agentl

Validate, plan, preview and run YAML data pipelines (ETL with LLM steps) and debug failed runs.

What does this entry provide?

agentl is an MCP server entry in ChatHome Skills, sourced from Official MCP Registry and scored 78/100. 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 agentl to a production agent workflow; the snapshot facts below are point-in-time values from the most recent ingest.

Snapshot facts

Source: Official MCP Registry
Quality: 78/100
Install method: pypi
Repo: arimayuji/agentl
Repo updated: Oct 16, 2026

Install command

uvx agentl-etl==0.9.0

Install method: pypi

Review the upstream source before running this command.

GitHub project Homepage

Quality

Repo stars

Repo updated

Oct 16, 2026

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

{
  "_meta": {
    "io.modelcontextprotocol.registry/official": {
      "status": "active",
      "isLatest": true,
      "updatedAt": "2026-10-16T02:06:04.206656Z",
      "publishedAt": "2026-10-16T02:06:04.206656Z",
      "statusChangedAt": "2026-10-16T02:06:04.206656Z"
    }
  },
  "server": {
    "name": "io.github.arimayuji/agentl",
    "title": "agentl",
    "$schema": "https://static.modelcontextprotocol.io/schemas/2025-12-11/server.schema.json",
    "version": "0.9.0",
    "packages": [
      {
        "version": "0.9.0",
        "transport": {
          "type": "stdio"
        },
        "identifier": "agentl-etl",
        "runtimeHint": "uvx",
        "registryType": "pypi",
        "packageArguments": [
          {
            "type": "positional",
            "value": "mcp"
          }
        ],
        "environmentVariables": [
          {
            "name": "AGENTL_DB",
            "description": "run store: SQLite path or postgresql:// URL (default .agentl/agentl.db)"
          },
          {
            "name": "AGENTL_PROVIDER",
            "description": "LLM provider for new_pipeline (anthropic, openai, openrouter, ...)"
          },
          {
            "name": "AGENTL_MODEL",
            "description": "model for new_pipeline"
          }
        ]
      }
    ],
    "repository": {
      "url": "https://github.com/arimayuji/agentl",
      "source": "github"
    },
    "websiteUrl": "https://agentl.com.br",
    "description": "Validate, plan, preview and run YAML data pipelines (ETL with LLM steps) and debug failed runs."
  }
}

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