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Address review on Foundry Toolbox MCP samples
Reviewed feedback addressed: - Drop the branch-pinned `git+https://...@feature/...` entries from `04_foundry_toolbox/requirements.txt`; restore the simple comment + `mcp` runtime dep. The git pins were only useful while iterating on the PR and shouldn't ship. (eavanvalkenburg) - Fix the `/toolsets/` typo in both `04_foundry_toolbox/README.md` and `06_files/README.md`. Verified empirically against the research_toolbox in the test workspace: the toolbox MCP gateway lives at `/toolboxes/{name}/mcp?api-version=v1` and requires the `Foundry-Features: Toolboxes=V1Preview` header. `/toolsets/{name}/mcp` returns 403 with `preview_feature_required: Toolsets=V1Preview` (a different opt-in feature). - Wrap `httpx.AsyncClient(...)` in `async with ... as http_client:` in both samples so the connection pool is cleaned up. (Copilot reviewer) - Make the `TOOLBOX_NAME` env var consistent in both samples. Previously the tool name silently fell back to `"toolbox"` when `TOOLBOX_NAME` was unset, but `resolve_toolbox_endpoint()` still required `TOOLBOX_NAME` and would raise `KeyError`. The samples now resolve the endpoint once and derive the tool name from the resolved URL when `TOOLBOX_NAME` isn't set, so the local tool name always matches the upstream toolbox identity regardless of which env var the user set. (Copilot reviewer) - Rename `_responses.is_consent_error` to `consent_url_from_error`: the helper returns `str | None` (the consent URL), not a bool, so the new name matches behavior. Update the test class accordingly. (eavanvalkenburg) - Tighten `_handle_inner_agent`'s lazy-entry catch from `Exception` to `AgentFrameworkException`, the type the MCP layer actually wraps consent errors in via `MCPStreamableHTTPTool.__aenter__` → `ToolExecutionException(inner_exception=mcp_error)`. Network failures, cancellations, and other non-framework exceptions now propagate normally instead of being briefly caught and re-raised. The test helper `_make_consent_error` is updated to use `ToolExecutionException` so it matches the real-world wrapping. (eavanvalkenburg) - Clarify the `github_pat` description in `agent.manifest.yaml` to note it's only needed when the PAT-based connection (`github-mcp-pat-conn`) is chosen; users selecting the OAuth2 connection (`github-mcp-oauth-conn`) can leave it empty. (Copilot reviewer) Validation: ran both samples end-to-end against a real Foundry toolbox (`research_toolbox`) -- the samples connect successfully and the agent lists the toolbox's MCP tools (`api_specs___fetch_azure_rest_api_docs`, etc.). `uv run poe test -P foundry_hosting` passes (119 tests), pyright + mypy clean. Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
This commit is contained in:
Eduard van Valkenburg
committed by
eavanvalkenburg
Unverified
eavanvalkenburg
parent
483bfe386b
commit
7f51de9937
+3
-3
@@ -45,20 +45,20 @@ An extra environment variable must be set to point to the toolbox MCP endpoint.
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**Option A – Set `FOUNDRY_TOOLBOX_ENDPOINT` directly** (recommended for local development):
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```bash
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export FOUNDRY_TOOLBOX_ENDPOINT="https://<account>.services.ai.azure.com/api/projects/<project>/toolsets/<name>/mcp?api-version=v1"
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export FOUNDRY_TOOLBOX_ENDPOINT="https://<account>.services.ai.azure.com/api/projects/<project>/toolboxes/<name>/mcp?api-version=v1"
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```
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Or in PowerShell:
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```powershell
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$env:FOUNDRY_TOOLBOX_ENDPOINT="https://<account>.services.ai.azure.com/api/projects/<project>/toolsets/<name>/mcp?api-version=v1"
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$env:FOUNDRY_TOOLBOX_ENDPOINT="https://<account>.services.ai.azure.com/api/projects/<project>/toolboxes/<name>/mcp?api-version=v1"
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```
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**Option B – Set `TOOLBOX_NAME`** (used automatically by the Foundry hosting scaffolding after `azd provision`):
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The agent derives the endpoint at runtime as:
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```
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{FOUNDRY_PROJECT_ENDPOINT}/toolsets/{TOOLBOX_NAME}/mcp?api-version=v1
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{FOUNDRY_PROJECT_ENDPOINT}/toolboxes/{TOOLBOX_NAME}/mcp?api-version=v1
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```
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When deployed via `azd provision`, the scaffolding injects `TOOLBOX_NAME=agent-tools` and `FOUNDRY_PROJECT_ENDPOINT` automatically from the provisioned resources declared in [`agent.manifest.yaml`](agent.manifest.yaml).
