Anthropic Mcp

Anthropic Model Context Protocol (MCP) Integration

The Model Context Protocol (MCP) is an open standard developed by Anthropic for connecting AI assistants to external data sources and tools. OSSA provides seamless integration with MCP, enabling you to build agents that leverage MCP servers while maintaining portability and standardization.

Overview

Model Context Protocol enables:

Standardized Tool Exposure: Connect agents to databases, APIs, and file systems
Context Sharing: Provide agents with relevant context from multiple sources
Claude Integration: Native support for Claude models with MCP tools
Server Ecosystem: Access to growing library of MCP servers
Bidirectional Communication: Agents can both consume and provide MCP resources

OSSA brings orchestration and standardization to MCP-enabled agents, allowing you to define complex multi-agent workflows with MCP tool access.

Key Benefits

Unified Agent Definitions: Define MCP-enabled agents using OSSA manifests
Multi-Server Support: Connect to multiple MCP servers simultaneously
Cross-Framework Compatibility: Use MCP tools with any OSSA-compatible runtime
Version Control: Track MCP configurations alongside agent definitions
Orchestration: Coordinate multiple MCP-enabled agents

Getting Started

Prerequisites

Node.js 18+ (for MCP servers)
Claude API access (optional, for Claude integration)
OSSA-compatible runtime
MCP server packages

Installation

# Install MCP server packages
npm install -g @modelcontextprotocol/server-filesystem
npm install -g @modelcontextprotocol/server-github
npm install -g @modelcontextprotocol/server-postgres

# Install OSSA runtime
pip install ossa-runtime

Basic Setup

Create an OSSA manifest with MCP integration:

# agents/mcp-assistant/manifest.ossa.yaml
ossa_version: "1.0"
agent:
  name: "MCP-Enabled Assistant"
  version: "1.0.0"
  description: "AI assistant with MCP tool access"
  
mcp:
  enabled: true
  servers:
    - name: "filesystem"
      type: "mcp_server"
      command: "npx"
      args: ["-y", "@modelcontextprotocol/server-filesystem", "/workspace"]
      description: "Access to local file system"
      
    - name: "github"
      type: "mcp_server"
      command: "npx"
      args: ["-y", "@modelcontextprotocol/server-github"]
      env:
        GITHUB_TOKEN: "{{env.GITHUB_TOKEN}}"
      description: "GitHub repository access"
      
capabilities:
  - name: "file_operations"
    type: "tool"
    provider: "mcp"
    server: "filesystem"
    tools:
      - read_file
      - write_file
      - list_directory
      
  - name: "github_operations"
    type: "tool"
    provider: "mcp"
    server: "github"
    tools:
      - create_issue
      - list_pull_requests
      - get_file_contents
      
  - name: "reasoning"
    type: "llm"
    provider: "anthropic"
    model: "claude-3-5-sonnet-20241022"
    
tasks:
  - id: "analyze_repository"
    name: "Analyze Code Repository"
    input:
      repository: "string"
    actions:
      - type: "mcp_tool_call"
        server: "github"
        tool: "list_files"
        parameters:
          repository: "{{input.repository}}"
          
      - type: "claude_analyze"
        prompt: "Analyze this repository structure and suggest improvements"
        tools:
          - filesystem
          - github

Run the agent:

ossa run agents/mcp-assistant/manifest.ossa.yaml \
  --input '{"repository": "owner/repo"}'

Use Cases

1. Code Analysis Agent with MCP

Build an agent that analyzes code using multiple MCP servers:

ossa_version: "1.0"
agent:
  name: "Code Analyzer"
  version: "1.0.0"
  
mcp:
  enabled: true
  servers:
    - name: "filesystem"
      type: "mcp_server"
      command: "npx"
      args: ["-y", "@modelcontextprotocol/server-filesystem", "."]
      
    - name: "git"
      type: "mcp_server"
      command: "npx"
      args: ["-y", "@modelcontextprotocol/server-git"]
      
capabilities:
  - name: "code_analysis"
    type: "llm"
    provider: "anthropic"
    model: "claude-3-5-sonnet-20241022"
    
tasks:
  - id: "analyze_codebase"
    name: "Analyze Codebase"
    actions:
      - type: "mcp_tool_call"
        server: "git"
        tool: "get_recent_commits"
        parameters:
          count: 10
          
      - type: "mcp_tool_call"
        server: "filesystem"
        tool: "list_directory"
        parameters:
          path: "src/"
          recursive: true
          
