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    Beeline MCP

    A secure way for AI agents to use your workforce data.

    Beeline MCP is Beeline's native implementation of the Model Context Protocol (MCP), an open standard that lets AI agents and assistants connect to your workforce data across sourcing, engagement and compliance decisions alike. 

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    Orchestration - insights-1-Ai Insights
    Rectangles-2-iStock-2256075816 AI hexagon

    Trusted foundation

    Every connection (whether a person working inside an AI assistant or an autonomous agent acting on their behalf) runs through the same identity, permissioning, and oversight already protecting every human user today.

    Interoperable

    Built on an open standard, so it works with whatever AI tools your team already has. No new vendor lock-in, and no need to abandon tools already in place.

    Human-in-the-loop

    Designed to keep experienced professionals in the decision loop on classification, compliance, and other high-stakes calls. Agents inform and act, people still decide.

    How it works

    One connection. Any tool.

    Normally, connecting a new AI tool to your data means building a custom integration for it (and doing it again for the next tool). Beeline MCP replaces that with one connection. Once it's set up, any approved AI assistant or agent can find and use the exact piece of Beeline data or action it needs, automatically. 

    •  Ask about an open assignment, spend, or a compliance flag, and get an answer right inside the AI tool you're already using
    • Kick off and track next steps (like moving a candidate forward) without opening Beeline and clicking through screens
    • Coordinate a process across Beeline and the other tools your team already uses, without switching between them 
    Surface the status of open assignments, spend, or compliance in plain language, inside whatever assistant a user already works in.
    Initiate and track process steps, from a candidate to an approval, without a person clicking through a screen.
    Coordinate multi-step processes that span Beeline and other platforms already in a customer's environment.
    AI sparkles

    Whether the front end is Beeline's own AI assistant, a proprietary interface a customer has built, or a third-party AI platform, every connection extends into the same trusted foundation already protecting every human user today.

    Interested in Beeline MCP?

    Your team is already using AI tools somewhere in their workflow. Beeline MCP means those tools can act on real workforce data securely, and without a separate integration for every one.

    Request a demo

    FAQs

    Beeline MCP is a capability of Beeline AI that natively embeds the Model Context Protocol (MCP), an open standard for connecting AI systems to external tools and data, directly into the Beeline platform. It gives enterprises a single, governed connection point through which approved AI agents and assistants can discover and act on workforce data across sourcing, engagement, and compliance workflows.

    A standard API integration is a fixed contract: a defined field goes in, a defined field or file comes out, built and tested for one specific workflow. MCP works differently. Beeline exposes a governed catalog of tools that agents can discover and invoke at runtime, so a new AI use case doesn't require a new integration to be engineered from scratch. Beeline continues to support and invest in flat file, batch, real-time API, and dedicated data platform connections for the transactional and analytics workloads they're built for; MCP adds an agent-native layer on top of that same governed foundation, it doesn't replace it.

    Beeline MCP has critical use cases for the extended workforce built in: engagement visibility across requests, assignments, statements of work, and projects; approvals across time, expenses, requests, offers, and milestone payments; and candidate actions, from reviewing a résumé to selecting, qualifying, or rejecting a candidate. New use cases are being added on an ongoing basis. In general, Beeline MCP fits any scenario where an AI agent or assistant, rather than a person clicking through a screen, is the one taking the action.

    No. Extending access to agents doesn't mean standing up a separate, less-tested authorization model. Every agent that connects through Beeline MCP inherits the same role-based permissions and approval-hierarchy model that governs Beeline's human users today. An agent only ever has access to the specific tools and data a person in that role would have, running through the same identity, permissioning, and oversight already protecting every human user today.