Find an AI connection with a clearly defined job.
Explore AI app and integration discovery routes. Evaluate the specific task, data access, permissions, and review process before connecting a tool to your everyday work.
Follow the official destination, then investigate the individual listing.
Read the official guide to app connections and discovery in ChatGPT.
Explore published Model Context Protocol server entries.
Explore the Python integration reference organized by provider.
Directory names and resource descriptions are grounded in the official destinations linked above. A listing is not a compatibility guarantee, a security endorsement, or a partnership claim. Requirements and availability can change; check the provider before acting. Read our editorial approach.
Use the directory as a starting point. Evaluate the complete workflow and the responsibility that comes with it.
Start with a concrete outcome such as finding approved instructions or preparing a draft summary. Name the input, expected output, and reviewer. Then decide whether a model-assisted workflow is appropriate at all; an existing search or a clear template may meet the requirement with fewer moving parts.
The resources above include connected-app documentation, a server registry, and an integration reference. They serve different discovery needs. Follow the one that matches the environment you intend to use, and verify each candidate’s current capabilities from its own documentation.
List the information the candidate needs and distinguish it from information that merely happens to be in the same account. Start with a small, approved test collection. Read the applicable provider documentation for access and data handling before expanding the pilot.
A connection that retrieves content is not the same as one that edits, sends, or publishes it. For changes, inspect the approval experience and the proposed destination. Test denial, ambiguity, and recovery with harmless examples rather than relying on one successful demonstration.
Prepare representative examples, including incomplete requests and material that should lead to clarification. Ask a reviewer to check the output against the approved inputs. Record omissions, unsupported statements, incorrect destinations, and unnecessary actions separately.
Include verification effort, subscriptions, and account administration in the decision. Save the approved workflow in a form another team member can understand, and assign an owner to review permissions and product changes. A useful integration needs an exit route as well as an install route.
Keep the scope of your project visible while comparing options.
No. It provides discovery links and evaluation guidance. Connections and permissions are handled by the relevant provider in its own product.
Do not treat the labels as interchangeable. Read the resource’s current documentation and identify its platform, capabilities, connection method, and operational requirements.
Use approved sample data, a bounded task, clear review criteria, and cases with missing or ambiguous information. Review access and any ability to make changes before expanding the pilot.
Assess AI integrations by their actual workflow, data access, approval controls, and practical usefulness—not their marketing labels.
Learn how to compare model-facing tools, framework integrations, and MCP servers before connecting them to a real workflow.
Build a controlled design workflow with clear inputs, review gates, editable output, and an explicit handoff to the site owner.
