| FMRS | 60 / 100 · C | 84 / 100 · B | 79 / 100 · B |
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| Reliability | 10 / 20 | 17 / 20 | 12 / 20 |
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| Security and permissions | 12 / 20 | 16 / 20 | 18 / 20 |
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| Maintenance | 14 / 20 | 16 / 20 | 15 / 20 |
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| Documentation | 12 / 20 | 17 / 20 | 19 / 20 |
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| Setup experience | 12 / 20 | 18 / 20 | 15 / 20 |
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| Best for | - Researchers and data scientists who want to perform complex data analysis through natural language
- Rapid data exploration and cleaning in HPC environments without writing repetitive code
- AI-assisted data preprocessing and feature engineering tasks
| - Local assistants that need to work inside explicitly approved directories
- Individuals and development teams that want path-based data boundaries
| - Serious Obsidian users who want AI agents reading and writing their vault
- Users wanting self-hosted, plugin-free operation with no external APIs
- Mobile/multi-device workflows accessing the vault remotely
- Security-conscious users (OAuth 2.1, atomic writes, container hardening)
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| Not for | - Large-scale analyses requiring real-time streaming or distributed processing
- Tasks needing visual chart outputs (this server provides data operations only, no plotting)
- Non-pandas users or those unfamiliar with Python data analysis
| - Highly sensitive environments that cannot allow model access to local file contents
- Shared remote file-service use cases
| - Users unwilling to run Docker or self-host a server
- Non-Obsidian note tools (Notion, Logseq, etc.)
- Scenarios requiring only a stdio local process without an HTTP server
- Remote multi-device sync without an Obsidian Sync subscription (the remote image requires one)
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| Required permissions | - This server uses local file system read/write via stdio, so it can read and write CSV, Excel, JSON, and other files specified in AI conversations
- Execution runs pandas/numpy code, consuming local CPU and memory resources
| - Read access to every local directory listed in the configuration
- Filesystem write access when write or move tools are enabled
| - Read/write access to the Obsidian vault folder (bind mount /vault, rw)
- Persistent /data volume (search index, OAuth token DB, logs)
- MCP_AUTH_TOKEN as Bearer token (also the JWT signing key)
- Obsidian Sync token for headless sync in remote mode
- Local download of embedding/reranker models (~45MB total), no external API calls
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| Risks and side effects | - Loading extremely large files may cause memory exhaustion or slow client responses; consider using profile_csv to inspect data size first
- User-provided data may contain sensitive information; file paths and contents are sent to the AI model, so privacy should be considered
- As part of a research project, the server is not an official pandas component and may have edge cases not fully covered
| - Sensitive files inside an allowed directory may enter model context
- Write and move tools change real files; keep scopes narrow and maintain backups
| - The server can read and write personal notes — guard MCP_AUTH_TOKEN carefully; leaking it exposes the whole vault
- Writes to real note files; despite atomic writes and protected paths, misconfiguration can alter data
- OAuth token DB lives on the /data volume; container compromise could expose valid sessions
- Remote deployments expose a public port — set PUBLIC_URL and reverse proxy correctly
- The remote image bundles proprietary obsidian-headless (not MIT-licensed); requires an active Obsidian Sync subscription
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| Supported clients | Claude Desktop | Claude Desktop, Cursor, Cline, Windsurf | Claude Code, Claude Desktop, claude.ai, Cursor, OpenCode, MCP Inspector |
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| Tools | 16 | 6 | 30 |
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