| FMRS | 52 / 100 · D | 57 / 100 · C | 55 / 100 · C |
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| Reliability | 7 / 20 | 8 / 20 | 9 / 20 |
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| Security and permissions | 6 / 20 | 10 / 20 | 8 / 20 |
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| Maintenance | 15 / 20 | 16 / 20 | 13 / 20 |
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| Documentation | 13 / 20 | 11 / 20 | 12 / 20 |
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| Setup experience | 11 / 20 | 12 / 20 | 13 / 20 |
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| Best for | - Users who want a single desktop app that unifies many cloud LLM providers (OpenAI, Anthropic, Gemini, DeepSeek, etc.) and local Ollama models
- Users who need to connect to multiple external MCP tool services with local-first storage and inspectable session history (Tape/Trace)
- Users who want to reuse Agent Skills built for Claude Code, Cursor, Windsurf, and similar tools
- Developers who want coding-focused ACP agents integrated into the same chat-style model selector
| - Existing Mindwtr users who want AI assistance with task capture and processing
- Developers or power users comfortable configuring a local stdio MCP server
- Self-hosters running a Mindwtr Cloud instance who need read-only querying
| - Teams needing a unified SQL interface across cloud and SaaS providers
- Users who want AI agents to access cloud resources in a structured way
- Cloud operations, security, FinOps, and infrastructure-as-code workflows
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| Not for | - Users looking for an MCP server to add into Claude Desktop or another AI client's config (DeepChat is an MCP client/host, not a server to be added elsewhere)
- Users who want a lightweight CLI-only tool rather than a full Electron desktop application
- Users needing a native mobile app (currently Windows/macOS/Linux desktop only; mobile access is indirect, via remote control)
| - Users without Mindwtr installed or without a local SQLite database
- Teams needing multi-user collaborative writes via Cloud (Cloud mode is read-only)
- Non-technical users unfamiliar with npm package installs and environment variables
| - Users who only need local file or browser automation
- Environments that cannot grant cloud or SaaS API access
- Users expecting a tool inventory with individually named MCP tools, which the source does not provide
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| Required permissions | - Local file system read/write access for session Tape storage, Skills files (imported from folders/ZIP/URL), and app configuration
- Ability to spawn local subprocesses, used to run stdio-based MCP services and the bundled Node.js runtime
- Network access to call cloud LLM APIs, search services (e.g. Brave Search), and SSE/StreamableHTTP MCP services
- Remote-control bot credentials (Telegram/Feishu/QQBot/Discord/WeChat iLink tokens) used to bind and remotely operate desktop sessions
- Local Ollama management access to download, deploy, and run local models
| - Read/write access to the local Mindwtr SQLite database file (path set via MINDWTR_DB_PATH)
- Network access to a self-hosted Mindwtr Cloud URL when Cloud mode is configured (read-only)
- A bearer token credential for Cloud API access
| - Provider authentication must be supplied through --auth or the client configuration
- Effective permissions depend on the provider credentials and requested operations; the server supports querying and provisioning
- GitHub Actions defaults to read_only mode, which is intended as the safe CI default
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| Risks and side effects | - Third-party MCP services connected via stdio run as local processes under the user's permissions — only install MCP services from trusted sources
- Imported Skills may include optional scripts that execute with the app's permissions once enabled — vet the source before enabling
- If a remote-control bot token is leaked, an attacker could remotely control the local session, switch models, or read pending interactions via the messaging app
- Some search functionality works by simulating user browsing of search engines, which may raise terms-of-service considerations for target sites
- The README describes encryption/obfuscation interfaces as "reserved" capabilities — verify what is actually enabled in a given release before relying on it
| - The server has write access to the local database, so erroneous AI-issued changes could corrupt or pollute local task data
- Storing the Cloud bearer token insecurely in environment variables risks credential exposure
- Task and project data is exposed to whichever AI model is connected, with no additional sandboxing on scope
| - Provisioning or modifying resources can cause real infrastructure changes
- Overprivileged credentials can broaden the impact across cloud or SaaS resources
- AI-generated SQL or API operations should be reviewed before execution
- Cross-cloud operations may create cost, compliance, and security consequences
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| Supported clients | | Claude Desktop | Claude, VS Code, Cursor |
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| Tools | 0 | 0 | 0 |
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