Meeting MCP Servers Compared: 6 Tested (Oct 2026)

I recorded four fictional sales and customer success calls on October 21, 2026, with six AI notetakers on every one, then asked each tool’s MCP server in Claude the same questions. The answers were almost identical. Everything around the answers was not.

TL;DR: I tested six meeting tools’ MCP servers in Claude on the same four scripted calls (tl;dv Meeting MCP Benchmark, October 2026).

• Fireflies scored 34 of 34 overall, the best free option; tl;dv and Otter scored 33.
• All six MCP servers refused to invent a discount when asked a trap question.
• Fireflies, Otter and Read AI named the right speaker for a planted code; tl;dv and Granola hedged.
• tl;dv, Fathom and Fireflies tied first on jumping to the exact moment in a recording.
• tl;dv’s MCP needs Pro; Fathom, Fireflies, Otter and Read AI include MCP on free plans.

Full disclosure: I write for tl;dv, and tl;dv is one of the six. Every tool got the same calls and prompts, and where a competitor beat tl;dv, I’ve said so.

An MCP (Model Context Protocol) server is the plug that lets Claude or ChatGPT read your meeting tool directly. Our guide to MCP covers the basics.

How I Tested the Six Meeting MCP Servers

I connected each tool’s MCP server to a test Claude Pro account one at a time and asked the same questions in a fresh chat, telling Claude to use only that connector. Each answer was scored 0 to 3 against an answer key written into the call scripts.

• A made-up company. Orrimsby is a fictional UK firm selling warehouse shift-scheduling software, so no real customer data was involved.
• Four scripted calls of about 8 minutes each: a discovery call, an internal customer success sync, a renewal check-in and a demo follow-up. The same four calls fed our hands-on review of Pipedrive Nova.
• Canary codes. I planted a made-up code in each call, such as GLASSWICK-77 in the internal sync. Each was said once and repeated back once.
• Plans: Fathom, Fireflies, Otter and Read AI ran on free plans. tl;dv ran on a Pro trial and Granola on a Business trial it started automatically when I signed up.
• Round 1: six questions in one message, worth 18 points. They covered a code search, a summary, a question across all four calls, an exact quote, a trap question and full transcript access.
• Round 2: two tests written down before any run. Can the MCP take you to the exact moment in a recording, and can it hand over the tool’s own AI notes? I also checked each tool’s notes against 10 facts from the script, for 34 points in all. Each tool ran each prompt once.

What went wrong on the day: we moved all four calls 15 minutes at the last minute and ran them back to back. Otter never joined the third call, likely because of that. My own tl;dv bot missed two, which was my setup error with the moved calls, not tl;dv’s. My colleague’s tl;dv bot in the same workspace caught them. No other tool had a second bot on the calls, so tl;dv’s answers on those two calls came from his. These count as capture misses, not MCP failures, so Otter’s Round 1 is scored out of 15.

One thing I couldn’t design around: speaker names. These tools tie a voice to the account that joined the call. My colleague played every customer from his own Google account, so Tom, Priya and Imogen often came out as “Matt Leppington”. On a real call each customer joins as themselves, so I judged who said what by which side of the call a line landed on, not by the name attached to it.

Which Meeting MCP Server Is Best? The Verdict by Use Case

No single meeting MCP server won outright. Fireflies is the best free all-rounder. Fathom links every transcript line to the moment, and tl;dv links every AI-note bullet. Otter ran without a single error, and Granola is the pick if you never want a bot in the call.

Add it all up and Fireflies leads with 34 of 34, with tl;dv and Otter on 33.

tl;dv Meeting MCP Benchmark: The Full Results

In the tl;dv Meeting MCP Benchmark (October 2026), the six meeting MCP servers were almost level on straight answers. Four scored 18/18, Otter scored 15/15 on the calls it recorded, and Fathom scored 16/18. The gaps opened up in who said what, and in the AI notes.

tl;dv Meeting MCP Benchmark (October 2026). The same 4 calls and prompts for every tool, scored against a pre-written answer key, both rounds run on October 22, 2026. Otter’s Round 1 scaled from 15 to 18. Granola had no notes until I pressed Generate.

What I Found That Nobody Else Mentions

Meeting MCP servers trip up mostly on speaker names and misspelled codes. Vendor pages don’t flag these, and neither did ChatGPT or Perplexity on October 23, 2026.

