The identity graph as an MCP server. Your AI agents query it natively.
Give your AI assistants direct access to our full 287M-organization identity graph — no wrapper code, no token plumbing. Connect over streamable HTTP, and tools and clean, model-ready data are available the moment you connect.
Shift your AI toolchains from rigid text lookups to dynamic graph reasoning.
Standard REST endpoints require an engineer to pre-program exactly when an application should fetch a record, which locks automated systems into rigid, step-by-step tracks. Model Context Protocol changes that: given a semantic knowledge graph inside its reasoning loop, the model decides on its own when to call a tool, traverses corporate family trees to verify subsidiary boundaries, and factors in continuously updated market-entry signals — grounded in sourced, verifiable data, not a guess.
Four capabilities. One native MCP protocol layer.
Automatic tool discovery
On the initial handshake, your connected agents discover all four available tools: search_organizations, search_employment_route, search_news, and list_industries.
Two ways to authenticate
API keys (X-API-Key) for backend agents and CLI toolchains; OAuth 2.0 browser sign-in for interactive, human-in-the-loop work.
Token-optimized JSON-LD payloads
Responses carry semantic, pre-resolved entity context shaped to fit model context windows, with structural noise stripped out.
Access governance
Scope what each agent can reach. Keys carry read access across the graph; provision, rotate, or revoke them per workspace as you add agents.
How does MCP compare to other approaches?
| Approach | What it is | Where it breaks |
|---|---|---|
| CSV Export + RAG | Data export from ZoomInfo/Apollo into a vector store; agent queries embeddings | Data goes stale immediately. No graph hierarchy. Agent retrieves flat text passages, not resolved entities. |
| REST API Wrapper | Custom code wrapper around a legacy database REST API, exposed as an agent tool | Significant engineering overhead. Vendor schemas weren't designed for agent runtime reasoning. |
| CRM-as-Context | Agent queries Salesforce or HubSpot directly via native CRM APIs | CRM contains only data your team manually maintains. Fragmented records, no external signal tracking. |
| GraphIQ.ai MCP | Native Model Context Protocol integration into the full identity graph | Continuously updated. Graph-structured. Relationship-native. No wrapper code. |
Where MCP agent access connects your GTM.
Developers Persona
Building a dedicated engineering toolchain or local agent app? Review our complete SDK configuration guides.
/solutions/developers →AI Agent Data Layer
Looking to eliminate model hallucination loops across your pipeline? See how grounding data layers protect enterprise credibility.
/use-cases/ai-agent-data-layer →Developer Documentation Reference Index
Need complete tool definitions, script samples, and endpoint path parameters?
docs.graphiq.ai →Frequently asked questions
Is our data connection restricted to specific LLM model providers or clients?
No. Because MCP is a completely provider-agnostic standard, our server functions out-of-the-box across any protocol-capable environment. This includes Anthropic Claude (claude.ai connectors, Claude Desktop, Claude Code), OpenAI (Responses API, Agents SDK), LangChain networks, LangGraph architectures, or custom internal Python setups.
How does the MCP server ensure tenant isolation and workspace security?
Every session initiated via an API key or an authorized OAuth token runs within an isolated environment. Automated agents can only visualize, reason over, and interact with data boundaries mapped directly to your enterprise plan credentials, preventing any cross-tenant data bleed.
What is the typical latency on an MCP server call?
Standard reads return in under 200ms (production p50). The server implements Server-Sent Events (SSE) streaming on large payloads so the model can begin reasoning loops before the full data packet completes.