Who knows who, and how strongly.
Your agent traverses the warmest path between any two people, weighted by relationship strength, recency, and mutual edges. The graph is the API.
No new interface. No new habit. Add the MCP server to your agent config and your network becomes a tool call.
{ "mcpServers": { "agentintros": { "command": "intros-mcp", "env": { "AGENTINTROS_API_URL": "https://api.agentintros.ai", "AGENTINTROS_API_KEY": "ai_..." } } }}It's not AI for your contacts. It's your contacts, structured for your AI: a graph, a vector store, and a relational record, all queryable behind a single MCP server.
Your agent traverses the warmest path between any two people, weighted by relationship strength, recency, and mutual edges. The graph is the API.
Every email, meeting, and touchpoint is stored as embeddings so your agent finds the right person by what they actually care about, not just who they know.
The record that backs every graph path and semantic search: queryable rows your agent can cite, never a black box.
AgentIntros exposes a handful of clean tools over MCP. Your agent picks the right one, queries your graph, and routes the result back for your approval. trust is the product.
ⓘ Real conversation skeleton. Tools are auto-discovered from the MCP server.
The category that exists today was built for humans to browse. AgentIntros is built for agents to query. That's the whole shift.
ⓘ Based on public product surface as of 2026. Corrections welcome.