How to Connect Claude, ChatGPT, or Any AI to Your Recruiting Stack

Every recruiting team has its own workflows, its own preferred tools, and increasingly, its own favorite AI. Some recruiters live in Claude. Others rely on ChatGPT for everything from writing outreach to brainstorming search strategies. A growing number are building custom agents tailored to their exact hiring needs. The problem is that most recruiting software treats AI as a locked feature, not a platform capability. Your CRM might have a built-in AI assistant, but what if you want to use your own?

At Leonar, we believe the answer should always be “yes.” That is why we built an open API and full support for the Model Context Protocol (MCP), giving recruiting teams the freedom to connect any AI agent to their pipeline, candidate data, outreach sequences, and analytics. This guide walks through how it works, why it matters, and how to set it up.

Why your recruiting stack should be open to external AI

Most recruiting platforms that advertise AI features operate as walled gardens. Tools like Gem, HireEZ, and SeekOut each offer their own built-in AI capabilities, and those capabilities are genuinely useful. But they come with a hard constraint: you can only use the AI that the vendor provides. You cannot bring your own model. You cannot connect a custom agent. You cannot pipe your recruiting data into the AI workflow you have already built for other parts of your business.

This creates a real tension for forward-thinking teams. Maybe your company has standardized on Claude for internal operations and you want recruiting to benefit from the same context. Maybe you have a technical recruiter who has fine-tuned a ChatGPT assistant that writes better outreach for DevOps roles than any generic tool. Maybe your TA ops team wants to build a custom agent that monitors pipeline health and alerts hiring managers when things stall.

None of that is possible with a closed system.

Leonar’s approach is different. We ship powerful built-in AI features for sourcing, outreach, and candidate evaluation. Our AI talent sourcing agent runs autonomously in the background, finding and scoring candidates while you focus on relationship building. But we also open the platform to external AI agents through a REST API and MCP support. This is not an either/or decision. It is a both/and architecture. Use our AI where it excels, bring your own where you need something different, and let them work together.

MCP vs. API: two ways to connect AI agents to Leonar

There are two distinct pathways for connecting external AI to your Leonar instance. Each serves a different purpose, and understanding the distinction will help you choose the right approach for your team.

What is MCP (Model Context Protocol) and why it matters for recruiting

MCP is an open standard originally created by Anthropic in late 2024 and now governed by the Agentic AI Foundation under the Linux Foundation. Major players including OpenAI, Google, and Microsoft have adopted it, making MCP a true industry standard rather than a single-vendor project. The simplest analogy is USB for AI. Just as USB gave hardware devices a universal way to connect to computers, MCP gives AI agents a universal way to connect to software platforms.

Before MCP, connecting an AI agent to a recruiting CRM meant building a custom integration from scratch: writing API calls, handling authentication, formatting data, managing errors. MCP eliminates most of that complexity. When a platform supports MCP (as Leonar does), any MCP-compatible AI agent can plug in and immediately access the platform’s capabilities.

For recruiting, this means an AI agent connected to Leonar via MCP can read your candidate pipeline, search your talent database, trigger outreach sequences, analyze performance metrics, and suggest next actions. All of this happens through a standardized protocol, so you do not need to write any code or maintain a custom integration.

The practical impact is significant. Instead of switching between your AI tool and your CRM, you can stay in one conversational interface and let the AI pull in recruiting context as needed.

When to use the REST API instead

Leonar’s REST API provides full programmatic access to your recruiting data and workflows. You get complete CRUD (create, read, update, delete) operations across candidates, jobs, sequences, analytics, and more.

The API is the better choice when you are building automated workflows that run without human interaction, when you need to sync data between Leonar and another system on a schedule, or when you are connecting tools that do not yet support MCP. It is also the right fit for custom dashboards, internal reporting tools, or any integration where you want fine-grained control over every request and response.

Think of the API as the foundation layer. It is flexible, powerful, and works with any programming language or automation platform. If you are already using tools like Zapier, Make, or n8n for workflow automation, the REST API is how you connect Leonar to those systems.

Choosing between MCP and API for your recruiting use case

The choice comes down to how the AI will be used.

MCP is ideal for conversational AI agents like Claude or ChatGPT that need real-time, interactive access to your recruiting data. When a recruiter asks Claude “Who are the strongest candidates in my Senior Engineer pipeline?”, MCP lets Claude query Leonar on the spot and return a thoughtful answer. The interaction is dynamic, back-and-forth, and context-aware.

The REST API is better suited for automated, programmatic workflows. If you want a script that runs every morning to identify stale candidates and send Slack alerts, or a custom dashboard that visualizes pipeline velocity across all open roles, the API gives you the control and reliability those use cases demand.

Many teams end up using both. MCP powers the conversational layer where recruiters interact with AI directly, while the API handles the background automation that keeps everything running smoothly.