      - type: "claude_generate"
        prompt: |
          Analyze this codebase:
          
          Recent commits: {{commits}}
          File structure: {{file_list}}
          
          Provide:
          1. Code quality assessment
          2. Architecture review
          3. Security concerns
          4. Refactoring suggestions
        tools:
          - filesystem
          - git
        max_tokens: 4096

2. Database Query Agent

Create an agent that interacts with databases via MCP:

ossa_version: "1.0"
agent:
  name: "Database Assistant"
  version: "1.0.0"
  
mcp:
  enabled: true
  servers:
    - name: "postgres"
      type: "mcp_server"
      command: "npx"
      args: ["-y", "@modelcontextprotocol/server-postgres"]
      env:
        DATABASE_URL: "{{env.DATABASE_URL}}"
        
capabilities:
  - name: "database_operations"
    type: "tool"
    provider: "mcp"
    server: "postgres"
    
  - name: "query_generation"
    type: "llm"
    provider: "anthropic"
    model: "claude-3-5-sonnet-20241022"
    
tasks:
  - id: "natural_language_query"
    name: "Execute Natural Language Query"
    input:
      question: "string"
    actions:
      - type: "mcp_tool_call"
        server: "postgres"
        tool: "get_schema"
        
      - type: "claude_generate"
        prompt: |
          Given this database schema:
          {{schema}}
          
          Generate a SQL query to answer: {{input.question}}
        output: "sql_query"
        
      - type: "mcp_tool_call"
        server: "postgres"
        tool: "execute_query"
        parameters:
          query: "{{sql_query}}"
          
      - type: "claude_generate"
        prompt: |
          Explain these query results in natural language:
          {{query_results}}

3. Multi-Source Research Agent

Build an agent that gathers information from multiple sources:

ossa_version: "1.0"
agent:
  name: "Research Agent"
  version: "1.0.0"
  
mcp:
  enabled: true
  servers:
    - name: "web"
      type: "mcp_server"
      command: "npx"
      args: ["-y", "@modelcontextprotocol/server-brave-search"]
      env:
        BRAVE_API_KEY: "{{env.BRAVE_API_KEY}}"
        
    - name: "github"
      type: "mcp_server"
      command: "npx"
      args: ["-y", "@modelcontextprotocol/server-github"]
      env:
        GITHUB_TOKEN: "{{env.GITHUB_TOKEN}}"
        
    - name: "filesystem"
      type: "mcp_server"
      command: "npx"
      args: ["-y", "@modelcontextprotocol/server-filesystem", "/research"]
      
capabilities:
  - name: "research"
    type: "llm"
    provider: "anthropic"
    model: "claude-3-5-sonnet-20241022"
    
tasks:
  - id: "comprehensive_research"
    name: "Comprehensive Research"
    input:
      topic: "string"
    actions:
      - type: "mcp_tool_call"
        server: "web"
        tool: "search"
        parameters:
          query: "{{input.topic}}"
          count: 10
        output: "web_results"
        
      - type: "mcp_tool_call"
        server: "github"
        tool: "search_repositories"
        parameters:
          query: "{{input.topic}}"
        output: "github_results"
        
      - type: "claude_generate"
        prompt: |
          Research topic: {{input.topic}}
          
          Web sources: {{web_results}}
          GitHub projects: {{github_results}}
          
          Create a comprehensive research report with:
          1. Overview and key concepts
          2. Current state of the art
          3. Notable projects and implementations
          4. Future directions
        tools:
          - web
          - github
        max_tokens: 8192
        
      - type: "mcp_tool_call"
        server: "filesystem"
        tool: "write_file"
        parameters:
          path: "/research/{{input.topic}}.md"
          content: "{{research_report}}"

4. DevOps Automation Agent

Create an agent for infrastructure management:

ossa_version: "1.0"
agent:
  name: "DevOps Agent"
  version: "1.0.0"
  
mcp:
  enabled: true
  servers:
    - name: "kubernetes"
      type: "mcp_server"
      command: "npx"
      args: ["-y", "@modelcontextprotocol/server-kubernetes"]
      env:
        KUBECONFIG: "{{env.KUBECONFIG}}"
        
    - name: "aws"
      type: "mcp_server"
      command: "npx"
      args: ["-y", "@modelcontextprotocol/server-aws"]
      env:
        AWS_REGION: "us-east-1"
        
