Universal protocol for connecting AI agents to tools, databases, and APIs. Build once, use with any LLM (Claude, GPT-4, Llama). 10x faster than custom integrations.
Stop building custom integrations for every LLM. MCP gives you universal AI connectivity.
The Pain: LLMs (Claude, GPT-4, Llama) can't interact with your databases, APIs, file systems. Building custom integrations for each LLM platform (OpenAI function calling, Anthropic tool use, LangChain, AutoGen) takes weeks. Every new tool requires separate implementations for each LLM. Fragmented ecosystem with no standardization.
MCP Solution: Model Context Protocol (MCP) provides a universal standard for connecting any LLM to any tool. One MCP server works with Claude, GPT-4, Llama, and any MCP-compatible client. Add new tools once, use everywhere. Build your tool ecosystem in days, not months.
The Pain: Building agentic AI workflows requires orchestrating multiple LLMs, each needing access to different tools. Agents can't share context or collaborate. Custom message passing, state management, tool routing for each agent. Complex architecture with brittle integrations. Development takes 12-20 weeks.
MCP Solution: MCP enables standardized agent-to-tool and agent-to-agent communication. Shared tool ecosystem across all agents. Centralized context management. Event-driven architecture with bidirectional streaming. Reduce multi-agent development from months to 6-8 weeks.
The Pain: OpenAI function calling only works with OpenAI models. Anthropic tool use locked to Claude. Switching from GPT-4 to Llama requires rewriting all tool integrations. Migrating 50 tools takes 200-400 hours ($20K-$80K in dev costs). Can't run hybrid (OpenAI + self-hosted Llama) without maintaining 2 codebases.
MCP Solution: MCP abstracts away LLM-specific implementations. Write tools once using MCP, swap LLM providers without code changes. Run OpenAI, Anthropic, Llama, DeepSeek simultaneously using same tool ecosystem. Zero migration cost when switching models. True LLM portability.
The Pain: LLMs accessing databases, file systems, APIs pose security risks. No granular permission controls (all-or-nothing tool access). Hard to audit what LLMs are doing. Compliance violations (HIPAA, SOC2) when LLMs access sensitive data. Can't enforce rate limits, data masking, or access policies per tool.
MCP Solution: MCP servers implement enterprise-grade auth & authorization. Role-based access control (RBAC) per tool per user. Audit logging for every LLM tool call. Data masking, redaction, sandboxing. Rate limiting, quota management. Full HIPAA/SOC2 compliance. Security layer between LLMs and sensitive systems.
Production-ready MCP servers, clients, and tools for universal AI connectivity
See how MCP solves complex AI integration challenges across industries
Need LLM to answer questions from 10 internal systems (Confluence, Jira, Salesforce, databases, Google Drive). Building custom integrations for each LLM platform takes 16-20 weeks. Can't switch from GPT-4 to Llama without rewriting everything.
Python MCP Server + 10 MCP tools (Confluence, Jira, Salesforce, PostgreSQL, Google Drive, etc.) + any LLM client (Claude, GPT-4, Llama)
Self-hosted MCP server (Docker + Kubernetes)
6-8 weeks (vs 16-20 weeks custom per LLM)
User question → LLM analyzes → Calls relevant MCP tools (search Confluence, query DB, read Drive files) → LLM synthesizes answer
Single MCP implementation works with ANY LLM. Switch from GPT-4 to Llama in 1 day (just swap client). Add new tools once, use everywhere.
Need 3 specialized agents: Research Agent (web search, docs), Analysis Agent (run Python/R scripts), Execution Agent (call APIs, deploy). Each agent needs different tools. Hard to coordinate agents, share context, manage state. Custom orchestration layer takes 12-16 weeks.
