synapseflow

synapseflow acts as an MCP server that orchestrates multiple AI agents to assist with academic research tasks. The server exposes interfaces that allow you to upload papers, configure processing options, and retrieve structured outputs such as citation graphs and summaries. Through the MCP protocol, you can connect other components (e.g., data loaders, vector stores, or visualization dashboards) and coordinate multi-agent workflows, enabling scalable processing of large document collections. Typical usage involves starting the server, connecting client tools, and using the provided endpoints to send documents, request analyses, and receive structured results like multi-agent decisions and provenance data.

Once running, you can leverage its capabilities to load PDFs or links to papers, adjust processing configurations (e.g., parsing formats, citation styles, search parameters), and then query for results. The server is designed to handle high-throughput workflows, allowing agents to operate in parallel and share intermediate results through the MCP layer. The real-time citation graph and vector search features help you quickly locate relevant papers and understand how sources relate to one another within your research corpus.

Prerequisites:

• Node.js (version 14.x or higher) or a runtime compatible with the server bundle
• Internet access for initial downloads (if pulling dependencies)
• Basic command-line proficiency

Installation steps:

1. Download the latest release of synapseflow from the releases page and extract it to a working directory.
2. Open a terminal and navigate to the extracted folder.
3. Install dependencies (if provided) using npm or yarn:

# If a package.json is present
npm install
# or
yarn install

4. Start the MCP server component (adjust command as needed for your environment):

# Example command to start the server
node path/to/server.js

5. Verify the server is running by checking the console output or visiting the configured health endpoint.
6. (Optional) Configure environment variables or proxy settings as required by your environment (see additional notes).

Note: If the release provides a prebuilt executable or desktop installer, follow the platform-specific instructions in the release notes instead of the above steps.

Environment variables and configuration tips:

• PORT: Set the port the MCP server should listen on (default 3000).
• LOG_LEVEL: Control verbosity (e.g., debug, info, warn, error).
• If running behind a reverse proxy, ensure proper routing and HTTPS termination.

Common issues:

• Node.js version incompatibilities: ensure you're using a supported Node.js version.
• Network/firewall restrictions blocking port 3000 or MCP endpoints: open required ports.
• Dependency installation failures: run with network access or use a bundled release that includes dependencies.

Configuration options:

• You can customize the MCP server’s name, port, and logging through environment variables or a config file if provided by the release.
• When integrating with other MCP components, expose and document the available endpoints/entities so downstream tools can communicate effectively.