | database | Schema design, SQL optimization, and DB architecture | 0.3 | | ux_designer | UI/UX design, wireframes, and accessibility | 0.5 | | refactorer | Code restructuring and maintainability improvements | 0.2 | | explainer | Code walkthroughs and detailed explanations | 0.5 | | validator | Verifies implementations match requirements | 0.2 | | automator | Automation scripts, CLI tools, and workflows | 0.3 | | migrator | Code/database/infrastructure migrations | 0.3 | | prompt_engineer | Crafts and optimizes LLM prompts | 0.4 | | diagrammer | Creates Mermaid/PlantUML system diagrams | 0.3 | | estimator | Effort and time estimation for tasks | 0.4 | | compliance | Regulatory compliance and standards audits | 0.2 | | product_manager | Requirements, user stories, and prioritization | 0.5 | | interviewer | Interview questions and answer evaluation | 0.5 | | git_expert | Git workflows, branching, and conflict resolution | 0.3 | | accessibility | WCAG compliance and inclusive design | 0.3 | | performance_tester | Load testing and scalability analysis | 0.3 | | error_handler | Error handling patterns and resilience | 0.3 | | documentation | API docs, changelogs, and guides | 0.5 | | regex_expert | Crafts and explains regular expressions | 0.2 | | shell_expert | Shell scripting and Unix tools | 0.3 | | ml_engineer | ML pipelines, training, and evaluation | 0.4 | | concurrency | Async, threading, and parallel processing | 0.3 | | config_manager | Configuration, env vars, and feature flags | 0.3 | | code_generator | Boilerplate, scaffolding, and templates | 0.3 | | tech_lead | Technical decisions and team guidance | 0.4 | | seo_expert | SEO optimization and web performance | 0.4 | | monitoring | Observability, alerting, and SRE practices | 0.3 | | networking | DNS, load balancing, and network architecture | 0.3 | | contract_tester | API contract testing and compatibility | 0.2 |

Workflows (50)

Development

| Workflow | Pipeline | |----------|----------| | full_dev | planner → coder → reviewer → tester | | code_review | coder → reviewer → tester | | bug_fix | debugger → coder → tester | | refactor | explainer → refactorer → reviewer → tester | | scaffold | planner → code_generator → coder → tester | | optimize | coder → optimizer → reviewer | | error_resilience | error_handler → coder → tester → reviewer | | concurrent_system | architect → concurrency → coder → tester → reviewer |

API & Backend

| Workflow | Pipeline | |----------|----------| | api_build | api_designer → coder → tester → writer | | api_full | api_designer → code_generator → coder → tester → documentation → security | | api_contract | api_designer → contract_tester → tester → documentation | | db_design | planner → database → reviewer | | microservice | architect → api_designer → coder → contract_tester → devops | | full_stack | planner → architect → api_designer → database → coder → tester | | data_pipeline | data_analyst → coder → tester → devops |

DevOps & Infrastructure

| Workflow | Pipeline | |----------|----------| | deploy | devops → security → validator | | production_ready | coder → error_handler → security → performance_tester → devops | | release | tester → security → compliance → documentation → devops | | observability | monitoring → devops → shell_expert | | config_setup | config_manager → devops → validator | | network_setup | networking → security → devops → validator | | git_workflow | git_expert → devops → automator | | shell_automation | shell_expert → automator → tester |

Security & Quality

| Workflow | Pipeline | |----------|----------| | security_audit | coder → security → compliance | | compliance_check | security → compliance → validator | | full_review | explainer → reviewer → security → optimizer → accessibility | | perf_audit | performance_tester → optimizer → reviewer |

Frontend & UX

| Workflow | Pipeline | |----------|----------| | frontend | ux_designer → coder → accessibility → reviewer | | ux_audit | ux_designer → accessibility → reviewer | | seo_optimize | seo_expert → coder → performance_tester |

Documentation & Learning

| Workflow | Pipeline | |----------|----------| | docs | researcher → writer → reviewer | | learn | researcher → mentor → summarizer | | code_explain | explainer → diagrammer → summarizer | | team_onboard | documentation → diagrammer → mentor → explainer | | translate | translator → reviewer → writer |

Planning & Management

| Workflow | Pipeline | |----------|----------| | design | planner → architect → diagrammer | | estimate | planner → estimator → reviewer | | tech_spec | product_manager → architect → api_designer → estimator | | tech_decision | researcher → tech_lead → architect → estimator | | mvp | product_manager → planner → coder → tester | | startup_mvp | product_manager → planner → architect → code_generator → coder → tester → devops |

