deepseek-agent-mcp
Related Servers
Alternatives to deepseek-agent-mcp
No user-submitted related servers found.
Related Servers
- AlicenseAqualityBmaintenanceLocal MCP server that provides DeepSeek-powered code review, file analysis, and consultation tools for coding agents, enabling read-only analysis with minimal token overhead.312 npmMIT
- FlicenseAqualityCmaintenanceMCP server wrapping DeepSeek's agent harness tools into minimal/standard modes, enabling persistent shell, file editing, search, job management, and more via natural language.2-
- AlicenseAqualityAmaintenanceMCP 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.3335 npm19MIT
- AlicenseAqualityDmaintenanceMCP server that wraps DeepSeek's AI capabilities into standard MCP tools, supporting three authentication modes including free web-based usage without API keys.6614 npm1MIT
- FlicenseAqualityBmaintenanceMCP server that enables Claude Code to delegate tasks to DeepSeek and other LLMs, offering 20 tools for code review, analysis, testing, and code generation.201-
- AlicenseNot gradedqualityBmaintenanceMCP server bridging ChatGPT to DeepSeek Harness, exposing 13 tools for task submission, status tracking, result retrieval, project/session management, and human-in-the-loop approvals.1MIT
TDQS
Scored across 2 tools
The two tools are clearly distinct: delegate_task is a stateless raw model call, while run_agent_task spawns a full agent with workspace access. The descriptions explicitly contrast them and cross-reference each other, leaving no ambiguity.
Both tools follow a consistent verb_noun pattern (delegate_task, run_agent_task), using a single naming convention that is predictable and readable.
With only 2 tools, the set is slightly under the typical well-scoped range, but given the server's narrow purpose of offering two modes of DeepSeek interaction, the count is reasonable and not excessive.
The tool surface fully covers its stated domain: a simple model query and a full agent execution. No critical operations are missing, though there are no extra lifecycle tools (e.g., cancel/status) that a broader agent workflow might expect.