Day 14: The Anthropic’s MCP as a Data Agent — Use-cases(Part 9)

100 days of Agentic AI: From foundations to Autonomous workflows

MCP’s Primary Use Cases

• Augmenting LLMs with External Data

Example: A chatbot queries the current weather or stock prices in real-time through the MCP server.

• Calling Specialized Tools

Example: A coding agent uses a compile tool or runs unit tests, with results fed back to the LLM for further analysis.

• On-the-Fly Resource Integration

Example: An AI assistant that dynamically fetches documents from a knowledge base (via get_resource) to generate factually grounded answers.

Real-World Implementations

• Anthropic’s Claude leveraging MCP for web browsing or calculator tools.
• Frameworks that integrate external utilities into LLM prompts (similar to OpenAI’s function-calling, but formalized under a protocol).
• Centralized Model Scenario: MCP excels when one server and one LLM/agent coordinate — ideal for simpler or initial deployments.

Architecture Scope

• Single-Agent Focus:
• MCP is optimized for one agent (the LLM) accessing one MCP server at a time.
• This streamlines the architecture but can be limiting if you have multiple independent agents that need to interact with each other.

Authentication Approaches

• Token-Based Auth:
• Commonly uses API keys or OAuth tokens for client–server communications.
• Example: The client includes a valid token in the header for every JSON-RPC call.
• Optional Decentralized Identifiers (DIDs):
• Offers a stronger identity layer, particularly in scenarios requiring secure, verifiable agent identities.
• Example: An enterprise environment might mandate DID signatures for each request, ensuring traceable identity verification.

MCP’s Use-cases specific to Data Lifecycle:

Below is a comprehensive overview of how Model Control Protocol (MCP) can be applied to the data engineering, management, and governance lifecycle — particularly within finance and investment banking contexts. Each phase (data acquisition, generation, storage, distribution, and use) is paired with MCP-relevant examples, sample code snippets, and notes on allied aspects (discoverability, observability, standards, security). This should illustrate how MCP can integrate an LLM (Large Language Model) seamlessly into enterprise data workflows.

1. Data Acquisition

Context & Definition

• Data Acquisition involves collecting raw data from multiple sources (e.g., trading systems, market data feeds, internal CRM systems).
• In finance or investment banking, sources often include Bloomberg terminals, Reuters feeds, internal risk systems, and client transaction logs.

MCP Use Case

• Tools:
• Example: An “ingest_market_data” tool accessible via MCP that retrieves current stock prices, exchange rates, or bond yields.
• The LLM (via MCP) can invoke this tool for real-time or historical data acquisition.
• Resources:
• Predefined common API endpoints or SFTP locations for large data dumps (e.g., daily transaction logs).
• The LLM can call get_resource to fetch data sets from a data lake or an object store.
• Prompts:
• Pre-configured templates for describing the source format, ingestion frequency, and quality checks.
• Example: “Ingest daily trades from trades_YYYYMMDD.csv, parse columns as symbol, price, volume.”

Sample MCP-Like Code

{
  "jsonrpc": "2.0",
  "method": "invoke_tool",
  "params": {
    "tool_name": "ingest_market_data",
    "arguments": {
      "data_source": "bloomberg_feed",
      "symbols": ["AAPL", "TSLA", "GS"],
      "start_date": "2025-06-01",
      "end_date": "2025-06-02"
    }
  },
  "id": 1
}

• The MCP server would handle authentication, call the appropriate ingestion pipeline, and return a status or partial results.

Effectiveness

• Centralized approach: All data acquisition requests funnel through a single MCP endpoint, ensuring consistentlogging, access control, and error handling.
• The LLM can rely on a standard prompt (“Pull data for these symbols over this date range”) without rewriting custom ingestion logic.

2. Data Generation

Context & Definition

• Data Generation covers processes that transform or create new data sets (e.g., risk models, financial analytics, derivative calculations).
• In investment banking, this might include simulated scenarios, risk metrics (VAR, CVA, etc.), or aggregatedsummaries.

MCP Use Case

• Tools:
• A “generate_risk_model” tool that calculates Value-at-Risk (VaR) for a portfolio.
• A “forecast_trends” tool that applies machine learning for predictive analytics.
• Resources:
• Large historical data sets (tick-by-tick quotes) provided as references to feed the generation process.
• Prompts:
• Predefined templates describing which type of analytics or modeling to run (e.g., “Generate a daily P&L simulation for fixed-income desks”).
• Sampling:
• The LLM’s text generation might create metadata or data labeling for newly created data sets (e.g., labeling outliers or anomalies).

Example: Generating a Risk Report

{
  "jsonrpc": "2.0",
  "method": "invoke_tool",
  "params": {
    "tool_name": "generate_risk_model",
    "arguments": {
      "portfolio_id": "FX_OPTIONS_DESK",
      "calculation_type": "VAR",
      "confidence": 0.95,
      "historical_window_days": 250
    }
  },
  "id": 2
}

• The MCP server orchestrates the risk model code in a secure environment, then returns the newly generated data (e.g., an aggregated risk report).

Effectiveness

• Using MCP to standardize data-generation workflows ensures the LLM or any user prompt must specify the same parameters (confidence interval, historical window), making the process repeatable and auditable.

3. Data Storage

Context & Definition

• Data Storage is about where and how data is persisted (data lakes, warehouses, cloud object stores).
• In finance, compliance and retention rules (e.g., SEC or MiFID II) can dictate storage formats and encryption.

MCP Use Case

• Resources:
• The LLM can query or store data in a database through get_resource or put_resource calls, mediated by MCP.
• Example: Storing a newly generated “intra-day risk summary” in an encrypted warehouse.
• Tools:
• “manage_db_schema” tool that modifies table structures.
• “encrypt_and_store” tool ensuring compliance with data protection policies.
• Prompts:
• Predefined instructions for specifying partitioning schemes, data retention intervals, or encryption keys.