    - name: "slack"
      type: "mcp_server"
      command: "npx"
      args: ["-y", "@modelcontextprotocol/server-slack"]
      env:
        SLACK_TOKEN: "{{env.SLACK_TOKEN}}"
        
tasks:
  - id: "monitor_and_alert"
    name: "Monitor Infrastructure"
    schedule: "*/5 * * * *"  # Every 5 minutes
    actions:
      - type: "mcp_tool_call"
        server: "kubernetes"
        tool: "get_pod_status"
        
      - type: "mcp_tool_call"
        server: "aws"
        tool: "get_ec2_instances"
        
      - type: "claude_analyze"
        prompt: |
          Analyze infrastructure health:
          
          Kubernetes pods: {{pod_status}}
          EC2 instances: {{ec2_instances}}
          
          Identify any issues or anomalies.
        
      - type: "mcp_tool_call"
        server: "slack"
        tool: "send_message"
        parameters:
          channel: "#devops-alerts"
          message: "{{health_report}}"
        condition: "{{issues_detected}}"

MCP Server Configuration

Custom MCP Server

Create a custom MCP server for your specific needs:

mcp:
  enabled: true
  servers:
    - name: "custom_api"
      type: "mcp_server"
      command: "node"
      args: ["./mcp-servers/custom-api-server.js"]
      env:
        API_KEY: "{{env.CUSTOM_API_KEY}}"
        API_URL: "https://api.example.com"
      config:
        timeout: 30000
        retry_attempts: 3

Multiple Server Coordination

Use multiple MCP servers together:

tasks:
  - id: "cross_platform_sync"
    name: "Sync Across Platforms"
    actions:
      - type: "mcp_tool_call"
        server: "github"
        tool: "get_issues"
        output: "github_issues"
        
      - type: "mcp_tool_call"
        server: "jira"
        tool: "get_tickets"
        output: "jira_tickets"
        
      - type: "claude_generate"
        prompt: "Compare these issues and identify duplicates or related items"
        context:
          github: "{{github_issues}}"
          jira: "{{jira_tickets}}"
          
      - type: "mcp_tool_call"
        server: "slack"
        tool: "send_message"
        parameters:
          channel: "#sync-report"
          message: "{{comparison_results}}"

Claude Integration

Using Claude with MCP Tools

capabilities:
  - name: "claude_with_tools"
    type: "llm"
    provider: "anthropic"
    model: "claude-3-5-sonnet-20241022"
    
configuration:
  anthropic:
    api_key: "{{env.ANTHROPIC_API_KEY}}"
    max_tokens: 4096
    temperature: 0.7
    
tasks:
  - id: "intelligent_task"
    name: "Execute Intelligent Task"
    actions:
      - type: "claude_generate"
        prompt: "{{user_request}}"
        tools:
          - filesystem
          - github
          - postgres
        tool_choice: "auto"
        system_prompt: |
          You are a helpful assistant with access to multiple tools.
          Use the appropriate tools to complete user requests.

Extended Thinking with MCP

Leverage Claude's extended thinking for complex tasks:

tasks:
  - id: "complex_analysis"
    name: "Complex Analysis with Extended Thinking"
    actions:
      - type: "claude_generate"
        model: "claude-3-7-sonnet-20250219"
        prompt: "{{analysis_request}}"
        extended_thinking: true
        thinking_budget: 10000  # tokens
        tools:
          - filesystem
          - database
          - web_search

Best Practices

Server Lifecycle Management: Properly start and stop MCP servers
Error Handling: Implement retry logic for MCP tool calls
Security: Use environment variables for sensitive credentials
Tool Selection: Only expose necessary tools to agents
Monitoring: Log MCP server interactions for debugging
Version Pinning: Pin MCP server versions for reproducibility
Resource Limits: Set timeouts and rate limits for MCP calls

Troubleshooting

Common Issues

MCP server fails to start:

Check Node.js version (18+ required)
Verify server package is installed
Review environment variables

Tool call errors:

Validate tool parameters match server schema
Check server logs for detailed errors
Verify authentication credentials

Performance issues:

Reduce number of concurrent tool calls
Implement caching for repeated queries
Optimize MCP server configuration

Available MCP Servers

Popular MCP servers you can integrate:

@modelcontextprotocol/server-filesystem: File system access
@modelcontextprotocol/server-github: GitHub API integration
@modelcontextprotocol/server-postgres: PostgreSQL database
@modelcontextprotocol/server-brave-search: Web search
@modelcontextprotocol/server-slack: Slack messaging
@modelcontextprotocol/server-git: Git operations
@modelcontextprotocol/server-kubernetes: Kubernetes management