AutoGen/LangChain + 3 MCP servers (Research tools, Analysis tools, Execution tools) + shared context layer
Kubernetes cluster with 3 MCP server pods + orchestrator
8-10 weeks (vs 12-16 weeks custom orchestration)
Research Agent uses MCP search tools → Passes context to Analysis Agent → Analysis Agent uses MCP Python execution tools → Results to Execution Agent → Execution Agent uses MCP API tools → Complete
Agents share standardized MCP tool ecosystem. Centralized context management. Add/remove agents without rewriting integrations. Parallel agent execution.
Want LLM to manage Kubernetes, deploy apps, analyze logs, fix issues. Need tools for kubectl, Docker, GitHub, Datadog, PagerDuty. Building custom tool integrations for each DevOps platform takes 10-14 weeks. Security risk: LLM has too much access.
Go MCP Server (high performance) + DevOps tools (Kubernetes API, Docker, GitHub Actions, Datadog) + OAuth2 + RBAC
On-premise (security) with API gateway, rate limiting, audit logging
8-12 weeks (includes security hardening)
LLM receives alert → Checks permissions → Calls MCP tools (kubectl get pods, analyze Datadog metrics) → Proposes fix → Human approval → Executes (kubectl apply)
Fine-grained RBAC (LLM can read logs but not delete pods). Full audit trail. Rate limiting prevents runaway tool calls. Human-in-the-loop for dangerous operations.
Support chatbot needs access to Zendesk, Salesforce, Intercom, product docs. Each platform has different auth, APIs. Building integrations for each LLM (OpenAI, Claude, self-hosted Llama) means 3x work. Want to A/B test GPT-4 vs Llama 70B.
TypeScript MCP Server + Support tools (Zendesk, Salesforce, Intercom, doc search) + multi-LLM client support
Cloud-hosted with CDN (global low latency)
6-8 weeks (single MCP server for all LLMs)
Customer message → LLM (GPT-4 or Llama) calls MCP tools (search Zendesk tickets, query Salesforce customer data, retrieve help docs) → Generates response
A/B test GPT-4 vs Llama with zero tool rewrite. Add new support platforms as MCP tools. Reduce integration cost by 70%. Switch LLMs based on cost/performance.
Quant analysts need LLM to query Bloomberg API, run backtests (Python), access internal databases, generate PDFs. Compliance requires audit trail for every AI action. Custom integrations not HIPAA/SOC2 compliant. Takes 14-18 weeks.
Rust MCP Server (ultra-secure, fast) + Financial tools (Bloomberg API, Python backtesting, PostgreSQL, PDF generation) + audit logging + data masking
On-premise (compliance) with SOC2-certified infrastructure
10-14 weeks (includes compliance setup)
Analyst query → LLM calls MCP tools (Bloomberg API for data, Python for backtest, DB for historical data) → All calls logged + audited → PDF report generated
Full audit trail (every LLM tool call logged with timestamp, user, params). Data masking (PII redacted). SOC2/HIPAA compliant. Regulatory-ready AI.
Need LLM to read product data from Shopify/WooCommerce, generate SEO descriptions, update catalogs, process images. Custom integrations for each e-commerce platform take 8-12 weeks. Want to scale to 10K products/day.
Go MCP Server (high performance) + E-commerce tools (Shopify API, WooCommerce, image processing, S3) + batch processing
Cloud with auto-scaling (handle spikes)
6-8 weeks (optimized for throughput)
Batch job → LLM reads product from Shopify MCP tool → Generates description → Updates via Shopify MCP tool → Processes images → Uploads to S3
Process 10K products/day (5x faster than custom). Scale horizontally (add MCP server instances). Sub-100ms latency per tool call. Cost: $0.10/product (vs $0.50 custom).
