Every interview contains signals about candidate strengths, concerns, compensation expectations, hiring bar calibration, and interviewer behavior. But if you want to compare candidates, analyze patterns, or understand what's happening across your hiring funnel, you usually have to create reports one by one, export data, or manually piece things together.

That's why we’re excited to share that Metaview now provides an MCP server. MCP (the Model Context Protocol) connects your interview data directly to AI tools like Claude so you can ask questions and manipulate the data using natural language. 

MCP is now active in three core Metaview products:

1. Notes: Query across your interview data

Metaview already captures everything that happens during an interview: the conversation, the key signals, the scorecard feedback, and the structured summaries recruiters rely on later.

With MCP, that data becomes directly queryable by AI. Give simple prompts like:

  • "Compare the top three candidates for this role." 
  • "Summarize red flags from the last five technical screens." 
  • "Build an interview plan based on this job description." 
  • "What are the common concerns raised by interviewers for this role?"

The AI retrieves the relevant interview data, analyzes it together, and returns a structured answer grounded in your actual interview data.

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“Using Metaview’s MCP, we were able to identify the behaviours and signals that consistently show up in interviews for our highest-performing hires. We turned those insights into benchmarks for what great interviews should look like. Now we have an automated report that highlights low-rigour or low-signal interviews so my team can intervene quickly.” 

— James Mantle, VP Talent, Mews

2. Reports: Analyze your entire hiring funnel with one question

Individual interview queries are useful. But we’ve found the biggest wins come from looking at your funnel as a whole.

You can ask questions about your entire pipeline, such as:

  • "What does scorecard feedback say about candidate quality across open roles?" 
  • "Which roles have the most interviews but no hires?" 
  • "What are the salary expectations across our open Sales roles?"

For most recruiting teams, this kind of analysis simply doesn't happen because it takes too much effort. With MCP, you just need to ask the question.

3. Sequences: Launch and improve outreach without effort

Building and managing outreach sequences is now easier than ever.

Just ask, and the MCP agent will:

  • Create a new sequence 
  • Add candidates to a sequence 
  • Update an email's sender details 
  • Change the content of an email 
  • Pause a candidate

Metaview Outreach makes reaching and engaging candidates simpler and faster than ever before. 

4. Reports: Compare comp expectations across candidates

Talent teams often have no idea how much data they have on candidate compensation requirements. All of those conversations at the end of standard interviews is a data point to use in positioning a role, or comparing salaries across geographies. 

Using plain language, you can quickly find: 

  • Average salary expectations for any role
  • How expectations compare between countries
  • Where hybrid or remote roles fit in
  • Key candidate profiles who’ve backed out or been rejected because of compensation

It’s the kind of data analysis that would otherwise take days or weeks, done in minutes (and with no lift). 

Connect Metaview to Claude in two minutes

Getting started with MCP is straightforward:

  • If you're already using Metaview go to Settings → MCP
  • Follow the step-by-step instructions

Once connected, your Metaview data becomes available for natural language queries immediately. No exports. No manual analysis. Just ask.

See it for yourself

MCP support for Metaview is available now. If you're already using Metaview, you can enable it and start querying your interview data right away.

And if you're not using Metaview yet, now's a great time to see what conversational hiring data actually feels like.

Try Metaview for free