Template Server for Contextual Protocol Integration (MCP)

An established server blueprint adhering to the Model Context Protocol (MCP), featuring deep integration with OpenAI, Anthropic, and the EnrichB2B data enhancement suite.

Initialization Procedures

1. Establish an isolated computational environment: bash python -m venv env_ctx source env_ctx/bin/activate # Windows users: env_ctx\Scripts\activate

2. Load required software modules: bash pip install -r dependencies.txt

3. Configure runtime secrets and parameters: bash cp config_defaults.env .env

Modify .env to include all necessary authentication tokens and operational parameters

Server Execution

To launch in development mode: bash python main_app_entrypoint.py

Alternatively, utilize the dedicated MCP command-line utility: bash mcp launch main_app_entrypoint.py

Core Capabilities

• Seamless linkage with GPT-4 (OpenAI)
• Native support for Claude (Anthropic)
• Data enrichment via EnrichB2B leveraging LinkedIn intelligence
• Web service hosting powered by FastAPI/Uvicorn architecture
• Dynamic environment variable management
• Pre-built utility functions and access resources
• Standardized, modular project organization

Directory Map

.
├── config_defaults.env # Blueprint for system configuration variables ├── .git_ignore # Rules for source control exclusion ├── README.md # Documentation source (this document) ├── dependencies.txt # List of Python library prerequisites ├── enrich_data_client.py # Module for interfacing with EnrichB2B service └── main_app_entrypoint.py # Primary MCP server implementation script

Operational Guidance

1. Initiate the service endpoint.
2. Connect utilizing any compliant MCP client agent.
3. Invoke available utilities and data sources:
4. config://system - Retrieve current server operational parameters
5. retrieve_profile_data - Fetch comprehensive data for a specified LinkedIn entity
6. fetch_user_feed - Acquire recent textual outputs and engagement records for a user
7. process_gpt4 - Invoke text synthesis via the GPT-4 engine
8. generate_claude_output - Request content generation from the Claude model
9. data_analysis_template - Predefined scaffolding for analytical textual tasks

EnrichB2B Service Functions

retrieve_profile_data

Obtain exhaustive metadata for a given LinkedIn profile URL: python response = await retrieve_profile_data( identity_locator="https://www.linkedin.com/in/userhandle", include_firm_info=True, include_follower_counts=True )

fetch_user_feed

Retrieve the latest posts and associated interactions for a LinkedIn profile: python response = await fetch_user_feed( identity_locator="https://www.linkedin.com/in/userhandle", depth_pages=2, # Paging limit (1 to 50) max_comments_per_item=3, # Comment count ceiling (0 to 50) max_likes_per_item=None # Like count ceiling (0 to 50) )

Future Enhancements

To integrate novel functionality:

1. Define new service conduits using the @mcp.tool() decorator
2. Introduce new data endpoints using the @mcp.resource() decorator
3. Define new procedural text generators using the @mcp.prompt() decorator

Licensing

MIT License