Unify AI Agents with Data Sources Using MCP

Often AI agents are isolated from real data. Models don't have access to databases, can't read code on GitHub, or work with tasks in Jira. Model Context Protocol (MCP) — an open standard from Anthropic — solves this problem. It defines a unified protocol between LLM clients (Claude Desktop, Cursor, IDE) and servers that provide resources, tools, and prompts. The key advantage: one MCP server works with any client that supports the standard.

We help set up MCP end-to-end: we use ready-made servers or develop custom ones. Our team has 5+ years of experience and has completed over 50 MCP integrations with a 95% on-time delivery rate. Request a consultation — we'll assess your infrastructure for free. Audit starts at $500; full setup with up to 5 servers from $2,500.

What are the benefits of MCP for development teams?

Without MCP, each AI connection to data requires a custom integration: REST API, parsers, authorization. With MCP, you describe the server once — and any client (Claude Desktop, Cursor, VS Code with Continue) gets access to the tools. Developers stop switching between windows — they ask a question and get an answer based on actual data from PostgreSQL, GitHub, Slack, Jira. Time savings reach up to 2 hours per engineer per day. Teams report up to 60% reduction in integration time. Contact us to discuss how MCP can accelerate your team. Our proven methodology ensures a 95% success rate in first-time setup.

What ready-made MCP servers are available?

Anthropic and the community have prepared dozens of ready-made servers — over 100 options. The most popular ones are listed below:

These servers are connected via a JSON configuration file. For Claude Desktop, it's ~/.claude/claude_desktop_config.json. Example configuration for multiple servers:

{
  "mcpServers": {
    "filesystem": {
      "command": "npx",
      "args": ["-y", "@modelcontextprotocol/server-filesystem", "/Users/user/projects"],
      "description": "Access to file system"
    },
    "postgres": {
      "command": "npx",
      "args": ["-y", "@modelcontextprotocol/server-postgres"],
      "env": {
        "POSTGRES_URL": "postgresql://user:password@localhost:5432/mydb"
      }
    }
  }
}

Our MCP Setup Process

Our approach includes four stages:

1. Audit of current infrastructure — identify which data sources and tools the AI agents need. Consider roles: developers need GitHub and PostgreSQL, analysts need BigQuery, support needs Slack.
2. Configuration design — select ready-made servers or design custom ones. For complex scenarios, we develop an MCP server in Python or TypeScript.
3. Implementation and testing — configure servers, check access from different clients (Claude Desktop, Cursor, API). Ensure security: read-only users, tool restriction via allowed_tools.
4. Documentation and team training — hand over configs, deployment scripts, and instructions. Conduct a workshop on using MCP in daily work.

The entire process takes from 1 to 5 days depending on complexity. Our guaranteed MCP deployment ensures you get a working setup on time, backed by a 30-day satisfaction policy.

From Our Practice: A Case for a Team of 10 Developers

One client — a product team using Claude Desktop and Cursor. Task: give AI access to code in GitHub, to a PostgreSQL database (development), and to tasks in Jira. We set up three servers: filesystem (project folder), postgres (read-only), jira. Result: developers began getting answers to questions like "Which PRs have been waiting for review for more than 2 days?" and "Show the structure of the orders table" without context switching. Time savings — up to 2 hours per engineer per day. MCP turned out to be 2–3 times faster than writing custom scripts for each source.

What's Included in Turnkey MCP Setup

• Ready-made MCP servers — up to 5 servers (PostgreSQL, GitHub, Slack, file system, web search)
• Custom server (optional) — development for non-standard API or protocol
• Client configuration — Claude Desktop, Cursor, VS Code + Continue, custom agent
• Security — token setup, read-only access, tool whitelists
• Documentation — configs, deployment scripts, team instructions
• Training — 1-hour workshop for the team

The full package reduces AI integration costs by 35–40% compared to custom solutions. Pricing for a turnkey solution with up to 5 servers starts at $2,500. Contact us for a precise estimate for your infrastructure.

