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AgentQL

BYtinyfish-io139GRADE B

Enable AI agents to get structured data from unstructured web with [AgentQL](https://www.agentql.com/).

Config Installation

Add this to your claude_desktop_config.json:

{
  "mcpServers": {
    "agentql": {
      "command": "npx",
      "args": [
        "-y",
        "@modelcontextprotocol/server-agentql"
      ]
    }
  }
}

* Note: Requires restart of Claude Desktop app.

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Adoption Framework for AgentQL

Before installing any skill, define a clear objective and measurable outcome. A useful implementation question is: what workflow becomes faster, safer, or more reliable after this skill is active? If that answer is vague, delay rollout and tighten scope first.

For most teams, a low-risk pattern is preview-first rollout with one owner, one test scenario, and one rollback plan. Capture failures in a structured log so quality decisions are evidence-based. This is especially important for skills that touch file systems, external APIs, or automation chains with downstream side effects.

  • Define success metrics before installation.
  • Validate permission scope against policy boundaries.
  • Run one controlled pilot and document failure categories.
  • Promote only after acceptance checks pass consistently.

Pre-Deployment Review Questions

Use these questions before enabling the skill in shared environments. They reduce surprise incidents and make approval decisions consistent across teams.

  • What data can this skill read, write, or transmit by default?
  • Which failures are recoverable automatically and which require manual stop?
  • Do we have verifiable logs that prove safe behavior under load?
  • Is rollback tested, documented, and assigned to a clear owner?

If any answer is unclear, keep rollout in preview and close the gap before production use.

Editorial Review Snapshot

This listing includes an editorial QA layer in addition to automated rendering. Review status is based on documentation depth, content uniqueness, and operational safety signals from the upstream repository.

  • Last scan date: 2026-01-18
  • README depth: 675 words
  • Content diversity score: 0.50 (higher is better)
  • Template signal count: 0
  • Index status: Quality review hold (noindex)

Recommendation: Keep this skill in review mode. Add a richer implementation guide and concrete failure-mode evidence before requesting broader index coverage.

This page remains accessible to users, but it is excluded from search indexing until editorial enrichment is complete.

Skill Implementation Board

Actionable utility module for rollout decisions. Use the inputs below to choose a deployment path, then execute the checklist and record an output note.

Input: Security Grade

B

Input: Findings

0

Input: README Depth

675 words

Input: Index State

Hold

Decision TriggerActionExpected Output
Input: risk band moderate, docs thin, findings 0Run a preview pilot with fixed ownership and observability checkpoints.Pilot can start with rollback checklist attached.
Input: page is on index holdKeep rollout internal and add concrete implementation evidence.Editorial unblock ticket with measurable proof points.
Input: context tags/scenarios are missingDefine two concrete scenarios before broad rollout.Clear scope definition before further deployment.

Execution Steps

  1. Capture objective, owner, and rollback contact.
  2. Run one preview pilot with fixed test scenario.
  3. Record warning behavior and recovery evidence.
  4. Promote only if pilot output matches expected threshold.

Output Template

skill=agentql
mode=B
pilot_result=pass|fail
warning_count=0
next_step=rollout|patch|hold

🛡️ Security Analysis

SCANNED: 2026-01-18
SCORE: 80/100

Clean Scan Report

Our static analysis engine detected no common vulnerabilities (RCE, API Leaks, Unbounded FS).

DocumentationREADME.md

Note: The content below is automatically rendered from the repository's README file.

AgentQL MCP Server

This is a Model Context Protocol (MCP) server that integrates AgentQL's data extraction capabilities.

Features

Tools

  • extract-web-data - extract structured data from a given 'url', using 'prompt' as a description of actual data and its fields to extract.

Installation

To use AgentQL MCP Server to extract data from web pages, you need to install it via npm, get an API key from our Dev Portal, and configure it in your favorite app that supports MCP.