How we choose the right MCP architecture for your needs
| Criteria | Simple | Moderate | Complex |
|---|---|---|---|
| Number of Tools | 1-5 tools: Single MCP server | 5-20 tools: Modular MCP servers (by category) | >20 tools: Microservices architecture (one MCP server per tool type) |
| Performance Requirements | <100 requests/min: Python/TypeScript (FastAPI, Express) | 100-1K requests/min: Go (native performance) | >1K requests/min: Rust or Go + load balancing + caching |
| Security & Compliance | Internal use: API keys, basic auth | External/multi-tenant: OAuth2, JWT, RBAC | HIPAA/SOC2: On-premise + audit logging + encryption + data masking |
| LLM Diversity | One LLM (Claude or GPT-4): Use official MCP client | 2-3 LLMs (Claude + GPT-4 + Llama): MCP adapter layer | 5+ LLMs or custom: Build universal MCP client wrapper |
| Integration Complexity | Standard APIs (REST): Use existing MCP tool libraries | Mixed APIs (REST + GraphQL + DB): Custom MCP tools | Legacy systems + custom protocols: MCP server with adapters |
MCP-powered AI agent ecosystems across industries
Challenge: Developers want AI to write code, run tests, deploy to cloud, analyze logs. Need integrations with GitHub, Docker, Kubernetes, AWS, Datadog. Each LLM platform requires custom tool implementations.
MCP Solution: DevOps MCP Server with 15+ development tools. LLMs can git clone, run tests, deploy containers, query logs - all via standardized MCP. Works with Claude, GPT-4, Llama.
MCP Tools: GitHub API, Docker, Kubernetes, AWS CLI, Terraform, Datadog, PagerDuty, Jira, Slack
Developers use any LLM (Claude, GPT-4, Cursor, Llama) with same tool ecosystem. 80% faster feature delivery. Zero-downtime deployments via AI.
Challenge: Support teams need AI to access Zendesk, Salesforce, Intercom, knowledge base. Want to switch between GPT-4 (quality) and Llama 70B (cost) based on query complexity. Custom integrations lock them into one LLM.
MCP Solution: Support MCP Hub with multi-LLM routing. High-complexity queries → GPT-4. Simple queries → Llama 70B. All via same MCP tool set.
MCP Tools: Zendesk, Salesforce, Intercom, Confluence, Google Drive, PostgreSQL (customer data)
60% cost reduction (Llama for 70% of queries). Same resolution quality. A/B test LLMs without re-implementing tools.
Challenge: Quants need AI to query Bloomberg, run backtests (Python/R), access databases, generate reports. Regulatory compliance requires audit trail. Custom integrations not SOC2 certified. Takes 14-18 weeks.
MCP Solution: Compliant Financial MCP Server with audit logging. Every LLM tool call logged (timestamp, user, params, result). Data masking for PII. SOC2/HIPAA ready.
MCP Tools: Bloomberg API, Alpha Vantage, Python/R execution, PostgreSQL, PDF generation, Excel integration
Full regulatory compliance. Audit trail for every AI action. Reduce analysis time by 70%. Analysts work 5x faster.
Challenge: Doctors need AI to search patient records (EMR), medical literature, drug databases. HIPAA compliance critical. LLMs can't directly access PHI. Custom HIPAA integrations cost $50K-$100K.
MCP Solution: HIPAA-Compliant Medical MCP Server. On-premise deployment. PHI data masked before LLM sees it. Audit logging. BAA-ready.
MCP Tools: EMR (Epic, Cerner), PubMed, DrugBank, ICD-10 database, lab results (HL7/FHIR)
Doctors get AI assistance without HIPAA violations. 50% faster diagnosis. Full audit trail for compliance. PHI never leaves premises.
Challenge: E-commerce teams need AI to update product catalogs (Shopify, WooCommerce), generate SEO descriptions, process images, sync inventory. Scaling to 10K products/day requires high-throughput integrations.
MCP Solution: High-Performance E-commerce MCP Server (Go). Batch processing, async I/O, auto-scaling. Process 10K products/day with sub-100ms latency.
MCP Tools: Shopify API, WooCommerce, BigCommerce, image processing (DALL-E/Stable Diffusion), S3, inventory DBs
Process 10K products/day (5x faster). 90% cheaper than manual content creation. Auto-scale during peak seasons.
