Agentic Research Arrives: GLG's MCP Connector Links AI to Human Experts

For the AI community, GLG’s MCP Connector is a practical instantiation of agentic research—an architecture where an AI copilot can autonomously call on human experts as a live knowledge source. By leveraging the Model Context Protocol, the connector turns GLG’s expert network into a pluggable 'human-in-the-loop' module that AI agents can probe, reason over, and blend with other data streams. This challenges the traditional silo between AI-generated synthesis and high-stakes human judgment.

On July 22, 2026, GLG—the world's largest platform for on-demand human expertise—announced the launch of a Model Context Protocol (MCP) connector, a significant product leap that embeds its vast repository of expert knowledge directly into clients' AI workspaces. The move signals a maturation of the expert-network industry: no longer a manual, phone-based search and scheduling service, but an always-on, API-first research layer. By implementing MCP, an open protocol originally developed by Anthropic for connecting AI assistants to external data and tools, GLG is positioning itself at the intersection of human intelligence and agentic AI.

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For the AI community, GLG’s MCP Connector is a practical instantiation of agentic research—an architecture where an AI copilot can autonomously call on human experts as a live knowledge source.

At its core, the MCP connector allows institutional investors, consultants, and corporate strategists to query their entire history of expert call transcripts and GLG's Expert Content Library—which includes named, attributable perspectives—without leaving the AI environments they already use. This means a private equity analyst vetting a potential deal can now prompt their internal AI agent with: 'What did experts tell us about this sector’s regulatory risk in the last six months?' and receive synthesized, citation-backed answers pulled directly from past GLG engagements. The connector also includes a conversational agent that helps formulate research angles, surface new experts, and initiate new primary research—collapsing the gap between insight retrieval and insight generation.

This launch is more than a feature update; it is GLG’s strategic response to a world where knowledge work is increasingly mediated by AI copilots. By adopting MCP, GLG is essentially becoming a 'tool' that any MCP-compatible agent can use—similar to how a search engine or database can be plugged into an AI workflow. This dramatically lowers the barrier to accessing high-quality qualitative research, turning GLG from a destination portal into an embedded intelligence layer across Slack, Microsoft Teams, custom GPTs, or enterprise agents. The emphasis on traceability and GLG’s proprietary compliance framework is critical: every AI-generated answer is always attributable to a specific expert conversation or piece of content, ensuring that confidentiality and legal guardrails are maintained—a non-negotiable requirement for the financial services and strategy clients that make up the bulk of GLG’s user base.

For the competitive landscape, the MCP connector likely gives GLG a first-mover advantage among expert networks. Rivals like AlphaSights, Third Bridge, and Capvision have invested in technology but have not yet announced MCP integrations. With enterprise AI adoption accelerating, a standardized connector makes GLG a plug-and-play research module within the rapidly growing ecosystem of AI agents. This could become a retention and acquisition wedge: a client building internal agent workflows may naturally gravitate toward the expert network that seamlessly plugs in, reinforcing GLG’s market-leading position.