Migration & Modernization

| Workflow | Pipeline | |----------|----------| | migrate | planner → migrator → tester → reviewer | | legacy_modernize | explainer → architect → migrator → coder → tester |

Incident & Operations

| Workflow | Pipeline | |----------|----------| | incident | debugger → devops → summarizer | | incident_response | debugger → monitoring → devops → summarizer → documentation |

Specialized

| Workflow | Pipeline | |----------|----------| | ml_project | researcher → ml_engineer → coder → tester → documentation | | regex_build | regex_expert → tester → explainer | | prompt_craft | prompt_engineer → tester → optimizer | | interview_prep | researcher → interviewer → mentor | | onboarding | explainer → mentor → diagrammer |

Use Cases with Examples

🚀 Build a New Feature

/workflow full_dev implement user authentication with JWT and refresh tokens

🐛 Fix a Bug

/workflow bug_fix TypeError: Cannot read property 'map' of undefined in UserList.tsx

🏗️ Design a System

/workflow design design a real-time notification system for 100k users

📝 Write Documentation

/workflow docs document the payment processing module with API reference

🔒 Security Review

/files src/auth.py,src/middleware.py security audit these files

⚡ Optimize Performance

/workflow perf_audit our API response time is 2s, analyze and optimize

🎯 Direct Agent Call

/ask coder write a Python decorator for caching with TTL
/ask database design a schema for multi-tenant SaaS
/ask devops write a GitHub Actions CI/CD pipeline for a Node.js app
/ask shell_expert write a bash script to backup PostgreSQL daily

🔄 Iterative Refinement

/feedback coder reviewer write a thread-safe LRU cache in Python

(Coder writes → reviewer critiques → coder improves → until approved)

📊 Compare Approaches

/compare architect,optimizer design a caching strategy for product catalog

⚡ Parallel Execution

/parallel security,optimizer,accessibility audit this React component

📋 Batch Processing

/batch coder implement stack;;implement queue;;implement linked list;;implement BST

🤖 Auto-Routing

/auto our login endpoint is returning 500 errors in production

(Automatically selects bug_fix workflow)

📁 Multi-File Analysis

/files src/api.py,src/models.py,src/tests.py review for consistency issues

🎓 Learning

/workflow learn explain event-driven architecture with examples
/ask mentor explain the CAP theorem like I'm a junior developer

🚢 Production Release

/workflow release prepare v2.0 release for the payment service

🏢 Full Startup MVP

/workflow startup_mvp build a SaaS invoicing app with Stripe integration

MCP Client Integration

Claude Desktop

Add to ~/.config/claude/claude_desktop_config.json (Linux) or ~/Library/Application Support/Claude/claude_desktop_config.json (macOS):

{
  "mcpServers": {
    "multi-agent": {
      "command": "/home/jaspal/jscode/js-ai-apps-api/multi-agent-app/.venv/bin/python",
      "args": ["/home/jaspal/jscode/js-ai-apps-api/multi-agent-app/main.py", "--mcp-server"]
    }
  }
}

Cursor

Add to Cursor MCP settings:

{
  "multi-agent": {
    "command": "/home/jaspal/jscode/js-ai-apps-api/multi-agent-app/.venv/bin/python",
    "args": ["main.py", "--mcp-server"],
    "cwd": "/home/jaspal/jscode/js-ai-apps-api/multi-agent-app"
  }
}

VS Code (Copilot MCP)

{
  "mcp": {
    "servers": {
      "multi-agent": {
        "command": "python",
        "args": ["main.py", "--mcp-server"],
        "cwd": "/home/jaspal/jscode/js-ai-apps-api/multi-agent-app"
      }
    }
  }
}

Any MCP Client (SSE/HTTP)

# Start HTTP server
python main.py --mcp-server --transport sse --port 8080

# Connect from any MCP client to: http://localhost:8080/sse

Advanced Features

🔄 Feedback Loop

Iteratively refines output until approved:

/feedback coder reviewer write a production-ready connection pool

Agent writes → reviewer evaluates → agent improves → repeat (max 3 rounds).

