Model Context
Protocol

Standardizing the connection between AI models and datasets. I build MCP Servers that expose local resources and tools to LLMs securely.

PROTOCOLMCP v1.0
LANGPYTHON / TYPESCRIPT
STATUS● ACTIVE DEV

01 — Official Structure

Standardized Server Layout

A robust MCP server requires a clean separation of concerns. I adhere to the official Python SDK structure, ensuring scalability for tool definitions, resource handling, and prompt templates.

  • ● src/tools: Executable functions
  • ● src/resources: Data contexts
  • ● tests: Integration reliability
project-explorer
my-mcp-server
pyproject.toml
.python-version
README.md
requirements.txt
src
my_mcp_server
__init__.py
server.py
tools
__init__.py
workspace_tools.py
execution_tools.py
resources
__init__.py
prompts
__init__.py
tests
test_tools.py
test_integration.py
conftest.py
examples
client_example.py

Security Protocol (Localhost)

1Transport: Stdio / SSE
2Human-in-the-loop Approval
3Sandboxed Execution

02 — Implementation & Logic

Type-Safe Tool Definitions

Building an MCP server isn't just about exposing functions. It's about defining rigid schemas that the Model can understand without ambiguity. I use the FastMCP framework to decorate Python functions with Pydantic models, automatically generating the JSON-RPC interfaces required by the protocol.

src/server.py
from mcp.server.fastmcp import FastMCP
from pydantic import Field

# Initialize Server
mcp = FastMCP("MyDataEngine")

@mcp.tool()
def query_metrics(
    metric_name: str = Field(..., description="Target metric (e.g. 'dau')"),
    range: str = Field("7d", description="Time range string")
) -> dict:
    """Safely retrieves aggregated metrics from Gold Layer."""
    
    # Internal validation logic
    if metric_name not in ALLOWED_METRICS:
        raise ValueError("Metric restricted via Governance Policy")

    return backend.fetch(metric_name, range)

@mcp.resource("config://pipeline_status")
def get_status() -> str:
    """Exposes real-time pipeline health as a readable resource."""
    return system.check_health()

Note: The @mcp.tool() decorator automatically handles the JSON Schema conversion. When Claude calls query_metrics, the server validates inputs against the Pydantic field definitions before any code executes.

Connected: localhost:8080
v1.0.4
bash
Assistant
█ RAG Enabled█ Tools: 14█ Latency: 45ms

03 — Vector Architecture

Retrieval Engine

Context window limits are no longer the bottleneck; retrieval accuracy is. I implement Hybrid Search architectures that combine dense vector similarity with sparse lexical matching (BM25) to capture both semantic meaning and exact keyword matches.

HNSW Indexing

Hierarchical Navigable Small World graphs implementation for sub-millisecond approximate nearest neighbor (ANN) search across billion-scale vectors.

Re-Ranking Models

Deploying cross-encoder models (e.g. BGE-Reranker) as a second-stage pass to strictly order retrieved chunks by relevance before context injection.

Embedding Strategy

Fine-tuned sentence-transformers on domain-specific corpora to align vector space with enterprise vernacular (e.g. medical or legal taxonomies).

Metadata Filtering

Pre-filtering vector queries using scalar metadata (Time, Author, Category) to drastically reduce search space and improve precision.

04 — Agentic Patterns

Beyond simple chains.
Cognitive loops.

I design autonomous agents utilizing the ReAct (Reasoning + Acting) paradigm. Instead of linear execution, these agents maintain a "Thought" trace, observe tool outputs, and iteratively refine their approach to solve multi-step problems.

  • Plan Formulation & Self-Correction
  • Long-term Memory (Vector Stores)
  • Tool-use Capability with Error Handling
Thought:User asked for Q3 sales. I need to query the database.
Action:sql_tool.execute("SELECT sum(sales) FROM...")
Observation:Error: Table 'sales' not found.
Thought:Ah, schema lookup shows table is 'revenue_q3'. Retrying.

05 — Evaluation & Metrics

RAGAS

A framework to quantify RAG pipeline performance. Measuring 'Faithfulness' and 'Answer Relevancy'.

Score: 0.85

TruLens

Feedback functions to detect hallucinations and bias in real-time. The "RAG Triad" validator.

Score: 0.92

DeepEval

Unit testing for LLMs. Asserting that outputs match expected semantic meaning, not just text.

Score: 0.78

Arize Phoenix

Production observability. Tracing spans, latency breakdown, and token usage cost analysis.

Uptime: 99.9%

06 — Deployment

Containerization Strategy

MCP servers are stateless. I wrap them in optimized Docker images (Distroless/Alpine) to minimize attack surface. These containers are orchestrated via Kubernetes, allowing horizontal scaling based on request queue depth.

DockerfileFROM python:3.11-slim
COPY . /app
RUN pip install uv
CMD ["uv", "run", "server.py"]
Helm ChartapiVersion: v1
kind: Deployment
replicas: 3
strategy: RollingUpdate
Anthropic Claude OpenAPI Python SDK
Model Context Protocol Server Client