• Wherever a tool put a name on the customer, it was my colleague’s, because his Google account joined the call. Ask customers to join from their own accounts.
• A planted code came back spelled several ways, from “Glasswick77” to “Glaswix 77”. Read AI’s own search couldn’t find it by the code at all.
• AI answers get Fireflies’ free limit wrong. ChatGPT and Perplexity both say 50 requests a day. Fireflies’ limits page says 30 a minute and 500 a day.
• Fireflies’ MCP said “No audio url”, yet its link opened the recording and played it.
• Otter and Read AI accounts came with sample meetings, and Fathom showed a 2021 “Fathom Demo” we didn’t record. All three reported them accurately instead of passing them off as our calls.

Fathom MCP: Joint Best for Jumping to the Moment, Worst at Who Said What

Fathom’s MCP scored 16/18 on a free plan. It was joint top on jumping to the moment (3/3, with tl;dv and Fireflies), and the only one with a link on every transcript line. It’s also the only one that merged both speakers into one block, so it couldn’t even hedge on who gave the canary code.

• Right: asked for Tom’s overtime figure, it gave 05:40 and a link straight to it. Its AI notes scored 9/10, with every bullet linked back to the recording.
• Wrong: it dropped a point each on the code search and the summary. It labeled every guest “Matt Leppington”. It also turned ORMSTEAD-52 into “Orrimsby 52” and the competitor ShiftHarbour into “Shiftalba”. One tool came back “not enabled for your account”, and it caps transcript reads at three per question.
• Plan and limits: Fathom’s pricing page ticks MCP on every plan, Free too. The server is at api.fathom.ai/mcp and only reaches meetings you can already see. On Claude Team or Enterprise, an owner must switch it on first (Fathom’s help article).

One Reddit user connected it to Claude for “checking if the Sales reps actually mentioned the new feature, where/how, which call etc.” (u/Virtual_Service610, r/ProductManagement, July 2026). For the full tool list, see our Fathom MCP test.

Fireflies MCP: The Best Free All-Rounder, With a Transcript Problem

Fireflies’ MCP scored 18/18 in Round 1 and 10/10 on its AI notes, all on a free plan. It gave us the line of the week, transcribing “Orrimsby test call one” as “Orrimsby, Testicle one”.

• Right: it named me as the person who said the canary code, splitting speakers sentence by sentence. Its notes got all 10 facts right, and the link built from its instructions opened the recording at the overtime line.
• Wrong: it heard “TMIN and Rook” for a customer called Tewin and Rook. Most transcript fetches returned “Partial results: some fields may be missing”, and one Bad Gateway cleared on a retry.
• Plan and limits: the server is at api.fireflies.ai/mcp and works on the free plan, at 30 requests a minute and 500 a day. On Free, the limits page says your first API request starts a 14-day trial at Pro limits. The tools reference lists 19 tools, and my connector showed four. Pro is $10 per seat per month billed annually.

Otter MCP: Zero Errors, Missing One Call

Otter’s MCP scored 15/15 on a free plan, with zero errors across 21 tool calls. It never joined our third call, which is why its Round 1 is scored out of 15.

• Right: it named me for the canary code and got all 10 facts in its notes. Asked about the call it missed, it said so plainly.
• Wrong: it never joined the 09:45 Calder Vale renewal call, our third of the day. Its keyword search missed “clocking”, a word that was in a transcript, and it listed every action twice.
• Plan and limits: the server is at mcp.otter.ai/mcp with three tools (Otter’s help article). Otter’s pricing page lists the MCP server on the free Basic plan.

Read AI MCP: The Only One That Hands Over the Recording

Read AI’s MCP scored 18/18 on a free plan and was the only one to give Claude a download link for the actual recording. Its AI notes were the least accurate of the five that produced them, at 8/10.

• Right: it named me for the canary code and handed over the full .mp4 through a link valid for an hour. It returned structured notes with chapter summaries and action items.
• Wrong: its own search couldn’t find GLASSWICK-77, one search came back “already running” until retried, and it transcribed Priya as “Freya”. It gives clock times, not links to that moment.
• Plan and limits: the server is at api.read.ai/mcp and is in open beta (Read AI’s MCP article). Read AI says MCP is on all plans, Free included. Its folder tools arrived October 15, 2026.

Granola MCP: No Bot, Notes Only When You Ask

Granola’s MCP scored 18/18 on a Business trial with the fewest tool calls of any tool (11). It records from your laptop, so there’s no bot in the meeting. It had no AI notes for our demo call until I pressed Generate in the Granola app.