Comparison of MCP vs Custom Integrations

MCP Architecture

MCP consists of three components:

• MCP Host — client (Claude Desktop, Cursor, IDE, custom agent)
• MCP Server — provides Tools, Resources, Prompts
• Transport — stdio (local servers) or HTTP+SSE (remote servers)

To use MCP via the Anthropic API, the mode betas=["mcp-client-2025-04-04"] is available. Example of connecting a remote server:

import anthropic

client = anthropic.Anthropic()
response = client.beta.messages.create(
    model="claude-opus-4-5",
    max_tokens=4096,
    tools=[{
        "type": "mcp",
        "server": {
            "type": "url",
            "url": "https://mcp.company.com/server",
            "authorization_token": "Bearer mcp-token",
        },
    }],
    messages=[{
        "role": "user",
        "content": "Find all customers with overdue payments in the database"
    }],
    betas=["mcp-client-2025-04-04"],
)

Timeline and Cost

• Setup of ready-made servers — from 1 day (up to 5 servers)
• Custom server — from 3 days (depends on API complexity)
• Turnkey with training — from 5 days, starting at $2,500

Cost is calculated individually after an audit. We provide a preliminary estimate within 1 business day, with no obligation.

MCP vs Custom Integrations: Why MCP Is More Profitable

MCP standardizes the protocol: one server serves all clients. In custom integrations, each bridge is written anew. MCP reduces development time by 2–3 times, and maintenance is reduced to updating one component. Learn more about the standard in the official MCP specification at https://github.com/modelcontextprotocol/specification.

Limitations of MCP

The protocol is actively developing: not all clients support the latest versions, documentation is being updated. But for typical scenarios (access to databases, files, APIs), MCP is stable. Anthropic is expanding the specification, and we track changes and update configurations as part of our support.

LLM Development: Fine-Tuning, RAG, Agents, and Production Deployment

Using GPT‑4 or Claude 3.5 Sonnet through a public API is not a solution — it's just a tool. When the requirement is to "make it like ChatGPT, but on our data," there is a real engineering challenge behind it: from prompt engineering to training a 70B model on your own infrastructure. End-to-end LLM solution development is a complex stack, and we have been doing it for over 5 years. During this time, we have completed over 20 projects in generative AI: from RAG systems for legal departments to custom support agents. Where exactly your task falls depends on data, latency requirements, budget, and how critical confidentiality is.

A typical situation: the client has already tried ChatGPT, but results are unstable — sometimes accurate, sometimes hallucinating. Or they need integration into a corporate portal while complying with security policies. Let's break down each layer of the stack in detail — from RAG to production deployment.

Why Do RAG Systems Break and How to Fix It?

RAG (Retrieval-Augmented Generation) looks simple: find relevant documents, put them in context, get an answer. In practice, it fails in several places.

Chunking without overlap. Classic mistake: chunk_size=512, overlap=0. If the answer lies across two chunks, retrieval won't find either with sufficient confidence. Solution: overlap 15–25% of chunk_size, or better yet, sentence-aware splitting with spaCy or NLTK instead of naive character splitting.

Poor embedder. text-embedding-ada-002 is good for general use, but on legal or medical texts, specialized models like E5-large-v2, BGE-M3, or fine-tuned sentence-transformers on domain data outperform it. Recall@5 differences can be 15–25%.

No re-ranking. Vector search optimizes for speed, not relevance. A cross-encoder re-ranker (ms-marco-MiniLM-L-6-v2, bge-reranker-large) after initial retrieval improves top-3 accuracy with acceptable latency (+50–150ms). This is often more impactful than improving the embedding model.

Hybrid search. Dense vectors alone work poorly on exact queries: names, SKUs, codes. BM25 (sparse) finds exact matches but misses semantics. Hybrid via RRF (Reciprocal Rank Fusion) is the optimal compromise. Qdrant, Weaviate, and pgvector 0.7+ support hybrid search natively.

