Metaview’s Model Context Protocol (MCP) server lets AI assistants like Claude connect directly to your interview data — transcripts, AI notes, structured fields, scorecards, and more.The Metaview MCP is a standardised integration layer that gives AI tools secure, real-time access to your Metaview workspace. Once connected, you can search conversations, retrieve transcripts and notes, and read structured data — all through your AI tool of choice. Please see here for integration details.All of this happens seamlessly, without manual exports or copy-pasting. You define the workflow; the MCP gets you the data you need so you can make faster, better-informed decisions.
Analyse the interview patterns of your best hires versus those who failed probation. By pulling transcripts across cohorts, you can surface which questions, topics, and interviewer behaviours correlate with long-term success — and which don’t.
What you can build: A recurring report that compares interview data against 90-day performance outcomes, helping calibrate your hiring rubric over time.
Candidate Reviews
Review candidates consistently against your hiring rubrics, ideal candidate profiles, and role criteria. Pull structured evidence from every conversation so you can assess each candidate against your own standards — not generic templates.
What you can build: A post-interview summary that shows you how well each conversation covered your rubric dimensions, so you can spot any gaps in evidence before the debrief.
Interview Process Optimisation
Use your accumulated interview data to build, refine, and standardise your interview structures. You can analyse which questions generate the most useful signal, identify stages with redundancy, and spot where candidates consistently drop off or disengage.
What you can build: A quarterly process review that benchmarks question effectiveness and recommends changes to your interview guide based on real conversation data.
Criteria Coverage
Ensure every role criterion is measured rigorously and consistently across all interviewers and panels. Use the MCP to review your conversations and identify where critical criteria are under-covered or missing entirely.
What you can build: A coverage tracker that alerts hiring managers after each interview when some rubric dimensions may need more coverage before moving to offer.
Interviewer Performance & Coaching
Surface patterns in interviewer consistency, competency coverage, and communication style to inform personalised coaching. Inconsistencies can help point to areas that may benefit from further calibration. This is one of the highest-value use cases, and is available as a ready-made skill — see below.
What you can build: A daily automated coaching pack delivered to each interviewer via Slack, grounded in their actual conversations from that day.
Identifying Potential Bias
Surface patterns across interviewers that may be worth investigating further. By analysing language and questioning across your full transcript corpus, you can surface inconsistencies that are difficult to spot manually — giving your DEI or talent leadership team a starting point for deeper review.
What you can build: A monthly pattern review that flags areas of inconsistency across your interview data for your team to investigate.
Market Intelligence
Extract compensation expectations, working preferences, AI competency benchmarks, and hiring metrics directly from what candidates are actually saying in interviews. This turns your Metaview data into a real-time market intelligence source.
What you can build: A living compensation and preferences dashboard, updated automatically as new conversations are added to Metaview.
Recruiter and Candidate Q&A Analysis
Analyse what your recruiters are asking, how candidates are answering, and surface recurring themes across the funnel. This is valuable for both script optimisation and candidate experience improvements.
What you can build: A weekly digest for recruiting leaders showing the top questions candidates are asking, common objections, and how recruiters are responding to them.
The workflows in this guide are examples of what’s possible. Every organisation is different, and what works for one team may not be right for another. The goal is to empower your recruiting team to make better-informed decisions, not to replace the human judgement that sits at the heart of great hiring.
The MCP helps you surface and organise information; what you do with it is always your call.
Before implementing any AI-powered workflow on your interview data, we recommend reviewing our Best Practices when working with AI guide and checking with your legal, compliance, and data protection teams to ensure alignment with your internal policies and applicable regulations.
Several of the use cases above now exist as installable AI Skills — reusable workflows you add to Claude or ChatGPT once, then trigger by asking for the outcome. A skill carries its own process, safeguards, and output structure, so you don’t have to write or maintain the prompt yourself.
Interviewer Coaching
Reviews interviews against your own interview guide (or a bundled screening framework), quotes the specific moments that worked and the follow-ups that were missed, and delivers a private coaching pack. Includes the recurring version, team-level patterns, and building a training guide — with the privacy and no-ranking rules built in.
Where a use case above maps to a skill, start there rather than building from scratch:
For anything a skill doesn’t cover — including bias pattern reviews — the prompting patterns and gotchas below apply. Start narrow, verify the output against the source conversations, and review the Best Practices when working with AI guide before rolling a workflow out to your team.
The MCP is powerful, but the quality of your output depends heavily on the quality of your prompt. Here are the most common pitfalls — and how to avoid them.
Vague prompts produce vague (or wrong) results
The AI can only work with what you give it. If your request is ambiguous, it will make inferences — and those inferences may be wrong.
Fix: Specifying the candidate name, role, date range, and conversation type dramatically improves accuracy.
❌ Avoid
✅ Better
”Show me last week’s conversations from the team"
"Show me conversations from the last 7 days. Identify each candidate by their email address. Only include conversations that have a transcript. Focus on conversations tagged as ‘Job Interview’."
"Find questions that come up in interviews for candidates we hired"
"Review conversations with the following candidates: [insert email addresses of hired candidates]. For each conversation, extract the questions the interviewer asked. Then rank the questions by how frequently they appear across all these conversations.”
Candidate “names” can appear as email addresses or phone numbers
When Metaview doesn’t have a proper name on record for a participant — which happens often for phone calls or conversations not scheduled through your ATS — it falls back to displaying their email address or even phone number as the display name.This happens because for calls booked outside your ATS (direct dials, ad-hoc video calls), only an email or phone may be available.
Fix: Ask specifically for the candidate’s email address as the primary identifier. This gives you the most consistent reference across all conversation types.
❌ Avoid
✅ Better
”List candidates interviewed this week with their names"
"List candidates interviewed this week. Use email address as the primary identifier and include their name where available.”
Pulling too many transcripts at once will burn through your tokens
Fetching full transcripts is expensive in terms of context. Asking Claude to “analyse all interviews from the past year” across a large dataset will hit limits, produce incomplete results, or run up unexpected costs.
Fix: Match your approach to the scale of your query — fetching full transcripts works well for a handful of conversations, but becomes inefficient at volume.
The right approach depends on scale:
1–5 conversations: Fetching full transcripts directly works well.
5–20 conversations: Use AI-generated summaries instead of full transcripts.
20+ conversations: Use AI Columns to extract structured data fields from each conversation, then aggregate — this is dramatically more efficient.
❌ Avoid
✅ Better
”Summarise all interviews from the past 6 months"
"Create an AI Column that extracts [specific signal] from each interview from the past 6 months”
Results may include unprocessed or ‘ghost’ sessions
Not every session in Metaview has a transcript. Sometimes the bot joins a call but no transcript is generated — for example, if the call was too short, ended before enough audio was captured, or hit a processing failure. These sessions can appear in search results and add noise to your analysis.
Fix: Explicitly filter to conversations that have a transcript. Include this instruction in your prompt: “Only include conversations that have a transcript. Skip anything that was not fully processed.”
Unclassified calls may be labelled as ‘Job Interview’
If a conversation’s type hasn’t been set in Metaview, the MCP defaults it to “Job Interview”. This means non-interview calls — portfolio meetings, exploratory chats, internal syncs — can be mislabelled and skew your results, especially if your team records a wide variety of conversation types.
Fix: When filtering by call type, be aware that unclassified calls will surface as “Job Interview”. If your workspace mixes conversation types, ask Claude to group results by call type and flag anything where the classification may be unreliable.
