teslamate-mcp: A Model Context Protocol server that provides access to your TeslaMate database, allowing AI assistants

by cobanov · MCP Server · ★ 140

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

How to install teslamate-mcp

git clone https://github.com/cobanov/teslamate-mcp.git
docker run --rm -e DATABASE_URL=... -p 8888:8888 ghcr.io/cobanov/teslamate-mcp:latest

Taken from the project's README on GitHub. Check the security audit before running it.

Security audit verdict: SAFE · quality 78/100

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. Full audit →

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.

mcpmcp-serverteslatesla-apiteslamate

Quick Facts

Stars140
Forks24
LanguagePython
CategoryMCP Server
LicenseMIT
Quality Score78/100
Open Issues1
Last Updated2026-10-20
Created2025-06-08
Platformsmcp, python
Est. Tokens~15k

About teslamate-mcp

TeslaMate MCP Server A Model Context Protocol server that exposes your TeslaMate PostgreSQL database to MCP-aware AI clients (Claude Desktop, Cursor, etc.) over either stdio or streamable HTTP. Features 20 tools — 18 predefined analytics queries (battery, charging, driving, efficie

Compatible Skills

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

  • data-analytics-skills — semantic(0.26)+complementary+same_lang+similar_pop+shared_platform (54%)
  • bloodhound_mcp — semantic(0.52)+same_lang+similar_pop+shared_platform (53%)
  • netbox-mcp-server — semantic(0.43)+same_lang+similar_pop+shared_platform (50%)
  • wikipedia-mcp — semantic(0.38)+same_lang+similar_pop+shared_platform (48%)

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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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