scheduler-mcp
Provides an MCP (Model Context Protocol) server that lets clients schedule and manage automated tasks using cron expressions. Tasks can include shell commands, HTTP/API calls, AI prompt executions (via OpenAI), and desktop reminders/notifications. It persists tasks/execution history in a SQLite DB and can run over stdio or SSE (HTTP), including an auto-discovery endpoint for MCP schema.
Score Breakdown
⚙ Agent Friendliness
🔒 Security
Potential risks: (1) Scheduled shell command execution can be abused if task inputs are attacker-controlled; sandboxing/allowlisting is not described. (2) No documented authentication/authorization for MCP tools/endpoints; exposure over network transports could permit unauthorized task creation/execution. (3) AI tasks require OPENAI_API_KEY; docs do not specify whether keys are stored securely (vs. config/environment) or whether logs redact secrets. (4) TLS requirements for SSE/HTTP transport are not specified in the README. (5) Dependency hygiene and CVE status are not verifiable from provided content.
⚡ Reliability
Best When
You control the deployment environment (local or trusted internal network), want MCP integration for an AI assistant to create/manage scheduled tasks, and are comfortable securing secrets (OpenAI key) and the host machine environment.
Avoid When
You need robust, documented authentication/authorization and rate limiting for external callers, or you cannot mitigate risks from scheduled shell execution (command injection/abuse) and outbound network calls.
Use Cases
- • Cron-based automation of recurring scripts/commands
- • Scheduled polling of external HTTP APIs
- • Automated report generation or content drafting driven by LLM prompts
- • On-user-machine reminders via desktop notifications
- • MCP client integration (e.g., Claude Desktop) to create/manage scheduled tasks
Not For
- • High-security environments without additional hardening (no explicit auth/TLS details in docs)
- • Executing untrusted shell commands or untrusted AI-generated prompts without sandboxing/validation
- • Multi-tenant production use cases that require strong per-user authorization boundaries
- • Use cases needing strict SLA guarantees or documented uptime
Interface
Authentication
Docs describe an OpenAI API key for AI tasks, but do not describe authentication/authorization for the MCP server endpoints/tools. That implies access control may be absent or deployment-provided (not documented).
Pricing
No pricing information provided; costs likely come from OpenAI usage for AI tasks plus infrastructure hosting.
Agent Metadata
Known Gotchas
- ⚠ Scheduled shell/API/AI tasks can have side effects; ensure you fully validate parameters before calling add_* tools.
- ⚠ If running over SSE, the schema auto-discovery endpoint appears on HTTP port+1; clients must know this convention.
- ⚠ No documented auth/rate-limit semantics for the MCP server tools; assume deployment may be permissive unless otherwise configured.
Alternatives
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Scores are editorial opinions as of 2026-03-30.