fireflies · MCP Server

Fireflies Data Meets AI — Powered by Improvado MCP

Improvado extracts your Fireflies meeting transcripts, action items, and call metadata and makes them queryable through any MCP-compatible AI agent. Ask what was decided across 500 meetings without opening a single recording. Works with Claude, Cursor, and more.

46K+ metrics ·Read & Write access ·500+ platforms ·<60s setup

Read

Read: Search Across Every Recording at Once

Stop scrubbing through recordings to find that one decision. Your AI agent searches full transcripts, speaker-attributed text, and action items across your entire Fireflies workspace. The MCP server handles transcript retrieval and semantic search.

Example prompts

"What did we decide about the pricing model in the last 30 days? Search all sales call transcripts."

3 hrs → 1 min

"Show me every action item assigned but not completed from last week's meetings."

45 min → 30 sec

"Which objections came up most often in discovery calls this quarter? Group by category."

4 hrs → 2 min

Works with Claude ChatGPT Cursor +5

Write

Write: Create Follow-Ups Without Manually Reviewing Calls

Your AI agent reads meeting transcripts and generates follow-up emails, Notion pages, or task lists — all grounded in what was actually said. No more rewriting the same post-meeting summary from scratch.

Example prompts

"Write a follow-up email for the discovery call with the enterprise prospect from Tuesday. Use action items from the transcript."

30 min → 2 min

"Create a Notion page summarizing all product feedback from this week's customer calls. Group by theme."

2 hrs → 5 min

"Draft a proposal outline based on requirements discussed in the last three calls with this account."

1.5 hrs → 5 min

Every action logged · Fully reversible · SOC 2 certified

Monitor

Monitor: Track Recurring Themes Across Calls

Let your AI agent watch for patterns across meetings — competitors mentioned frequently, unresolved action items piling up, or topics your team keeps getting asked about but hasn't addressed.

Example prompts

"Alert me when any competitor is mentioned in three or more calls in a single week."

Manual → auto

"Every Monday: show me unresolved action items from last week's calls, grouped by owner."

2 hrs → auto

"Flag any call where 'pricing' or 'discount' came up more than five times."

Manual → auto

Alerts sent to Slack, email, or your AI agent

Full cycle

The Closed Loop: Read → Decide → Write → Monitor

Your AI agent doesn't just surface data — it acts. Adjust pricing, update product descriptions, manage inventory, apply discounts — all through natural language. The MCP server translates intent into API operations.

Every phase runs through the same MCP connection. One protocol, all platforms, full governance. No switching between tools.

Ideate

Launch

Measure

Analyze

Report

Iterate

One conversation. All six phases. Every platform.

The daily grind

Common problems. Direct answers.

Challenge 1

Institutional Knowledge Disappears When People Leave

The problem

A team member who handled a key account leaves. Their notes are incomplete, their memory of conversations is gone, and the new person starts from zero. Fireflies has every call recorded, but without searchable transcripts connected to AI, that knowledge is practically inaccessible.

How MCP solves it

Improvado indexes all Fireflies transcripts and makes them queryable. The new team member asks their AI agent for the full history of discussions with that account — commitments made, objections raised, product feedback given. Full context in minutes, not weeks.

Try asking

Summarize everything discussed with this account over the last 12 months. What did we commit to, and what issues came up repeatedly?

Answer in seconds

All data sources, one query

Challenge 2

Action Items Fall Through the Cracks After Every Call

The problem

Fireflies captures action items per meeting, but there's no consolidated view across all calls. Following up means opening each meeting individually, checking what was assigned, and manually tracking completion. Most action items are forgotten within 48 hours.

How MCP solves it

Ask your AI agent for a consolidated action item report across all recent meetings. It pulls every assigned item, groups by owner, and flags which ones are past due. The entire follow-up workflow becomes one query.

Try asking

List all open action items from meetings in the last two weeks. Group by person and flag anything more than 5 days old.

Full detail preserved

No data loss on export

Challenge 3

No Way to Analyze Patterns Across Hundreds of Calls

The problem

Your team wants to understand why deals stall in late-stage. The answer is probably in call transcripts, but analyzing hundreds of recordings to find common themes is impractical. You'd need to listen to each one or read each transcript manually.

How MCP solves it

Improvado extracts and normalizes Fireflies transcript data so your AI agent can run pattern analysis across the entire corpus. Identify recurring objections, topics that correlate with closed-won deals, or questions that signal high buyer intent — at scale.

Try asking

What topics came up in calls that converted to closed-won vs. calls that went dark? Find the top 5 differences.

Unified data model

Compare anything side by side

👥 Teams

One Framework. Five Roles. Zero Setup.

Same MCP connection, different workflows for every team member. Each role asks in natural language — the MCP server handles the complexity (rate limits, auth, schema normalization, governance) behind the scenes.

Agency CEO

Portfolio health. Client risk. Revenue signals.

Media Strategist

70% strategy, not 70% ops. Auto campaign QA.

Marketing Analyst

Zero wrangling. Cross-platform. AI narratives.

Account Manager

QBR decks auto-generated. Call prep in 30s.

Creative Director

Performance-to-brief. Predict winners before spend.

FAQ

Common questions

What Fireflies data is accessible through this MCP?

Full meeting transcripts with speaker attribution, AI-generated summaries, action items, meeting metadata (date, duration, attendees, meeting type), call scores, and topic tags. All queryable in natural language across your entire workspace.

Can I search transcripts by specific speaker or topic?

Yes. You can filter by speaker name, meeting date range, topic keywords, assigned action items, or meeting type. For example: 'Show me everything a specific role said about pricing in Q1 discovery calls.' The AI agent handles the filtering logic.

Does this work with all meeting types Fireflies records?

Yes — Zoom, Google Meet, Microsoft Teams, and any other platform Fireflies connects to. As long as Fireflies has the transcript, the MCP server can access it. Meeting type metadata is preserved so you can filter by sales calls, standups, or customer success meetings separately — all through Improvado's hosted MCP server.

How does this compare to Fireflies' built-in search?

Fireflies' native search is keyword-based within their UI. With the MCP server, your AI agent can perform semantic search, cross-reference multiple calls simultaneously, aggregate patterns across hundreds of transcripts, and combine meeting data with other data sources — things the native search can't do — all through Improvado's hosted MCP server.

Is meeting transcript data handled securely?

Yes. Improvado is SOC 2 Type II certified. Transcript data is accessed through Fireflies' official API using your credentials, processed in Improvado's secure infrastructure, and never stored in conversation logs. Access is scoped to what your Fireflies API token permits.

How long does setup take?

Under 5 minutes. Generate a Fireflies API key from your account settings, connect it to Improvado, then add one line to your MCP client config. Your full transcript history is available immediately — no data migration required.

Stop Reporting. Start Executing.

Connect your data to an AI agent in under 60 seconds. The closed loop starts with one conversation.

SOC 2 Type II GDPR 500+ Platforms