Keep in mind that queries to external AI providers incur token-based costs. For a typical recruiting workflow (pipeline analysis, outreach generation), expect to spend $20 to $50 per month per recruiter in AI provider fees, depending on volume. This is separate from your Leonar subscription and is billed directly by your chosen AI provider.

Three real-world use cases for connected AI agents in recruiting

To make this concrete, here are three scenarios where connecting an external AI agent to Leonar creates real value. These are not hypothetical. They represent patterns we see among teams already using the platform.

Pipeline analysis with Claude: “Which candidates should I follow up with today?”

One of the most immediate wins is using Claude as a pipeline analyst. Once connected to Leonar via MCP, Claude can read your entire candidate pipeline, including engagement history, last contact dates, stage progression, and response patterns.

A recruiter starts their morning by asking Claude: “Show me all candidates in the Product Designer pipeline who have not been contacted in the last seven days.” Claude queries Leonar, returns a structured list, and goes a step further. It ranks the candidates by engagement signals, noting which ones opened previous emails, which ones viewed your LinkedIn profile recently, and which ones are approaching the point where they are likely to disengage entirely.

The recruiter then says: “Draft a follow-up for the top three, referencing their most recent career move.” Claude pulls each candidate’s profile data from Leonar and generates three personalized messages, each one grounded in real information rather than generic templates. The recruiter reviews, edits, and triggers the sequence, all without leaving the conversation.

This is the kind of workflow that would take 30 to 45 minutes of manual pipeline review, CRM navigation, and message drafting. With a connected AI agent, it takes five minutes.

Personalized outreach with ChatGPT: beyond template-based messaging

Every recruiter knows that personalization improves response rates. The challenge is that genuine personalization at scale is exhausting. You can insert {first_name} and {company} merge fields into a template, but candidates see through that instantly.

When ChatGPT is connected to Leonar’s candidate data, the personalization goes much deeper. The AI can access a candidate’s full profile: work history, skills, education, recent job changes, shared connections, even content they have published or engaged with. Instead of surface-level tokens, ChatGPT crafts messages that reference a candidate’s specific career trajectory, mention a project they led, or draw a connection between their background and the role you are hiring for.

This pairs naturally with Leonar’s multi-channel outreach automation. The AI generates the personalized content, and Leonar handles the delivery across LinkedIn, email, and other channels. The result is outreach that feels handwritten at a volume that would be impossible to maintain manually.

For teams already exploring how to use ChatGPT for recruiting, connecting it directly to your CRM data is the logical next step. It transforms ChatGPT from a general-purpose writing tool into a recruiting-specific assistant with full context.

Custom sourcing agent for niche roles: building your own specialist

Some roles are so specialized that generic sourcing tools struggle. If you are hiring quantum computing researchers, embedded systems engineers for automotive LiDAR, or regulatory affairs specialists for biotech, you need a sourcing approach that understands the niche deeply.

With Leonar’s API, technical recruiting teams can build custom sourcing agents that combine domain expertise with CRM data. The agent knows what skills and experiences matter for the niche (not just keywords, but the subtle signals that indicate genuine expertise). It searches Leonar’s 870M+ profile database and scores candidates based on criteria that a general-purpose tool would miss.

For example, a custom agent for machine learning infrastructure roles might weigh contributions to specific open-source projects, experience with particular distributed training frameworks, and publication history more heavily than generic “machine learning” keywords. It queries Leonar’s database, ranks candidates, and adds the top matches directly to your pipeline with notes explaining why each person is a strong fit.

This is where the difference between AI sourcing and traditional recruiting becomes most visible. Instead of running searches and manually reviewing hundreds of profiles, you define the intelligence once and let the agent apply it continuously.

Step-by-step: connecting Claude to Leonar via MCP

Let us walk through the process of connecting Claude to your Leonar instance. This is the most common setup we see, but the same principles apply to any MCP-compatible AI agent.

Prerequisites: what you need before starting

You will need three things. First, a Leonar account with API access enabled. This is available on all Professional and Enterprise plans. If you are on a different plan and want to try it, reach out to our team. Second, you need Claude Desktop installed on your machine (or access to the Claude API if you are building a more advanced integration). Third, basic comfort with editing configuration files. You will not need to write any code, but you will need to add a few lines to a settings file.

Configuration: setting up the MCP connection

The setup process involves telling Claude where to find your Leonar instance and how to authenticate. In Claude Desktop, navigate to Settings, then Developer, then MCP Servers. Add Leonar as a new MCP server by providing your Leonar API key (found in your Leonar account settings under Integrations) and the server endpoint.

Once connected, Leonar exposes a set of tools that Claude can call on your behalf: search_candidates to query your talent database, get_pipeline to pull pipeline stages and candidate status, send_sequence to trigger outreach workflows, and get_analytics to retrieve performance metrics. You choose which of these capabilities to enable during configuration.