Sample Code

{
  "jsonrpc": "2.0",
  "method": "invoke_tool",
  "params": {
    "tool_name": "encrypt_and_store",
    "arguments": {
      "data_label": "risk_summary_Q2_2025",
      "payload": "BASE64_ENCODED_DATA_OR_OBJECT",
      "encryption_key_id": "vault_key_123"
    }
  },
  "id": 3
}

Effectiveness

• Centralized governance: The MCP server can enforce consistent encryption and retention policies whenever the LLM requests data storage — less risk of inconsistent or non-compliant storage.

4. Data Distribution

Context & Definition

• Data Distribution deals with sending data to downstream systems, stakeholders, or external partners.
• In investment banking, you might distribute daily trade blotters, risk updates to front-office teams, or regulatory filings to authorities.

MCP Use Case

• Tools:
• “publish_report” tool that can send a PDF or Excel summary to a distribution list.
• “push_to_regulator_api” tool that calls a compliance endpoint (e.g., for regulatory trade reporting).
• Resources:
• Metadata about recipients, channels (email, SFTP, internal messaging bus).
• Prompts:
• “Send compliance data to regulator with file format X, including these fields…”

Sample Code

{
  "jsonrpc": "2.0",
  "method": "invoke_tool",
  "params": {
    "tool_name": "publish_report",
    "arguments": {
      "report_id": "daily_blotter_20250605",
      "recipients": ["trading_desk@bank.com", "compliance@bank.com"],
      "format": "pdf"
    }
  },
  "id": 4
}

Effectiveness

• By centralizing distribution via MCP, the LLM and client can track exactly how data is shared and confirm successful delivery (via returned results or notifications).

5. Data Use

Context & Definition

• Data Use covers analytics, insights, or operational activities that consume data, from dashboards to algorithmic trading strategies.
• In finance, this includes real-time PnL monitoring, portfolio optimization, or advanced risk analytics.

MCP Use Case

• Tools:
• “run_portfolio_optimization” (Monte Carlo or ML-based approach).
• “interactive_insights” tool that queries the data warehouse in real-time to answer user queries.
• Resources:
• Pre-loaded data sets (positions, market data, reference data).
• Prompts:
• Reusable prompt patterns for performing scenario analysis, building ad-hoc dashboards, or generating summary briefs for stakeholders.

Example: Querying a Data Warehouse for Interactive Insights

{
  "jsonrpc": "2.0",
  "method": "invoke_tool",
  "params": {
    "tool_name": "interactive_insights",
    "arguments": {
      "query": "SELECT symbol, SUM(volume) as total_volume FROM trades WHERE date = '2025-06-05' GROUP BY symbol"
    }
  },
  "id": 5
}

Effectiveness

• The LLM can orchestrate complex data usage scenarios by standardizing these calls, while the MCP serverensures compliance with access roles, logging, and usage policies.

Allied Architecture Aspects:

A. Discoverability (Metadata, Lineage, Search)

• Use: The LLM can call a “search_metadata” tool to find relevant data sets or generate lineage reports.
• Example: “List all data sets with the label FX quotes that originated from Bloomberg in the last 24 hours.”

B. Observability (Quality, Testing, Observability)

• Use: “check_data_quality” tool that runs validations on newly ingested data, checks if required fields are missing, etc.
• Example: The LLM can prompt: “Verify trades_20250605.csv has no outlier volumes beyond standard deviation threshold.”

C. Standards (Data Model, Data Format)

• Use: A “validate_schema” tool ensures data adheres to ISO 20022 or internal JSON schema definitions.
• Example: “Validate that trades.json conforms to tradeSchema_v1.1.”

D. Security (Access Control, Privacy, Classification)

• Use: The MCP server enforces RBAC (Role-Based Access Control) and classification levels. Tools or resources can be locked to certain user roles.
• Example: Only users with “Compliance_Officer” role can call “push_to_regulator_api” or retrieve certain sensitive data sets.

rbac:
  roles:
    - name: compliance_officer
      permissions: ["push_to_regulator_api", "get_sensitive_reports"]
    - name: trader
      permissions: ["ingest_market_data", "interactive_insights"]

How to Use MCP Effectively in Data Engineering

1. Standardize Tools & Resources

• Define a tool registry with unique names and stable versioning so that the LLM (and other clients) always know how to request a tool.
• Store resource definitions (URLs, formats) in a central metadata system that the LLM can query.

2. Adopt Prompt Templates

• Create prompt libraries for repeating tasks (e.g., “generate risk metrics,” “fetch daily trades,” “publish compliance report”), ensuring consistent usage and fewer errors.

3. Leverage Security & Governance

• Enforce centralized access control and logging at the MCP server.
• For finance, incorporate compliance checks (MiFID II, GDPR) directly into tool or resource usage policies.

3. Combine Observability & Automation

• Integrate data quality checks (e.g., data drift, volume anomalies) as part of each LLM-driven request, so the system can auto-flag or auto-correct anomalies.

4. Monitor Version Drift

• If your data schemas or tools change often, rely on a handshake/negotiation mechanism in MCP to ensure the client and server always know which version is being used.

Summary

By integrating MCP into each phase of the data lifecycle — from acquisition to use — and covering allied aspects like discoverability and security, you establish a unified, governed approach. This is particularly impactful in heavily regulated sectors such as finance, where auditability, consistency, and compliance are paramount.

MCP’s tool/resource abstraction and standardized prompts make it simpler to incorporate an LLM into enterprise-scale data engineering workflows while enforcing the necessary controls (RBAC, encryption, validation) at every step.

As next steps we will see how to use programming languages and tools to build Apps.