
Developed core backend features for the juspay/clairvoyance repository, focusing on remote LLM integration and system reliability. Built a custom MCP client in Python, enabling the voice agent to connect to remote LLM servers via streamable HTTP transport. Leveraged asynchronous programming and Pydantic models to ensure robust, version-safe communication and dynamic tool discovery. Enhanced configuration management by introducing environment-based client timeouts and explicit error handling for HTTP responses, improving diagnostics and SLA adherence. The work established a scalable, extensible foundation for future integrations, emphasizing maintainability and flexibility in microservices architecture while addressing production reliability and debuggability requirements.
August 2025: Delivered MCP Client Configurability and Robust Error Handling for clairvoyance, introducing configurability and improved diagnostics to boost reliability and SLA adherence across the service.
August 2025: Delivered MCP Client Configurability and Robust Error Handling for clairvoyance, introducing configurability and improved diagnostics to boost reliability and SLA adherence across the service.
In July 2025, delivered a new Custom MCP Client for Clairvoyance enabling the voice agent to connect to a remote LLM server using a streamable HTTP transport. Implemented dynamic tool discovery/registration, expanded configuration options, a client implementation, and robust communication models using Pydantic. This work establishes a scalable, extensible remote LLM integration foundation that enhances flexibility and future-proofing of the voice-enabled workflow.
In July 2025, delivered a new Custom MCP Client for Clairvoyance enabling the voice agent to connect to a remote LLM server using a streamable HTTP transport. Implemented dynamic tool discovery/registration, expanded configuration options, a client implementation, and robust communication models using Pydantic. This work establishes a scalable, extensible remote LLM integration foundation that enhances flexibility and future-proofing of the voice-enabled workflow.

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