Atlas researches a third-party API before you integrate it in Python by leaving the repo when the answer is not in the repo. websearch finds the current documentation page, webfetch pulls it with format markdown or text so the Accept header steers the server toward a compact representation, and both sit behind explicit permissions so the model cannot quietly exfiltrate context to arbitrary hosts. You then write the client with write or edit against the real signatures, check the pattern against your codebase with grep, and prove it with pytest.
How do you get current API docs into an AI agent before writing Python?
Atlas pulls current API documentation into context with two tools. websearch finds the documentation page and injects the current year, 2026, into its description so the model biases toward fresh sources. webfetch then retrieves the page, and only after that do you write the Python client.
The job to be done is getting the current shape of an external API into context before writing the integration, instead of guessing from memory. A model that guesses at a payments or messaging API will produce a Python client that imports plausible names, passes plausible parameters, and fails at runtime. websearch closes that gap by finding the live page rather than recalling one, and its description carries the current year so stale blog posts rank lower than the vendor's own docs. Nothing about the resulting integration depends on the model remembering an SDK correctly.
How does webfetch pull documentation for a Python integration?
Atlas fetches a documentation page with webfetch, passing format markdown or text so the Accept header steers the server toward a compact representation instead of a script-heavy page. The fetched content lands in context, and 3 things become the spec for your Python client: endpoint names, parameters, and error shapes.
webfetch supports format negotiation for markdown, text, or html, which matters because documentation sites often ship a lightweight representation that is far cheaper to put into a model's context than the rendered page. Once the docs are in context, writing the Python integration is a transcription job rather than a recall job: the request signature in the docs becomes the function signature in your module, and the documented error codes become the exceptions you actually handle. uv resolves whichever HTTP client the project already pins in pyproject.toml or requirements.txt, so the integration matches the environment.
Can Atlas fetch a URL without asking? How webfetch permissions work
Atlas asks first. Approve the webfetch permission prompt, which asks with the URL as the pattern before any request goes out, so no request leaves your machine unapproved. Every Atlas tool call is permission-gated against allow, ask, and deny rules before it runs, and webfetch is no exception in 2026.
Network access is the point where an agent could quietly send context somewhere it should not. Atlas handles that with explicit permissions on both websearch and webfetch, so the model cannot quietly exfiltrate context to arbitrary hosts. The permission prompt shows the URL as the pattern, which means you can allow a vendor's documentation host while leaving everything else on ask or deny. For a Python team working under a compliance regime, that is the difference between an agent that can read the docs and an agent that can talk to anything.
How do you write a Python API client from fetched documentation?
Atlas writes the Python integration with 1 of 2 tools, write or edit, once the fetched documentation is in context, using the real endpoint names, parameters, and error codes from the page. Atlas computes a unified diff for every file edit and surfaces it for approval before writing, so nothing lands unreviewed.
Writing the client is where the earlier discipline pays off: the module Atlas produces references the documented request shape rather than an imagined one, and it can add type hints so the contract is explicit in the Python source. Before committing to a pattern, verify against the repo's own conventions with grep, because an integration that ignores how the codebase already builds clients is a maintenance problem even when it works. If the project uses Django settings or FastAPI dependencies to inject configuration, the new client should follow that, not invent a new pattern.
How do you verify a new Python integration with pytest and ruff format?
Atlas verifies a new Python integration in 2026 by running pytest against the tests that exercise the client, with uv keeping the virtualenv aligned to pyproject.toml. Run ruff format so the diff you review is logic rather than formatting, and use grep to confirm the pattern matches the rest of the codebase.
An integration written from live documentation still has to prove itself locally. pytest is the arbiter: write a test that constructs the request the docs describe and asserts on the error branch the docs document, then run it. uv keeps the dependency tree reproducible so the run is not an artifact of your machine. ruff format keeps the diff readable. A client written against the wrong version of a doc page shows up immediately as a failing pytest assertion rather than as a production incident.
Which Python files change when you add an API integration?
Adding a third-party API integration to a Python project touches 4 files: the new client module, pyproject.toml or requirements.txt for the HTTP dependency uv installs, a pytest module that exercises the client, and the Django settings or FastAPI dependency that injects the credentials.
The client module is where the fetched documentation becomes Python code: documented endpoint names become functions, documented parameters become type hints, and documented error codes become exceptions the module handles. pyproject.toml or requirements.txt gains the HTTP dependency that uv installs. A pytest module proves the request shape matches the docs rather than the model's memory. Django settings or a FastAPI dependency injects the credentials so the client never reads them itself. grep confirms the pattern matches how the codebase already builds clients, and ruff format keeps the diff readable.
Which commands follow a webfetch in a Python integration?
After webfetch pulls the documentation, a Python integration takes 3 commands: uv to install the HTTP dependency into the virtualenv, pytest to prove the request shape matches the documented endpoint, and ruff format so the diff you review is logic rather than reflowed lines.
uv installs the HTTP client the project already pins in pyproject.toml or requirements.txt, which keeps the integration consistent with the rest of the Python codebase rather than adding a second client library. pytest is where the fetched documentation becomes a check: a test that builds the documented request and asserts on the documented error code will fail loudly if the vendor changed the contract. ruff format keeps the diff readable. Type hints on the client functions make the documented parameters explicit in the Python source, so the next reader does not need to refetch the page.
Step by step
- 01Call websearch to find the current documentation page; the tool injects the current year into its description so the model biases toward fresh sources.
- 02Fetch the page with webfetch, passing format markdown or text so the Accept header steers the server toward a compact representation.
- 03Approve the webfetch permission prompt; the tool asks with the URL as the pattern before any request goes out.
- 04Read the fetched content, then write the Python client with write or edit against the real endpoint names, parameters, and error codes.
- 05Verify against the repo's own conventions with grep before committing to a pattern that does not match how the codebase already builds clients.
- 06Resolve the HTTP dependency with uv against pyproject.toml or requirements.txt, then run pytest on the tests that exercise the new client.
- 07Run ruff format so the diff you review contains logic changes rather than reflowed Python lines.
- 08Review the unified diff Atlas surfaces for every file edit before it writes the integration module.
Frequently asked questions
- how do I stop an AI agent from hallucinating a Python API client
- Make it read the docs first. Atlas calls websearch to find the current documentation page and webfetch to pull it into context, so the client is written against the real endpoint names and parameters instead of the model's memory.
- can Atlas read live API documentation from the web
- Yes. webfetch pulls a documentation page with format negotiation for markdown, text, or html, and websearch finds the page when you do not have the URL. Both sit behind explicit permissions before any request goes out.
- does Atlas ask before fetching a URL
- Yes. The webfetch permission prompt asks with the URL as the pattern before any request goes out, and every Atlas tool call is permission-gated against allow, ask, and deny rules first.
- how do I make an AI agent match my existing Python client conventions
- Verify against the repo's own conventions with grep before committing to a pattern. If your Django settings or FastAPI dependencies already inject configuration, the new client should follow that rather than invent a new approach.
- how do I test a new third-party API integration in Python
- Run pytest against tests that construct the request the documentation describes and assert on the documented error branch. uv keeps the virtualenv aligned to pyproject.toml so the run is reproducible.
- why does an AI agent get third-party API signatures wrong
- Because it is recalling instead of reading. Atlas's websearch tool injects the current year into its description so it biases toward fresh sources, and webfetch puts the actual page into context before any Python is written.
- can I review the code an AI agent writes for an API integration
- Yes. Atlas computes a unified diff for every file edit and surfaces it for approval before writing, and it snapshots file changes as git patches so the integration can be rolled back.
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