ChatGPT Creates a New MCP Session for Every Tool Call. Claude Doesn't.

Apr 7, 2026

MCPR Team

We caught something weird.

We’re building mcpr, an open-source proxy for MCP servers. Our cloud dashboard tracks every MCP request at the protocol level — including session lifecycle. Initialize calls, tool invocations, session IDs, latencies. Everything.

While monitoring a production MCP server that serves both ChatGPT and Claude simultaneously, we noticed a pattern that made us do a double-take:

ChatGPT: 2 tool calls. 2 separate sessions. Claude: 2 tool calls. 1 session.

Same server. Same tools. Same protocol. Completely different behavior.

Let us show you.

The raw data

Here’s exactly what our dashboard recorded for a simple interaction where the AI calls two tools back-to-back.

ChatGPT — one session per tool call

Session 1:
  04:02:40 PM  initialize              3ms   ok
  04:02:40 PM  tools/call  create_matching_question  12ms  ok
  -- session ended --

Session 2:
  04:03:47 PM  initialize              3ms   ok
  04:03:47 PM  tools/call  submit_answer            12ms  ok
  -- session ended --

Two sessions. Two full initialize handshakes. Each session lives for roughly one second — just long enough to shake hands, call a tool, and disappear.

ChatGPT creates two separate MCP sessions for two tool calls

Claude — one session, many calls

Session 1:
  02:03:06 PM  initialize              30ms  ok
  02:03:07 PM  tools/list              11ms  ok
  02:03:08 PM  resources/list          14ms  ok
  02:03:38 PM  resources/read           4ms  ok
  02:03:41 PM  tools/call  create_cloze_question    35ms  ok
  02:03:45 PM  tools/call  get_latest_answer         6ms  ok
  -- session ended --

One session. One initialize. Claude even runs discovery — tools/list, resources/list, resources/read — before making any tool calls. All within the same session. Total duration: 39 seconds.

Claude reuses a single MCP session across multiple tool calls

Why this matters more than you think

1. Initialize is not free

Every MCP initialize is a full handshake. The client sends its capabilities, the server responds with its own, they negotiate a protocol version. Some servers also load config, set up database connections, or warm caches during init.

ChatGPT pays this cost on every single tool call. Claude pays it once.

A conversation that triggers 10 tool calls means 10 handshakes on ChatGPT vs 1 on Claude. If your initialize takes 30-50ms — which is modest — you’re adding 300-500ms of pure overhead that your users feel but can’t explain.

And that’s the optimistic case. We’ve seen MCP servers where initialize fetches user preferences, loads database schemas, or warms embedding caches. If your init takes 200ms, ten tool calls just cost you two full seconds of invisible latency on ChatGPT.

2. Your in-memory state is gone

This is the sneaky one. The silent killer.

If your MCP server stores anything in memory per session — user context, conversation history, cached API responses, computed state — ChatGPT will destroy it between tool calls.

# This pattern works perfectly on Claude.
# On ChatGPT, it's a landmine.

session_cache = {}

async def handle_initialize(session_id):
    session_cache[session_id] = {"user": None, "history": []}

async def handle_tool_call(session_id, tool, args):
    # On Claude: same session_id, cache hit, everything works
    # On ChatGPT: NEW session_id, cache miss, data is gone
    cache = session_cache.get(session_id)  # None on ChatGPT!

You test on Claude. Everything works. State persists across tool calls. You ship it. Then ChatGPT users start reporting bugs — results missing context, follow-up calls returning empty data, conversations that seem to “forget” what just happened.

The worst part? Your server logs show zero errors. Every individual request succeeds. The failure is between requests, in the gap where your state quietly vanishes.

3. Tool discovery follows different paths

Look at the session data again. Claude calls tools/list and resources/list during the session. It discovers what’s available, reads resources, then acts on what it learned.

ChatGPT skips all of this. It goes straight to initialize then tools/call. No discovery phase. This suggests ChatGPT caches the tool schema externally and doesn’t need to rediscover it per session — which makes sense given the disposable session model.

This is actually clever engineering on ChatGPT’s side: if you’re going to throw away the session anyway, why waste time discovering what you already know?

How to build MCP servers that survive both models

The rule is simple: design for the worst case.

Make initialize blazing fast

ChatGPT will call it constantly. Every millisecond in init multiplies across every tool call in a conversation.

# Bad: heavy init that ChatGPT will pay for on every tool call
async def handle_initialize(session_id):
    await load_database_schema()      # 200ms
    await warm_embedding_cache()       # 500ms
    await fetch_user_preferences()     # 100ms
    # Total: 800ms per tool call on ChatGPT

# Good: return immediately, defer everything
async def handle_initialize(session_id):
    return {"capabilities": {...}}     # < 5ms

Go stateless or go home

Don’t rely on session-scoped state. Period. Use external persistence keyed on something stable — user ID, API key, anything that survives a session reset.

# Fragile: dies on ChatGPT
session_state = {}

# Robust: works everywhere
async def get_state(user_id):
    return await redis.get(f"user:{user_id}")

Pass context explicitly

If tool B depends on output from tool A, don’t cache it in session memory. Return it as structured output so the AI client can pass it back as input. Let the AI be the state carrier — it’s the only thing that actually persists across ChatGPT’s disposable sessions.

How we spotted this

A regular HTTP reverse proxy — nginx, HAProxy, Caddy — would see these as normal HTTP requests. It has no idea that two POST requests belong to different MCP sessions, or that initialize was called twice instead of once.

mcpr is different. It parses MCP JSON-RPC at the protocol level. It knows what initialize means, tracks session IDs, groups tool calls by session, and measures per-method latency. That’s how a pattern like this surfaces in the dashboard instead of hiding in raw HTTP logs.

If you’re running MCP servers in production, this kind of protocol-level visibility is the difference between guessing why things are slow and knowing.

The takeaway

ChatGPT and Claude have fundamentally different MCP session models:

	ChatGPT	Claude
Session lifetime	One tool call	Entire conversation turn
Initialize calls	Once per tool call	Once per session
In-memory state	Lost between calls	Persists within session
Tool discovery	Skipped (cached externally)	Done within session

Design for the disposable model. If your server works on ChatGPT’s session-per-call approach, it’ll work everywhere. The reverse is not true.

mcpr is an open-source MCP proxy (Apache 2.0). See session data like this in the cloud dashboard at cloud.mcpr.app.