This granular permission system is important. Leonar’s MCP integration lets you decide exactly what the AI agent can and cannot do. You might allow it to read pipeline data and search candidates but restrict it from triggering outreach sequences until you are comfortable with the setup. You can always expand permissions later.

Once the configuration is saved, Claude detects the Leonar server automatically. You will see Leonar listed as an available tool in Claude’s interface, confirming the connection is active.

Your first interaction: asking Claude about your pipeline

With the connection established, start with a simple query. You might ask Claude: “Show me all candidates in the Senior Engineer pipeline who were last contacted more than 7 days ago.” Claude queries Leonar via MCP, retrieves the filtered list, and presents it with recommended next actions.

You will see candidate names, current pipeline stages, last contact dates, and engagement indicators, all structured in a way that makes it easy to act immediately.

From there, you can go deeper. Ask Claude to compare response rates across different outreach sequences. Ask it to identify which sourcing channels are producing the most engaged candidates. Ask it to draft a follow-up message for a specific candidate based on their profile. Each request flows through MCP to Leonar and back, with Claude handling the analysis and language generation while Leonar provides the recruiting data and workflow execution.

The experience feels like having a knowledgeable recruiting analyst sitting beside you, except this one has instant access to every data point in your CRM.

How Leonar’s open architecture compares to closed-ecosystem recruiting tools

The key differentiator is not just having AI features. Every modern recruiting tool has those. It is whether the platform lets you bring your own AI and connect it to your data on your terms. Leonar is one of the first recruiting CRMs to support MCP natively, which means you can connect Claude, ChatGPT, or a custom agent and have it interact with your pipeline in real time without relying on third-party middleware.

For teams evaluating their options, our comparison of the best AI recruiting tools covers how these platforms stack up across other dimensions as well.

Privacy, compliance, and data control with external AI agents

Connecting external AI to your recruiting data raises legitimate questions about privacy and compliance. These concerns deserve direct answers, especially for teams operating under GDPR or navigating the EU AI Act.

Your candidate data remains stored in your Leonar instance. When an external AI agent queries your pipeline via MCP, you control exactly which data is shared with the AI provider. Leonar does not independently share your data with third parties. However, any data sent to an AI provider (Anthropic for Claude, OpenAI for ChatGPT) is subject to that provider’s data processing terms. Enterprise plans from most AI providers include commitments not to use API data for model training, but you should review your provider’s data processing agreement before connecting.

The granular permissions system adds another layer of control. When you configure an MCP connection, you decide exactly what the AI agent can access. You might grant read access to pipeline data but block access to candidate contact information. You might allow the agent to search your database but prevent it from modifying records. These permissions are set at the connection level, so different agents can have different access scopes.

Every action taken by an external AI agent is logged in Leonar’s audit trail. You can see exactly what was queried, when, and by which agent. This is critical for GDPR compliance, where you need to demonstrate that candidate data is processed lawfully and with appropriate controls. It is also relevant to the EU AI Act, which introduces transparency requirements for AI systems used in employment decisions.

There is an irony worth noting. Open architecture with granular permissions is actually more secure than black-box AI. When a vendor’s built-in AI processes your candidate data, you have limited visibility into how that data is used, stored, or retained. With an open API and MCP approach, you control the entire data flow. You choose the AI provider. You set the permissions. You own the audit trail. That level of transparency is exactly what regulators are moving toward.

The future of recruiting is agent-native, not tool-native

We are in the middle of a fundamental shift in how software gets used. For the past two decades, recruiters have operated tools: clicking through interfaces, running searches, copying data between systems, manually executing each step of a workflow. The next decade will look very different. Instead of operating tools, recruiters will direct agents that operate on their behalf.

Understanding what an AI sourcing agent does is a good starting point, but the concept extends far beyond sourcing. Imagine agents that monitor your pipeline health and proactively flag risks. Agents that analyze every outreach sequence and suggest copy changes based on real response data. Agents that coordinate across your ATS, CRM, and communication channels to keep every candidate moving forward without manual intervention.

This is not science fiction. The building blocks exist today. Leonar’s AI sourcing agent already runs autonomously, finding and scoring candidates in the background. MCP support means any external AI can now participate in your recruiting workflows. The REST API opens the door to custom automation that fits your exact process.

The recruiting teams that build these agent-native workflows now will have a compounding advantage. Every week of pipeline data makes the agents smarter. Every iteration of a custom prompt makes the outreach more effective. Every workflow that gets automated frees up recruiter time for the high-value work that actually requires human judgment: building relationships, selling candidates on the opportunity, and making great hires.

The question is not whether AI agents will transform recruiting. It is whether your stack is ready to support them when they do.