When to Fine-Tune Instead of Prompt Engineering?

Prompt engineering solves ~70% of LLM adaptation tasks for a domain. The remaining 30% require fine-tuning. Three indicators: the model ignores a specific output format even with detailed prompting; the task requires deep knowledge of specialized vocabulary (medicine, law); you need to significantly reduce token costs by replacing a large model with a smaller specialized one.

LoRA and QLoRA are the standard for SFT. LoRA adds trainable low-rank matrices to attention layers. A typical configuration for Llama-3 8B: r=64, lora_alpha=128, target_modules=["q_proj","v_proj","k_proj","o_proj"] yields ~0.8% trainable parameters, training on one A100 40GB. QLoRA adds 4-bit quantization (NF4) and allows fine-tuning 70B models on two A100 40GB, though speed drops by half compared to bf16.

DPO instead of RLHF. Direct Preference Optimization requires only (chosen, rejected) pairs, not scalar reward signals. DPOTrainer from the trl library (Hugging Face) implements it in a few dozen lines.

Common mistake. A dataset of 500 examples, 5 epochs, validation loss 0.8 — seems fine. But on test, the model degrades on general instructions. Cause: catastrophic forgetting. Solution: add 10–20% general instruction-following examples (Alpaca, FLAN) to the training set to preserve original capabilities.

How to Choose a Base Model: 8B or 70B?

For most tasks, fine-tuning an 8B model is sufficient. 70B is needed when deep reasoning is required or the 8B baseline does not reach the required quality even after fine-tuning. Inference cost for Llama-3 8B via vLLM on A100 is efficient; the exact cost depends on volume.

What Does PagedAttention Bring to Production?

vLLM is the first choice for serving open-source models. PagedAttention is the key technical innovation: KV-cache is managed like virtual memory in an OS, without fragmentation. This yields 2–4x higher throughput compared to naive HuggingFace Transformers inference. The vLLM documentation confirms that continuous batching and PagedAttention are the standard for high-load LLM services.

Typical numbers on A100 80GB for Llama-3 8B (bf16): 400–600 req/s, P50 latency 200–400ms, P99 latency 600–900ms at concurrency 64. For 70B on two A100 with tensor parallelism: 80–120 req/s, P99 latency 1.5–2.5s. AWQ or GPTQ quantization reduces memory consumption by 2x with quality loss within 1–3%.

Multi-Agent Systems

Agents are LLMs with access to tools: search, code execution, API calls, database interaction. Common patterns:

• ReAct (Reason + Act): the model reasons → chooses a tool → observes the result → reasons again. LangChain and LlamaIndex implement it out of the box.
• Multi-agent orchestration: multiple specialized agents with a coordinator on top. Example: coordinator → researcher (search + summarization) → coder (code generation and execution) → critic (verification). Tools: AutoGen (Microsoft), CrewAI, custom implementation on LangGraph.

In production, agent systems are non-deterministic. Essential: guardrails, step limits, logging of each step, human-in-the-loop for critical actions.

How We Work: Stages, Timeline, Deliverables

What Is Included

We deliver:

• Technical documentation (model card, configs, deployment instructions)
• Access to infrastructure (code repository, trained weights)
• 1 month of post-deployment support (consultations, bug fixes)
• Customer team training (2–3 sessions on system operation)

Timeline: basic RAG prototype — 1–2 weeks. Fine-tuning with customer data — 3–6 weeks (including data preparation). Production system with monitoring and retraining — 2–4 months. Cost is calculated individually based on data volume, model complexity, and infrastructure requirements.

We guarantee the quality of the final model with performance benchmarks and ongoing monitoring. Our engineers have hands‑on experience with dozens of production LLM systems.

Want to evaluate your project? Leave a request — we will prepare a preliminary summary within 1–2 business days. Or get a consultation on choosing the approach: RAG, fine-tuning, or hybrid — we will tell you what works best for you. Contact us to discuss your LLM development needs. Schedule a free consultation today.