Install the package

npm install -g agentql-mcp

Configure Claude

  • Open Claude Desktop Settings via +, (don't confuse with Claude Account Settings)
  • Go to Developer sidebar section
  • Click Edit Config and open claude_desktop_config.json file
  • Add agentql server inside mcpServers dictionary in the config file
  • Restart the app
{
  "mcpServers": {
    "agentql": {
      "command": "npx",
      "args": ["-y", "agentql-mcp"],
      "env": {
        "AGENTQL_API_KEY": "YOUR_API_KEY"
      }
    }
  }
}

Read more about MCP configuration in Claude here.

Configure VS Code

For one-click installation, click one of the install buttons below:

Install with NPX in VS Code Install with NPX in VS Code Insiders

Manual Installation

Click the install buttons at the top of this section for the quickest installation method. For manual installation, follow these steps:

Add the following JSON block to your User Settings (JSON) file in VS Code. You can do this by pressing Ctrl + Shift + P and typing Preferences: Open User Settings (JSON).

{
  "mcp": {
    "inputs": [
      {
        "type": "promptString",
        "id": "apiKey",
        "description": "AgentQL API Key",
        "password": true
      }
    ],
    "servers": {
      "agentql": {
        "command": "npx",
        "args": ["-y", "agentql-mcp"],
        "env": {
          "AGENTQL_API_KEY": "${input:apiKey}"
        }
      }
    }
  }
}

Optionally, you can add it to a file called .vscode/mcp.json in your workspace. This will allow you to share the configuration with others.

{
  "inputs": [
    {
      "type": "promptString",
      "id": "apiKey",
      "description": "AgentQL API Key",
      "password": true
    }
  ],
  "servers": {
    "agentql": {
      "command": "npx",
      "args": ["-y", "agentql-mcp"],
      "env": {
        "AGENTQL_API_KEY": "${input:apiKey}"
      }
    }
  }
}

Configure Cursor

  • Open Cursor Settings
  • Go to MCP > MCP Servers
  • Click + Add new MCP Server
  • Enter the following:
    • Name: "agentql" (or your preferred name)
    • Type: "command"
    • Command: env AGENTQL_API_KEY=YOUR_API_KEY npx -y agentql-mcp

Read more about MCP configuration in Cursor here.

Configure Windsurf

  • Open Windsurf: MCP Configuration Panel
  • Click Add custom server+
  • Alternatively you can open ~/.codeium/windsurf/mcp_config.json directly
  • Add agentql server inside mcpServers dictionary in the config file
{
  "mcpServers": {
    "agentql": {
      "command": "npx",
      "args": ["-y", "agentql-mcp"],
      "env": {
        "AGENTQL_API_KEY": "YOUR_API_KEY"
      }
    }
  }
}

Read more about MCP configuration in Windsurf here.

Validate MCP integration

Give your agent a task that will require extracting data from the web. For example:

Extract the list of videos from the page https://www.youtube.com/results?search_query=agentql, every video should have a title, an author name, a number of views and a url to the video. Make sure to exclude ads items. Format this as a markdown table.

[!TIP] In case your agent complains that it can't open urls or load content from the web instead of using AgentQL, try adding "use tools" or "use agentql tool" hint.

Development

Install dependencies:

npm install

Build the server:

npm run build

For development with auto-rebuild:

npm run watch

If you want to try out development version, you can use the following config instead of the default one:

{
  "mcpServers": {
    "agentql": {
      "command": "/path/to/agentql-mcp/dist/index.js",
      "env": {
        "AGENTQL_API_KEY": "YOUR_API_KEY"
      }
    }
  }
}

[!NOTE] Don't forget to remove the default AgentQL MCP server config to not confuse Claude with two similar servers.

Debugging

Since MCP servers communicate over stdio, debugging can be challenging. We recommend using the MCP Inspector, which is available as a package script:

npm run inspector

The Inspector will provide a URL to access debugging tools in your browser.

Related Use Cases

AE
AgentSkillsHub Editorial TeamAI Agent Infrastructure Reviewers

The AgentSkillsHub editorial team evaluates MCP servers, Claude skills, and AI agent integrations for security, reliability, and practical deployment readiness. Every listing undergoes permission audit, README analysis, and operational risk triage before publication.

  • Reviewed 450+ MCP server repositories
  • Developed security grading methodology (A-F)
  • Published agent deployment safety guidelines
Published: 2026-01-18Updated: 2026-05-07github