timer-mcp
A minimal MCP server that gives AI agents the ability to pause execution by waiting a specified number of seconds, with support for local and AWS AgentCore deployment.
README
⏱️ timer-mcp
<p align="center"> <img src="https://img.shields.io/badge/MCP-streamable--http-blue?style=for-the-badge" /> <img src="https://img.shields.io/badge/FastMCP-3.x-6c5ce7?style=for-the-badge" /> <img src="https://img.shields.io/badge/Python-3.12-3776AB?style=for-the-badge&logo=python&logoColor=white" /> <img src="https://img.shields.io/badge/Docker-ARM64-2496ED?style=for-the-badge&logo=docker&logoColor=white" /> <img src="https://img.shields.io/badge/AWS-AgentCore-FF9900?style=for-the-badge&logo=amazonaws&logoColor=white" /> </p>
<p align="center"> A minimal MCP server that gives AI agents the ability to <strong>wait</strong>.<br/> One tool. One job. Works locally and on AWS AgentCore Runtime. </p>
🤔 The problem it solves
Agents often need to pause between actions — waiting for a resource to become ready, spacing out API calls, or inserting a deliberate delay in a workflow. Without a timer tool, an agent has no way to wait.
A naive implementation that holds the HTTP connection open for the full duration fails on AgentCore: the platform's sidecar proxy enforces a hard ~60 second timeout on individual MCP tool calls, returning:
MCP error -32001: Request timed out
⚙️ How it works
The server caps MAX_DURATION at 55 seconds per call when running on AgentCore — safely under the proxy limit. For longer waits the agent calls the tool multiple times sequentially, which it works out automatically from the tool description:
wait(55) → "Waited 55 second(s)."
wait(35) → "Waited 35 second(s)."
──────────────────────
total: 90s across 2 calls
Locally there is no proxy, so the default
MAX_DURATIONof 840 seconds applies and a single call can hold for up to 14 minutes.
🛠️ Tool reference
| Tool | Argument | Description |
|---|---|---|
wait |
seconds: int |
Pause for the specified duration (capped at MAX_DURATION). For longer waits, call multiple times sequentially. |
🔧 Environment variables
| Variable | Default | Description |
|---|---|---|
HOST |
0.0.0.0 |
Bind address |
PORT |
8000 |
Listen port |
MAX_DURATION |
840 |
Max seconds per call. Set to 55 on AgentCore. |
🐳 Run locally
Start the server
docker build -t timer-mcp .
docker run -p 8000:8000 timer-mcp
Server is available at http://localhost:8000/mcp.
🔌 Connect to your MCP client
Add the following to your MCP client config and restart:
{
"mcpServers": {
"timer": {
"type": "http",
"url": "http://localhost:8000/mcp"
}
}
}
| Client | Config file |
|---|---|
| Claude Desktop | ~/Library/Application Support/Claude/claude_desktop_config.json |
| Claude Code | ~/.claude/mcp.json (global) or .claude/mcp.json (project) |
| Cursor | ~/.cursor/mcp.json (global) or .cursor/mcp.json (project) |
| VS Code | ~/.vscode/mcp.json (global) or .vscode/mcp.json (project) |
🧪 Smoke test
# Initialize a session
SESSION=$(curl -si -X POST http://localhost:8000/mcp \
-H "Content-Type: application/json" \
-H "Accept: application/json, text/event-stream" \
-d '{"jsonrpc":"2.0","id":1,"method":"initialize","params":{"protocolVersion":"2024-11-05","capabilities":{},"clientInfo":{"name":"test","version":"0.1"}}}' \
| grep -i 'mcp-session-id' | awk '{print $2}' | tr -d '\r\n')
# Call wait(5) — should take ~5s and return "Waited 5 second(s)."
time curl -s -N -X POST http://localhost:8000/mcp \
-H "Content-Type: application/json" \
-H "Accept: application/json, text/event-stream" \
-H "mcp-session-id: $SESSION" \
-d '{"jsonrpc":"2.0","id":2,"method":"tools/call","params":{"name":"wait","arguments":{"seconds":5}}}'
☁️ Deploy to AWS AgentCore
Prerequisites
- Docker with buildx (ARM64 support)
- AWS CLI with a profile that has ECR and AgentCore permissions
- An ECR repository (
timer-mcp) - An AgentCore execution role with ECR pull access
1. Push the image to ECR
Uncomment the ECR authentication block in deploy.sh, then run:
bash deploy.sh
This builds the ARM64 image and pushes it to:
<account-id>.dkr.ecr.us-east-1.amazonaws.com/timer-mcp:latest
2. Create the AgentCore runtime
aws bedrock-agentcore-control create-agent-runtime \
--region us-east-1 \
--agent-runtime-name "timer_mcp" \
--agent-runtime-artifact '{
"containerConfiguration": {
"containerUri": "<account-id>.dkr.ecr.us-east-1.amazonaws.com/timer-mcp:latest"
}
}' \
--role-arn "arn:aws:iam::<account-id>:role/<execution-role>" \
--network-configuration '{"networkMode": "PUBLIC"}' \
--protocol-configuration '{"serverProtocol": "MCP"}' \
--environment-variables '{"HOST":"0.0.0.0","PORT":"8000","MAX_DURATION":"55"}' \
--authorizer-configuration '{
"customJWTAuthorizer": {
"discoveryUrl": "https://cognito-idp.<region>.amazonaws.com/<pool-id>/.well-known/openid-configuration",
"allowedClients": ["<client-id>"]
}
}'
3. Update an existing runtime
AgentCore pulls the image fresh on every invocation — pushing a new latest tag to ECR is enough for code changes, no runtime update needed.
To update environment variables:
aws bedrock-agentcore-control update-agent-runtime \
--agent-runtime-id <runtime-id> \
--region us-east-1 \
--agent-runtime-artifact '{"containerConfiguration":{"containerUri":"<account-id>.dkr.ecr.us-east-1.amazonaws.com/timer-mcp:latest"}}' \
--role-arn "arn:aws:iam::<account-id>:role/<execution-role>" \
--network-configuration '{"networkMode":"PUBLIC"}' \
--protocol-configuration '{"serverProtocol":"MCP"}' \
--environment-variables '{"HOST":"0.0.0.0","PORT":"8000","MAX_DURATION":"55"}'
⚠️ AgentCore proxy timeout
This limitation does not apply to local Docker runs.
AgentCore's sidecar proxy enforces a hard ~60 second per-request timeout on MCP tools/call requests. This is not configurable via lifecycleConfiguration (which only governs session/microVM lifetime). Any wait call exceeding this receives MCP error -32001: Request timed out.
Setting MAX_DURATION=55 on the runtime leaves a 5-second safety margin. The agent chains calls automatically for longer waits:
| Total wait | Calls |
|---|---|
| ≤ 55s | 1 |
| 56s – 110s | 2 |
| 111s – 165s | 3 |
| N seconds | ceil(N / 55) |
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