🧠 Persistent Memory

Notes that survive across sessions:

/remember project Using PostgreSQL 15 with pgvector extension
/remember style snake_case, type hints, 4-space indent
/recall project
/forget project

⚡ Parallel Execution

Multiple agents simultaneously:

/parallel security,optimizer,reviewer analyze this module

📦 Batch Processing

Same agent, multiple tasks:

/batch tester write tests for login;;signup;;logout;;password-reset

🎯 Auto-Routing

Keyword-based workflow selection:

/auto deploy our app to kubernetes with monitoring

📁 Multi-File Context

Cross-file analysis:

/files src/api.py,src/models.py,tests/test_api.py find inconsistencies

🔀 Custom Chains

Build pipelines on the fly:

/chain planner|architect|coder|tester|documentation build a rate limiter

💾 Session Persistence

/save my-project
/load my-project
/export

📊 Token Tracking

/tokens
# Output: Tokens: ~12,450 total (4,200 in / 8,250 out) | Requests: 7

Configuration Reference

config.py

| Setting | Default | Description | |---------|---------|-------------| | OLLAMA_BASE_URL | http://localhost:11434 | Ollama API endpoint | | MODEL_NAME | llama3.1:8b | Default model | | TEMPERATURE | 0.7 | Default temperature | | MAX_TOKENS | 2048 | Max response tokens | | MCP_SERVER_NAME | MultiAgentSystem | MCP server name | | MCP_SERVER_TRANSPORT | stdio | Default transport | | MCP_SSE_HOST | 0.0.0.0 | SSE bind address | | MCP_SSE_PORT | 8080 | SSE port | | MCP_REQUEST_TIMEOUT | 300 | Request timeout (seconds) | | EXTERNAL_MCP_SERVERS | [] | External local MCP servers |

Environment Variables (Ollama)

export OLLAMA_HOST=0.0.0.0:11434   # Bind address
export OLLAMA_NUM_GPU=999           # GPU layers
export OLLAMA_NUM_THREAD=8          # CPU threads
export OLLAMA_KEEP_ALIVE=5m         # Model keep-alive time

Project Structure

multi-agent-app/
├── agent_registry.py   # 45 agent definitions (single source of truth)
├── config.py           # All settings + supervisor prompt
├── graph.py            # LangGraph supervisor orchestration
├── runners.py          # Execution modes + 50 workflows + advanced features
├── session.py          # History, tokens, save/load/export
├── main.py             # CLI dispatcher + entry point
├── mcp_server.py       # FastMCP server (dynamic tool registration)
├── tool_registry.py    # External MCP server consumption
├── requirements.txt    # Python dependencies
└── sessions/           # Saved sessions + agent memory
    ├── *.json          # Session files
    ├── *.md            # Exported conversations
    └── memory.json     # Persistent agent memory

Extending the System

Add a New Agent

Add one entry to agent_registry.py:

"my_agent": {
    "description": "What it does",
    "temperature": 0.3,
    "prompt": "You are a ... agent. Your job is to: 1) ... 2) ... 3) ...",
},

Automatically available in: supervisor routing, /ask, /chain, MCP tools.

Add a New Workflow

Add to WORKFLOWS in runners.py:

"my_workflow": ["planner", "my_agent", "reviewer", "tester"],

Add External MCP Server

Add to EXTERNAL_MCP_SERVERS in config.py:

{"name": "my-server", "transport": "stdio", "command": "python", "args": ["my_server.py"]}

Troubleshooting

Ollama not reachable

# Check if running
curl http://localhost:11434/api/tags

# Start it
ollama serve

Model not found

# List available models
ollama list

# Pull the model
ollama pull llama3.1:8b

Slow responses

  • Use a smaller model: /model mistral or /model phi3:mini
  • Reduce MAX_TOKENS in config.py
  • Use GPU: install CUDA/ROCm drivers

Out of memory

  • Use smaller model: llama3.1:8b instead of 13b/70b
  • Close other applications
  • Set OLLAMA_NUM_GPU=0 to use CPU only (slower but less RAM)

Import errors

# Make sure venv is activated
source .venv/bin/activate

# Reinstall dependencies
pip install -r requirements.txt

Requirements

  • Python 3.12+
  • Ollama (any version)
  • 8GB+ RAM (16GB recommended)
  • No GPU required
  • No internet after initial setup

Python Dependencies

langchain>=0.3.0
langchain-ollama>=0.2.0
langgraph>=0.2.0
pydantic>=2.0.0
mcp>=1.0.0
requests>=2.28.0

License

MIT

Quick Setup
Installation guide for this server

Install Package (if required)

uvx agentic-mcp-server

Cursor configuration (mcp.json)

{ "mcpServers": { "jsxtech-agentic-mcp-server": { "command": "uvx", "args": [ "agentic-mcp-server" ] } } }
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