• Right: 18/18 with no errors in the scored run, and once generated, its notes covered all 10 facts.
• Wrong: it labels speakers by device (“Microphone”, “System audio”), not person. When my colleague repeated PEBBLEMARSH-31, it heard “Apple March 31st”. On October 22 the connector dropped to one feedback tool until I reconnected it.
• Plan and limits: the server is at mcp.granola.ai/mcp, and it’s Granola’s own official server. On the free plan, Granola’s MCP docs say it reaches only your own notes from the last 30 days, with no transcript tool, so my full-transcript question probably wouldn’t have worked there. Our Granola review covers that free-plan wall.

tl;dv MCP: Where It Wins and Where It Doesn’t

tl;dv’s MCP scored 18/18 and 33 of 34 overall, joint second with Otter. Its AI notes scored 9/10, with every bullet linked to the moment in the recording. It needs Pro, at $18 / €18 per seat per month billed annually or $29 / €29 monthly.

• Where it won: joint top on jumping to the moment, with the overtime link landing five seconds after the line. Its notes came back structured into action items, commercial, technical and timeline sections. Its only error was one badly formatted date search, which worked on a retry.
• Where it didn’t: tl;dv’s help center says the meeting organizer must be on Pro too. In my colleague’s recordings it hedged with “Speaker 00” instead of a name, and its notes gave two of my actions to the customer. It also wrote the canary code as “Glaswix 77”.
• Plan and limits: the connector is in Claude’s directory at mcp.tldv.io/mcp. It’s read-only and returns up to 50 meetings per query. tl;dv’s docs list five tools; my connector showed three. There’s also a self-hosted server for Business and Enterprise plans.

tl;dv also runs its own transcription benchmarks, separate from this test and on different audio, including Japanese and Finnish.

How Much Does Each Meeting MCP Server Cost?

Four of the six meeting MCP servers work on a free plan: Fathom, Fireflies, Otter and Read AI. Granola’s free plan has MCP with tight limits, and tl;dv’s MCP starts at Pro. Otter’s Pro is the cheapest paid plan of the six, at $8.33 per seat per month billed annually.

Prices in USD from each vendor’s pricing page, checked October 22 and 23, 2026. tl;dv’s euro prices checked from France on October 23.

Why the Transcript Under the MCP Matters

A meeting MCP server can only give Claude what the transcript says. Every MCP in my test handled the same questions well, but the transcripts underneath turned a project code into “Apple March 31st” and a customer called Tewin and Rook into “TMIN and Rook”.

In my test, the damage showed up in search: Read AI couldn’t find GLASSWICK-77, and Otter’s search missed “clocking”.

Every tool slipped somewhere, tl;dv included. If your meetings aren’t in English, check a transcription benchmark for your language before you pick an MCP.

Should You Use tl;dv’s MCP?

Pick tl;dv’s MCP if your team records calls with tl;dv and wants Claude to take you straight to the moment something was said. It tied first on jumping to the moment in my test, and every AI-note bullet it returned linked to that point in the recording.

tl;dv offers EU data hosting and doesn’t train its AI on your recordings, which matters if you sell into Europe. The trade-off is price: you’ll need Pro.

To try it, start a tl;dv Pro trial, add tl;dv from Claude’s directory and follow our setup guide for connecting meeting notes to Claude. Then ask it something you’d normally dig through recordings for, like the last time a customer mentioned a competitor, and click through to it.

FAQ

Which meeting notetakers have an MCP server?

Granola, Fathom, Fireflies, Otter, Read AI and tl;dv all have official MCP servers, and those are the six I tested in October 2026. Gong and Zoom have servers too; see our Gong MCP and Zoom MCP guides. Each server address, the cheapest plan that includes it and the limits worth knowing are in the pricing table.

Which meeting MCP servers work on a free plan?

Fathom, Fireflies, Otter and Read AI include MCP on their free plans. Granola’s free plan only reaches your own notes from the last 30 days, and tl;dv’s MCP needs Pro. Fireflies’ free limit is 30 requests a minute and 500 a day, not the 50 a day some AI answers quote.

Can a meeting MCP server change my meetings, or only read them?

It depends on the tool. Fireflies’ docs list tools that share meetings and move them between channels. Read AI’s can share reports and manage folders. Granola’s connector can create folders and move notes in and out of them (I saw 14 tools on October 23). Otter only reads your meetings, and tl;dv’s connector is read-only too.

Do meeting MCP servers make things up?

Not on my trap question. Asked what discount we’d offered a customer who was never offered one, all six said none, and Otter said plainly it had no recording of the call it missed. The mistakes came from the transcripts underneath, from misspelled names and codes, and from AI notes giving actions to the wrong person.

Does transcript accuracy matter if the MCP is good?

Yes. The MCP can only pass Claude what the transcript says. In my test, a project code became “Apple March 31st” and every tool misspelled at least one name or code, tl;dv included. In English, on clean audio, Claude still got the answers right. If your calls aren’t in English, check a transcription benchmark for your language first.