Challenge: Law firms need AI to search case law, analyze contracts, draft documents. LegalTech tools fragmented (LexisNexis, Westlaw, Clio). Want to switch between Claude (nuanced reasoning) and GPT-4 (speed) based on task.
MCP Solution: Legal MCP Hub with multi-LLM support. Complex legal reasoning → Claude. Contract extraction → GPT-4. All via same MCP tool ecosystem.
MCP Tools: LexisNexis, Westlaw, Clio, contract databases, document management (NetDocuments), e-discovery
Lawyers 3x more productive. Switch LLMs without tool rewrite. Reduce legal research time by 60%. Cost savings: $100K/year per firm.
Fixed-price MCP integration packages based on scope
Everything you need for production-ready MCP integration
Everything you need to know about MCP integration
MCP is a universal standard protocol for connecting LLMs (Claude, GPT-4, Llama, etc.) to tools, databases, and APIs. WHY YOU NEED IT: Without MCP, you must build custom integrations for EACH LLM platform separately. OpenAI has "function calling", Anthropic has "tool use", LangChain has its own system - all incompatible. If you have 10 tools and want to support 3 LLMs, that's 30 separate implementations. WITH MCP: Build each tool once as an MCP server. Any MCP-compatible LLM client can use it. Add new tools → all LLMs get access. Switch from GPT-4 to Llama → zero code changes. BENEFITS: (1) 10x faster integration, (2) LLM portability (no vendor lock-in), (3) Standardized security/auth, (4) Easier multi-agent coordination. You need MCP if: Building AI agents that access tools/data, Integrating multiple LLMs, Planning multi-agent systems, Want to avoid vendor lock-in.
OPENAI FUNCTION CALLING: Only works with OpenAI models (GPT-4, GPT-3.5). Switching to Claude or Llama requires complete rewrite. Vendor lock-in. ANTHROPIC TOOL USE: Only works with Claude models. Can't use tools with GPT-4 or Llama without separate implementation. LANGCHAIN: Framework-specific. Tools written for LangChain don't work with native OpenAI/Anthropic clients. Adds abstraction layer. MCP: UNIVERSAL STANDARD. Tools work with ANY MCP-compatible client (Claude, GPT-4, Llama, custom LLMs). Write once, use everywhere. No vendor lock-in. MIGRATION EXAMPLE: You built 20 tools for OpenAI function calling. Now want to switch to Llama 70B (cheaper). With OpenAI: Rewrite all 20 tools for Llama. 200-400 hours ($20K-$80K). With MCP: Write 20 tools as MCP servers once. Swap LLM client (Claude → GPT-4 → Llama) in 1 day. Zero rewrite. RECOMMENDATION: Use MCP if you value portability, plan to use multiple LLMs, or want future-proof architecture. Use native function calling if locked into one LLM forever.
MCP can integrate with ANYTHING that has an API or can be accessed programmatically. DATABASES: PostgreSQL, MySQL, MongoDB, Redis, Elasticsearch, ChromaDB, Pinecone (any SQL/NoSQL/vector DB). FILE SYSTEMS: Local files, S3, Google Cloud Storage, Azure Blob, Google Drive, SharePoint, Dropbox. APIs: REST APIs (Salesforce, Zendesk, GitHub, Slack, any HTTP API), GraphQL, gRPC, SOAP (yes, even legacy). CODE EXECUTION: Python scripts, Bash commands, Docker containers, Kubernetes jobs, AWS Lambda. ENTERPRISE SYSTEMS: CRMs (Salesforce, HubSpot), ERPs (SAP, Oracle), Help Desks (Zendesk, Intercom, Jira), Document Management (SharePoint, Box). CUSTOM/LEGACY SYSTEMS: If it has an API or command-line interface, we can wrap it in MCP. Custom protocols, proprietary systems, mainframes (via API gateway). EXAMPLES WE'VE BUILT: Bloomberg API (finance), Epic EMR (healthcare), Shopify (e-commerce), Kubernetes API (DevOps), proprietary trading systems. If you can call it from Python/Go/Node.js, we can make it an MCP tool. We handle auth, rate limiting, error handling, retries, caching.
