"deepseek" matching MCP servers:

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.

Enables Claude Code to delegate bounded repository tasks to DeepSeek as a local sub-agent, handling exploration, routine changes, and test runs within a controlled workspace and budget.

Enables a Codex/GPT conversation to request independent second opinions from DeepSeek through a local stdio MCP tool and receive provider-reported model metadata for response or synthesis.

Enables deep, recursive multi-source scientific literature research across 8+ academic databases, producing cited, evidence-graded reports with red-team review and cost details through an outline-first, user-confirmed workflow.

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.

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.

Enables any MCP-compatible AI coding client such as Claude Code, Codex, or Cursor to run web searches and fetch pages as Markdown through a self-built search engine, with per-day and per-month spend caps, prompt-injection fencing on retrieved content, and local usage/cost reporting. Search results can be filtered by site, language, and date, and repeated page fetches within a short window are not billed twice.

Enables MCP clients to run web searches and fetch web page contents through DeepSeek's Search and Fetch APIs, returning structured JSON results with configurable tool names and multiple transport options.

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.

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

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.

Lets any MCP-compatible agent analyze, describe, OCR, compare, locate, upload, and manage images through DeepSeek's vision model.

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 perform web search, page fetching, and multi-source research via the official search and fetch API, with deduplication, content limits, caching, and rate limiting.

Exposes canonical knowledge about DeepSeek FR (models, pricing, FAQ, official links) to MCP-compatible AI clients, enabling querying of site information without API keys.

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.

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

An MCP server for local image, speech, music, and SFX generation that preserves character identity across calls and rejects degenerate outputs. It runs with near-zero GPU idle memory and is designed for DeepSeek Harness agents.