SchemaCrawler AI

The SchemaCrawler AI MCP Server enables natural language interaction with your database schema using an MCP client in "Agent" mode. It allows users to explore tables, columns, foreign keys, triggers, stored procedures and more simply by asking questions like "Explain the code for the interest calculation stored procedure". You can also ask it to help with SQL, since it knows your schema. This is ideal for developers, DBAs, and data analysts who want to streamline schema comprehension and query development without diving into dense documentation.

What does this entry provide?

SchemaCrawler AI is an MCP server entry in ChatHome Skills, sourced from Docker MCP Registry and scored 100/100. As a Model Context Protocol server, it exposes tools that MCP-compatible LLM clients such as Claude, Cursor, and Copilot can call directly, extending an agent's reach beyond its built-in capabilities. ChatHome Skills discovered this entry through its daily ingest of official registries and GitHub discovery queries, and re-verifies repository facts, freshness signals, and install metadata on every run. The quality score weighs official-registry presence, a working install command, license clarity, GitHub star trajectory, and repository freshness — the full formula is on the Sources page. The install command above is ready to copy. Review the last-pushed date and repository status before adding SchemaCrawler AI to a production agent workflow; the snapshot facts below are point-in-time values from the most recent ingest.

Snapshot facts

Source: Docker MCP Registry
Quality: 100/100
Install method: docker
Repo: Registry only
Repo updated: Oct 26, 2026

Install command

docker run -i --rm mcp/schemacrawler-ai

Install method: docker

Review the upstream source before running this command.

GitHub project Homepage

Quality

100

Repo stars

Repo updated

Unknown

How is this score calculated?

Weighted across six dimensions, scored 0-100.
Provenance (25): listed in an official or curated registry.
Installability (25): a runnable install command beats a declared transport, which beats a source checkout.
Maintenance (20): recency of the last push; archived entries score zero here.
Adoption (20): repository stars on a log curve.
Documentation (10) and description quality (10): parsed manifest or SKILL.md frontmatter, and a real description rather than a placeholder.

MCP manifest

{
  "raw": "name: schemacrawler-ai\nimage: mcp/schemacrawler-ai\ntype: server\nlongLived: true\nmeta:\n  category: database\n  tags:\n    - ai\n    - database\nabout:\n  title: SchemaCrawler AI\n  description: >-\n    The SchemaCrawler AI MCP Server enables natural language interaction with\n    your database schema using an MCP client in \"Agent\" mode. It allows users\n    to explore tables, columns, foreign keys, triggers, stored procedures and\n    more simply by asking questions like \"Explain the code for the interest\n    calculation stored procedure\". You can also ask it to help with SQL,\n    since it knows your schema. This is ideal for developers, DBAs, and data\n    analysts who want to streamline schema comprehension and query\n    development without diving into dense documentation.\n  icon: https://avatars.githubusercontent.com/u/58572?v=4\nsource:\n  project: \"https://github.com/schemacrawler/SchemaCrawler-AI\"\n  branch: docker-mcp-registry\n  commit: 3dda2c1d154b707299f9ba577bb67ae4f4726daf\nrun:\n  volumes:\n    - \"{{schemacrawler-ai.volume.host_share}}:/share\"\nconfig:\n  description: Configure SchemaCrawler AI and its connection to the database\n  secrets:\n    - name: schemacrawler-ai.database_user\n      env: SCHCRWLR_DATABASE_USER\n      example: SA\n      description: \"--user Database user name\"\n    - name: schemacrawler-ai.database_password\n      env: SCHCRWLR_DATABASE_PASSWORD\n      example: super_S3cret\n      description: \"--password Database user password\"\n  env:\n    - name: SCHCRWLR_JDBC_URL\n      example: 12.12.12.12\n      value: \"{{schemacrawler-ai.url_connection.jdbc_url}}\"\n    - name: SCHCRWLR_SERVER\n      example: 12.12.12.12\n      value: \"{{schemacrawler-ai.server_connection.server}}\"\n    - name: SCHCRWLR_HOST\n      example: 12.12.12.12\n      value: \"{{schemacrawler-ai.server_connection.host}}\"\n    - name: SCHCRWLR_PORT\n      example: 9999\n      value: \"{{schemacrawler-ai.server_connection.port}}\"\n    - name: SCHCRWLR_DATABASE\n      example: public\n      value: \"{{schemacrawler-ai.server_connection.database}}\"\n    - name: SCHCRWLR_INFO_LEVEL\n      example: standard\n      value: \"{{schemacrawler-ai.general.info_level}}\"\n    - name: SCHCRWLR_LOG_LEVEL\n      example: ALL\n      value: \"{{schemacrawler-ai.general.log_level}}\"\n  parameters:\n    type: object\n    properties:\n      url_connection:\n        type: object\n        properties:\n          jdbc_url:\n            type: string\n            description: \"--url JDBC URL for database connection\"\n        required:\n          - jdbc_url\n      server_connection:\n        type: object\n        properties:\n          server:\n            type: string\n            description: \"--server SchemaCrawler database plugin\"\n          host:\n            type: string\n            description: \"--host Database host (optional)\"\n          port:\n            type: integer\n            description: \"--port Database port (optional)\"\n          database:\n            type: string\n            description: \"--database Database to connect to (optional)\"\n        required:\n          - server\n      general:\n        type: object\n        properties:\n          info_level:\n            type: string\n            description: \"--info-level How much database metadata to retrieve\"\n            default: standard\n          log_level:\n            type: string\n        required:\n          - info_level\n      volume:\n        type: object\n        properties:\n          host_share:\n            type: string\n            description: Host volume to map within the Docker container\n        required:\n          - host_share\n    anyOf:\n      - required:\n          - url_connection\n      - required:\n          - server_connection\n",
  "path": "servers/schemacrawler-ai/server.yaml"
}

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