LLMs accessing databases, APIs, file systems is a MAJOR security concern. We implement multi-layer security: (1) AUTHENTICATION - Who is the user? OAuth2, SAML, SSO, API keys, JWT tokens. User identity verified before any tool access. (2) AUTHORIZATION (RBAC) - What can this user do? Role-based permissions per tool. Example: Junior analyst can READ database, not DELETE. Admin can deploy Kubernetes, analyst can only VIEW. (3) DATA MASKING - Redact sensitive data before LLM sees it. PHI (healthcare), PII (personal data), financial account numbers masked with [REDACTED]. LLM never sees raw sensitive data. (4) AUDIT LOGGING - Every LLM tool call logged: timestamp, user, tool name, parameters, response. Full audit trail for compliance (HIPAA, SOC2, GDPR). (5) RATE LIMITING - Prevent runaway LLM tool usage. Max 100 API calls/minute per user. Quota management (1,000 DB queries/day). (6) SANDBOXING - Tools run in isolated containers (Docker). File system access sandboxed (can't access /etc or system files). Python execution in restricted environment (no os.system). (7) HUMAN-IN-THE-LOOP - Dangerous operations require approval. Example: LLM can PROPOSE "kubectl delete pod" but needs human approval to execute. COMPLIANCE: We've built HIPAA-compliant (healthcare), SOC2-certified (finance), GDPR-ready MCP systems. Full encryption (TLS 1.3, AES-256), zero-trust architecture.
MCP works with BOTH cloud LLMs (OpenAI, Anthropic) AND self-hosted LLMs (Llama, Qwen, DeepSeek, Mistral, custom models). CLOUD LLMs: Claude Desktop (official MCP client from Anthropic), OpenAI GPT-4 via MCP bridge/adapter, Anthropic API + MCP integration. SELF-HOSTED LLMs: Llama 4 (8B-405B), Qwen3 (14B-72B), DeepSeek-R1 (7B-70B), Mistral (7B-22B), ANY open-source LLM. You need to build or use an MCP CLIENT for self-hosted LLMs. We provide this as part of our service. EXAMPLE ARCHITECTURE: (1) Deploy Llama 4 70B on your server (vLLM, TensorRT), (2) We build MCP client wrapper (Python/Go) that connects Llama to MCP servers, (3) Llama can now use all your MCP tools (database, APIs, file system). BENEFITS OF SELF-HOSTED + MCP: Zero API fees (Llama is free), Data privacy (LLM runs on-premise, data never leaves), Same tool ecosystem as cloud LLMs (write tools once, use with Llama OR GPT-4), Cost savings (Llama 70B ~$2-5 per 1M tokens vs GPT-4 ~$30 per 1M). HYBRID APPROACH: Use Llama 70B (self-hosted) for 80% of queries (cheap), GPT-4 (cloud) for 20% complex queries (quality). Both use same MCP tools. We help you build the MCP client integration for self-hosted LLMs. Timeline: +2 weeks for custom LLM client vs using Claude Desktop (native MCP).
COST COMPARISON - Custom LLM Integrations vs MCP: SCENARIO: You need 10 tools (database, 3 APIs, file system, 5 custom tools) and want to support 3 LLMs (GPT-4, Claude, Llama). CUSTOM APPROACH: Build 10 tools for OpenAI function calling: 10 tools × 20 hours/tool = 200 hours ($20K at $100/hour). Build 10 tools for Anthropic tool use: 200 hours ($20K). Build 10 tools for Llama (custom): 200 hours ($20K). TOTAL: 600 hours, $60K. Timeline: 16-20 weeks (sequential development). Switching LLMs later: Another 200 hours ($20K) per new LLM. MCP APPROACH: Build 10 tools as MCP servers ONCE: 10 tools × 16 hours/tool = 160 hours ($16K). Build MCP clients for 3 LLMs: 3 clients × 20 hours = 60 hours ($6K). TOTAL: 220 hours, $22K. Timeline: 8-10 weeks (parallel development). Switching LLMs later: 0 hours (just swap MCP client). SAVINGS: $38K (63% cheaper) + 8-10 weeks faster. ROI gets better with more tools/LLMs: 20 tools × 5 LLMs: Custom = $200K, MCP = $40K (80% savings). OUR PRICING: Simple MCP (3-5 tools, 1 LLM): $12K, Production MCP (10-15 tools, multi-LLM): $28K, Enterprise MCP (25+ tools, multi-agent): $65K. Break-even: If you plan to support 2+ LLMs or have 10+ tools, MCP is always cheaper + faster.
