doris-mcp-server: Apache Doris MCP Server

by apache · MCP Server · ★ 353

Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h

How to install doris-mcp-server

pip install doris-mcp-server
pip install doris-mcp-server==0.6.0

Taken from the project's README on GitHub.

Security audit verdict: SAFE · quality 72/100

None of the 42 rules matched — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against 42 published rules built on the SlowMist agent-security taxonomy, refreshed every 8 hours.

The quality score combines nine heuristic checks of the repo and its README: completeness, clarity, specificity, examples, structure, agent readiness, step-by-step content, instruction quality and security notes. It rates how well the project explains itself to an agent, not the code.

ailakehousemcpolapquery-enginereal-time

Quick Facts

Stars353
Forks104
LanguagePython
CategoryMCP Server
LicenseApache-2.0
Quality Score72/100
Open Issues28
Last Updated2026-10-23
Created2025-05-06
Platformsmcp, python
Est. Tokens~25k

About doris-mcp-server

Doris MCP Server Doris MCP (Model Context Protocol) Server is a backend service built with Python and FastAPI. It implements the MCP, allowing clients to interact with it through defined "Tools". It's primarily designed to connect to Apache Doris databases, potentially leveraging Large Language Models (LLMs) for tasks like converting natural language queries to SQL (NL2SQL), executing queries,...

Compatible Skills

These tools work well together with doris-mcp-server for enhanced workflows:

  • llmtop — semantic(0.17)+complementary+same_lang+similar_pop+shared_platform (51%)
  • mcp-apache-spark-history-server — semantic(0.51)+same_lang+similar_pop+shared_platform (48%)
  • opennews-mcp — semantic(0.36)+same_lang+similar_pop+shared_platform (47%)
  • davinci-resolve-mcp — semantic(0.33)+same_lang+similar_pop+shared_platform (46%)
  • MCPTest — semantic(0.47)+same_lang+similar_pop+shared_platform (46%)

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How this security grade is produced

Grades come from a rule-based scan built on the SlowMist agent-security taxonomy: 42 published rules, covering credential harvesting, data exfiltration, curl | sh installers and more. It is a first-layer scan, not a manual audit — we say so rather than overstate it.

The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.

Sources & who's responsible:

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