mcp-todo-server
Provides a Model Context Protocol (MCP) server that performs line-based read and edit operations on TODO.md and CHANGELOG.md files, including fetching specific line ranges, applying line-based patches, managing version sections, and importing/generating content.
Score Breakdown
⚙ Agent Friendliness
🔒 Security
Appears to be a local filesystem MCP server (TLS/auth not applicable from README). Main risk is unintended file modification if the server is pointed at the wrong path; ensure sandboxing/least-privilege filesystem permissions. Error handling is claimed but details are not provided in the README.
⚡ Reliability
Best When
You want local (filesystem) TODO/CHANGELOG manipulation via an MCP tool interface where agents can work with line ranges and patches safely within a controlled repo.
Avoid When
You need strong access control, internet-exposed multi-tenant usage, or a hosted service with SLAs and documented rate limiting.
Use Cases
- • LLM-assisted maintenance of TODO.md tasks and versioned task sets
- • Automated generation of CHANGELOG.md entries from completed TODO items
- • Programmatic line-range retrieval for context-efficient agent workflows
- • Applying structured edits to changelogs and tasks with atomic multi-file operations
Not For
- • Serving as a general-purpose document editor across arbitrary formats
- • Remote multi-user editing without appropriate auth and sandboxing
- • Use cases requiring network APIs, webhooks, or hosted SaaS guarantees
Interface
Authentication
No authentication mechanism is described in the provided README; access appears to rely on local execution and filesystem permissions/scope.
Pricing
Open-source package; no pricing info provided.
Agent Metadata
Known Gotchas
- ⚠ Line-based patching depends on correct line ranges; agents should re-read surrounding context before applying patches.
- ⚠ Without explicit idempotency notes, repeated tool calls may duplicate or corrupt task/version sections if not carefully guarded.
- ⚠ Atomic operations across multiple files are beneficial, but agents should still handle partial failure scenarios cautiously (exact behavior not documented here).
Alternatives
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Scores are editorial opinions as of 2026-04-04.