- Developers building web data extraction tools for agents
- Teams using MCP-compatible agent hosts
- Builders who need Playwright and AgentQL-style extraction in workflows
MIT ยท Plugins
AgentQL MCP
Model Context Protocol server that exposes AgentQL data extraction capabilities to AI agents.
0.2K stars 0.0K forks MIT license 2026-06-11 verified
$
# AgentQL MCP$
pip install agentql-mcp$
npx agentql-mcp --help Open sourceMCPAPI
Overview
What is AgentQL MCP?
AgentQL MCP is an open-source Model Context Protocol server that integrates AgentQL's data extraction capabilities. It is relevant for agents that need structured web data extraction through an MCP-compatible tool surface.
MCP extraction tool
AgentQL MCP exposes data extraction as a Model Context Protocol server.
MCP makes extraction reusable across compatible agent environments.AgentQL integration
The server integrates AgentQL's data extraction capabilities.
Structured extraction helps agents work with web data more reliably than raw page text.Web automation fit
Repository topics include Playwright, scraping, Cursor, Claude, and MCP.
The project sits directly in the browser-agent and web-tooling stack. Use cases
What teams use it for
Ecosystem
Tags & capabilities
Comparison
How it stacks up
When to choose AgentQL MCP
Compare it with nearby plugins by looking at hosting model, integration surface, license, and whether the official docs show the workflow you need.
FAQ
Questions
Is AgentQL MCP open source?
Yes. The GitHub repository is listed under the MIT license.
Who should evaluate AgentQL MCP?
Teams building MCP tools for web extraction, browser agents, or research automation should evaluate it.
Decision brief
JSON
Should you use AgentQL MCP?
- Teams that only need passive browsing
- Workflows where external web extraction is not allowed
- Verified 2026-06-11
- License: MIT
- Repo: tinyfish-io/agentql-mcp
- Open-source signal
Deployment
cloud
Permission surface
browser, memory, external services
Decision signals
MCP, API
Agent packet
Structured decision data for AgentQL MCP
This packet is the compact machine-readable view agents should use before following source links or taking action.
Capabilities
plugin, mcp, protocol, connectors, browser automation
Constraints
open source, mcp compatible
Deployment
cloud
Permission surface
browser, memory, external services
Recommended workflows
Browser automation, Coding agent workflow, Connector or protocol layer
Overview
What AgentQL MCP does
What it is
It gives AI agents an MCP-compatible tool surface for extracting structured data from web pages.
Why it matters
Web agents become more useful when they can extract reliable structured data rather than only browse pages.
How to evaluate it
Start with a non-sensitive page, configure the MCP server, and compare extracted data against the source before automating broader workflows.
Facts
Known metadata and operating surface
These fields are separated from editorial interpretation so agents can reason over facts and missing checks.
Resource type plugin
Category Plugins
Maturity active
Difficulty Unknown
License MIT
Pricing open source
Verified 2026-06-11
Source confidence high
Risk level elevated
Fit matrix
Where AgentQL MCP fits in an agent stack
strong
Browser automation
AgentQL MCP has multiple signals for browser automation, including matching tags, capabilities, category, or positioning.
- Run one non-sensitive website task and inspect clicks, waits, retries, and changed URLs.
- Confirm official docs, current maintenance, license, and runtime constraints before production use.
strong
Coding agent workflow
AgentQL MCP has multiple signals for coding agent workflow, including matching tags, capabilities, category, or positioning.
- Run a small repository change and inspect the diff, tests, and rollback path.
- Confirm official docs, current maintenance, license, and runtime constraints before production use.
strong
Connector or protocol layer
AgentQL MCP has multiple signals for connector or protocol layer, including matching tags, capabilities, category, or positioning.
- Connect one low-risk service, then inspect schemas, auth scope, errors, and logs.
- Confirm official docs, current maintenance, license, and runtime constraints before production use.
partial
Memory or RAG workflow
AgentQL MCP has at least one signal for memory or rag workflow, but should be checked against a real task before adoption.
- Create, update, retrieve, correct, and delete memory or retrieval objects with real data.
- Confirm official docs, current maintenance, license, and runtime constraints before production use.
partial
Reusable skill workflow
AgentQL MCP has at least one signal for reusable skill workflow, but should be checked against a real task before adoption.
- Run one skill end to end and check whether it produces evidence or structured output.
- Confirm official docs, current maintenance, license, and runtime constraints before production use.
weak
Evaluation and observability
AgentQL MCP is not primarily positioned for evaluation and observability in the current metadata.
- Add one repeatable test case and confirm results can run again in review or CI.
- Confirm official docs, current maintenance, license, and runtime constraints before production use.
Inputs and outputs
What an agent should inspect
Likely inputs
- Web pages, DOM state, screenshots, forms, or browser sessions
- Repositories, files, issues, terminal output, and test results
- Tool schemas, API requests, service resources, and auth scopes
- Prompts, messages, documents, images, or model inputs
- Official setup instructions and a small real workflow
Likely outputs
- Action traces, changed pages, extracted data, or completed browser steps
- Diffs, commits, explanations, test results, or review notes
- A decision on whether this resource fits the target workflow
Evidence
Sources, claims, and missing checks
Claims are marked separately from source links so future crawlers and reviewers can update them without rewriting the page.
GitHub github
Repository source for code, license, issues, releases, and implementation details.
Homepage homepageOfficial or project-controlled source for this resource profile.
AgentQL MCP is listed as open source.
License metadata: MITAgentQL MCP has a recorded GitHub repository: tinyfish-io/agentql-mcp.
Resource facts and GitHub source link.AgentQL MCP supports these recorded deployment modes: cloud.
OpenAgent decision signal metadata.AgentQL MCP is tagged with plugin, mcp, protocol, connectors, browser automation capabilities.
OpenAgent capability taxonomy.- Dedicated docs link is missing.
- Repository freshness has not been recorded.
Next action
How to start evaluating AgentQL MCP
Inspect repository
Check license, recent activity, issues, examples, and security-sensitive code paths.
Open sourceOpen Homepage
Start from the official source before adopting third-party instructions.
Open source Compare
Alternatives and nearby resources
Use related resources to compare category fit, license, deployment model, and first-workflow behavior.
FAQ
Common questions about AgentQL MCP
Is AgentQL MCP open source?
Yes. The GitHub repository is listed under the MIT license.
Who should evaluate AgentQL MCP?
Teams building MCP tools for web extraction, browser agents, or research automation should evaluate it.