Yes. MCP supports BOTH request/response (low latency) AND streaming (real-time updates). STREAMING: MCP uses Server-Sent Events (SSE) and WebSockets for bidirectional streaming. Use cases: (1) Real-time log analysis (LLM streams logs from Kubernetes, analyzes live), (2) Live data feeds (stock prices, IoT sensors → LLM processes in real-time), (3) Interactive coding (LLM generates code, streams output as it types). PERFORMANCE: We optimize MCP servers for high throughput: (1) Go/Rust servers: Sub-10ms latency per tool call, >1,000 requests/second per server, (2) Caching (Redis): Reduce duplicate tool calls by 70%, cache frequently-accessed data, (3) Load balancing: Deploy multiple MCP server instances behind load balancer, auto-scale based on traffic, (4) Async I/O: Non-blocking operations, parallel tool execution. BENCHMARKS: Python MCP server (FastAPI): 200-500 req/sec, latency 20-50ms. Go MCP server: 1,000-2,000 req/sec, latency 5-15ms. Rust MCP server: 2,000-5,000 req/sec, latency 2-10ms. SCALING: For ultra-high throughput (>10K req/sec): Kubernetes with 10-20 MCP server replicas, Service mesh (Istio) for advanced routing, Distributed caching (Redis Cluster), CDN for global low-latency. We've built MCP systems processing 100K+ tool calls/day (e-commerce product enrichment) with p99 latency <100ms.
Yes! MCP is PERFECT for multi-agent systems. It solves the two biggest challenges: (1) Tool sharing across agents, (2) Agent-to-agent communication. MULTI-AGENT ARCHITECTURE: Each agent (Research Agent, Analysis Agent, Execution Agent) connects to shared MCP tool ecosystem. Agents call MCP tools as needed. No duplicate tool implementations. AGENT COORDINATION: Option 1 - CENTRALIZED ORCHESTRATOR: Orchestrator (AutoGen, LangChain, custom) manages agent workflow. Agents communicate via orchestrator. MCP tools shared across all agents. Option 2 - MCP-BASED MESSAGING: Agents communicate via MCP "message" tools. Agent A calls MCP tool "send_message_to_agent_B". Agent B receives via MCP "get_messages" tool. Decentralized coordination. SHARED CONTEXT: Store shared context in MCP-accessible database (Redis, PostgreSQL). All agents read/write context via MCP tools. Centralized state management. EXAMPLE WORKFLOW: Research Agent calls MCP search tool → Finds data → Writes to shared context (MCP DB tool), Analysis Agent reads shared context (MCP tool) → Calls MCP Python execution tool → Runs analysis, Execution Agent reads results → Calls MCP API tool → Deploys to production. BENEFITS: (1) No custom inter-agent protocols, (2) All agents use same MCP tools, (3) Easy to add/remove agents, (4) Centralized monitoring (all tool calls logged). FRAMEWORKS WE INTEGRATE WITH: AutoGen (Microsoft), LangChain/LangGraph, CrewAI, Custom orchestrators. Timeline: Multi-agent MCP system = 8-12 weeks (vs 16-20 weeks custom).
Let's connect your LLMs to any tool, database, or API with the Model Context Protocol. Universal AI connectivity starts here.
