MCP vs REST API for VC Research, Which Should You Use?

MCP (Model Context Protocol) is a standard for AI agents to invoke tools. REST APIs are the standard for software-to-software calls. They overlap, both can return JSON over HTTP, but they differ in what they assume about the caller.

Pick MCP when:

- Your runtime is Claude Desktop, Claude Code, Cursor, Windsurf, Zed, Cline, ChatGPT Apps, or any other MCP-compatible host. - You want the agent to discover tool schemas, validate arguments, and surface the right tool to the user without you wiring it. - Your workflow is conversational, a user asks a natural-language question and the agent picks tools.

Pick REST when:

- Your runtime is a Python, Node, Go, or Bash script with no MCP client. - You want predictable, deterministic calls with explicit URLs, headers, and bodies. - You're integrating into a CRM, BI tool, dashboard, or pipeline that doesn't know what MCP is.

GitDealFlow exposes both. The MCP server (@gitdealflow/mcp-signal) wraps the same data that the REST endpoints expose at /api/signals.json, /api/signal?name=, etc. Function-calling tool definitions are at /api/agent/tools for OpenAI / Anthropic / Gemini SDKs that don't speak MCP but want structured-tool semantics.

The honest answer for most users: start with REST because it's universal, add MCP later when you get an AI-agent host in your workflow. Both surfaces are free, no auth, idempotent.

What MCP actually adds over plain HTTP. When an MCP host loads the server, it discovers the tool schemas itself, validates arguments before they are sent, and surfaces the right tool in response to a natural-language question. That is the concrete difference: the agent reads the tool descriptions and selects among them, where a REST client needs you to write the URL, the method, and the body by hand. The six tools are the same either way, so the data you get back is identical.

The two transports and why they exist. The server speaks both stdio, the local process channel used by desktop hosts like Claude Desktop and Cursor, and Streamable HTTP, the remote form used by hosted and cloud agents. That split is an implementation detail for most users, but it is why the same package works on a laptop and behind a service.

The six read-only tools cover the whole loop. get_trending_startups returns the current top movers, search_startups_by_sector filters them by sector, get_startup_signal returns one org's full metric history, and get_signals_summary, get_scout_receipts, and get_methodology round out the set. Because every tool is read-only and needs no auth, you can experiment freely without risking a write or spending on a key.

A combined pattern that holds up in production. The common setup is MCP inside the AI-agent layer, where natural-language tool selection is genuinely valuable, plus plain REST for the deterministic parts: cron jobs that pull /api/signals.json on a schedule, batch enrichment scripts, and CRM webhooks that never speak the Model Context Protocol. Both read the same dataset, so nothing is duplicated and nothing drifts out of sync.

The function-calling middle ground. For runtimes that want structured tools but do not implement MCP, the same schemas are published in OpenAI, Anthropic, and Gemini function-calling formats. That covers SDKs and platforms that sit one step short of a full MCP client and keeps the toolset reachable from almost any agent stack.

The practical default. Start with REST because it is universal and needs no host. Add MCP the moment an AI-agent host enters the workflow, at which point it is a single config line rather than a rewrite. Both surfaces are free, unauthenticated, and idempotent, so switching between them carries no migration cost.

One rule of thumb to close. If a natural-language agent will be choosing the calls, reach for MCP; if a script, a webhook, or a dashboard will be making them, reach for REST. The dataset underneath is identical either way, so the decision is about the caller, never about the data.