"How to integrate an information system with DeepSeek" matching MCP servers:

Provides real-time system metrics and information through a Model Context Protocol interface, enabling access to CPU usage, memory statistics, disk information, network status, and running processes.

An MCP server that grants image recognition to text-only models like DeepSeek by forwarding images to vision models and returning text descriptions. Supports clipboard, pasted session images, and batch folder image recognition.

Provides tools to monitor host system health including CPU load, disk usage, and network status while enabling file system management tasks like searching and moving files. It includes built-in safety guards to prevent operations on critical system directories.

Run DeepSeek as a real sub-agent inside Claude Code / Codex CLI — not just a single LLM call. DeepSeek gets its own 7-tool agent loop (Read/Write/Edit/Bash/Glob/Grep/NotebookEdit) inside a sandboxed workspace.

Enables AI coding agents like Claude Code or Codex to delegate tasks to a DeepSeek Harness subagent with its own context window, providing tools for task delegation, result waiting, continuation, and supervision with sandboxed execution.

MCP server for DeepSeek AI models (Chat + Reasoner). Supports multi-turn sessions, model fallback with circuit breaker, function calling, thinking mode, JSON output, multimodal input, and cost tracking.

Governance framework for LLM agents. Exposes seven mechanically-enforced rules plus tools for preflight file protection, audit-chain verification, session handoff and worklog templates, MCP config auditing, and an emergency kill switch.

Generates or reviews Git unified diffs via DeepSeek API with local path validation, enabling secure patch suggestions without scanning repositories.

A Model Context Protocol (MCP) server that provides access to DeepSeek-R1's reasoning capabilities, allowing non-reasoning models to generate better responses with enhanced thinking.

Enables AI agents to debug and repair historical Solaris/SPARC systems by providing an out-of-band control plane with guest DTrace, QEMU monitor access, SPARC-aware GDB, host eBPF/perf tracing, and an immutable evidence ledger for cross-layer hypothesis testing.

This MCP server gives pure-text LLM agents vision capabilities by integrating the free GLM-4.6V-Flash visual model, enabling image, video, and file understanding through standard MCP tools.

Provides vision capabilities to text-only LLMs by acting as a cloud vision adapter layer, enabling image understanding and OCR text extraction through a single MCP tool.

Converts AI Skills (following Claude Skills format) into MCP server resources, enabling LLM applications to discover, access, and utilize self-contained skill directories through the Model Context Protocol. Provides tools to list available skills, retrieve skill details and content, and read supporting files with security protections.

Enables integration of DeepSeek's language models with MCP-compatible applications, offering features like chat completion, custom model selection, and parameter control for enhancing language-based interactions.

An MCP server that enables LLMs to automate browser interactions using Playwright to fetch real-time Israeli weather data.

Adds image understanding to text-only coding agents via two tools: describe_image for detailed descriptions and describe_ui for structured UI reports.

Enables users to turn any API's documentation into a tested MCP server whose tools expose the API's own operations, with linting, contract tests, and live verification. It accepts OpenAPI/Swagger, Postman, RAML, WSDL, GraphQL, RSS, and HTML docs.

Enables MCP clients to generate and edit images through Codex's built-in image generation using a ChatGPT login, returning final files with metadata and optional inline previews.

MCP server that lets AI agents discover, inspect, install, and run DeepSeek Harness plugins via MCP, bridging the gap between the MCP protocol and Cordis-based plugin bundles.

Enables searching and retrieving documentation from crawled documentation sites as an MCP server, allowing coding agents to query real docs instead of relying on training data.