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+5
-2
@@ -26,9 +26,12 @@ template:
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# secret: false
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# description: URL of the public MCP server (e.g. https://gitmcp.io/Azure/azure-rest-api-specs) that does not require authentication
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# - name: github_pat
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# # `azd ai agent init -m` will prompt for this value when initializing the agent manifest
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# # `azd ai agent init -m` will prompt for this value when initializing the agent manifest.
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# # Only needed when the GitHub MCP connection is configured to use the `github-mcp-pat-conn`
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# # PAT-based connection below; if you use the `github-mcp-oauth-conn` OAuth2 connection
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# # instead, you can leave this empty.
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# secret: true
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# description: GitHub Personal Access Token used to authenticate with the GitHub MCP server (press Enter if OAuth2 is used instead)
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# description: GitHub Personal Access Token used to authenticate with the GitHub MCP server (only needed when using the PAT connection; press Enter if using OAuth2 instead)
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# - name: language_mcp_entra_audience
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# secret: false
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# description: Entra ID audience for the Azure Language MCP server (e.g. https://cognitiveservices.azure.com/)
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+31
-26
@@ -48,38 +48,43 @@ async def main():
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# Create the toolbox
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token_provider = get_bearer_token_provider(credential, "https://ai.azure.com/.default")
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http_client = httpx.AsyncClient(
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# Resolve the endpoint once and derive the tool name from the same source: when
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# ``TOOLBOX_NAME`` isn't explicitly set, parse it out of the resolved URL so the
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# tool's local name and the upstream toolbox always agree.
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toolbox_endpoint = resolve_toolbox_endpoint()
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toolbox_name = os.environ.get("TOOLBOX_NAME") or toolbox_endpoint.rsplit("/mcp", 1)[0].rsplit("/", 1)[-1]
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async with httpx.AsyncClient(
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auth=ToolboxAuth(token_provider),
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headers={"Foundry-Features": "Toolboxes=V1Preview"},
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timeout=120.0,
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)
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) as http_client:
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toolbox = MCPStreamableHTTPTool(
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name=toolbox_name,
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url=toolbox_endpoint,
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http_client=http_client,
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load_prompts=False,
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)
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toolbox = MCPStreamableHTTPTool(
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name=os.environ.get("TOOLBOX_NAME", "toolbox"),
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url=resolve_toolbox_endpoint(),
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http_client=http_client,
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load_prompts=False,
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)
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# Create the chat client
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client = FoundryChatClient(
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project_endpoint=os.environ["FOUNDRY_PROJECT_ENDPOINT"],
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model=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
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credential=credential,
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)
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# Create the chat client
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client = FoundryChatClient(
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project_endpoint=os.environ["FOUNDRY_PROJECT_ENDPOINT"],
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model=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
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credential=credential,
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)
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agent = Agent(
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client=client,
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instructions="You are a friendly assistant. Keep your answers brief.",
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tools=toolbox,
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# History will be managed by the hosting infrastructure, thus there
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# is no need to store history by the service. Learn more at:
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# https://developers.openai.com/api/reference/resources/responses/methods/create
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default_options={"store": False},
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)
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agent = Agent(
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client=client,
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instructions="You are a friendly assistant. Keep your answers brief.",
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tools=toolbox,
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# History will be managed by the hosting infrastructure, thus there
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# is no need to store history by the service. Learn more at:
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# https://developers.openai.com/api/reference/resources/responses/methods/create
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default_options={"store": False},
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)
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server = ResponsesHostServer(agent)
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await server.run_async()
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server = ResponsesHostServer(agent)
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await server.run_async()
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if __name__ == "__main__":
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+1
-6
@@ -1,9 +1,4 @@
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# agent-framework
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# agent-framework-foundry-hosting
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git+https://github.com/microsoft/agent-framework.git@feature/add-more-foundry-toolbox-mcp-auth-methods-in-sample#egg=agent-framework-core&subdirectory=python/packages/core
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git+https://github.com/microsoft/agent-framework.git@feature/add-more-foundry-toolbox-mcp-auth-methods-in-sample#egg=agent-framework-openai&subdirectory=python/packages/openai
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git+https://github.com/microsoft/agent-framework.git@feature/add-more-foundry-toolbox-mcp-auth-methods-in-sample#egg=agent-framework-foundry&subdirectory=python/packages/foundry
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git+https://github.com/microsoft/agent-framework.git@feature/add-more-foundry-toolbox-mcp-auth-methods-in-sample#egg=agent-framework-foundry-hosting&subdirectory=python/packages/foundry_hosting
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mcp>=1.24.0,<2
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mcp>=1.24.0,<2
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@@ -35,20 +35,20 @@ An extra environment variable must be set to point to the toolbox MCP endpoint.
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**Option A – Set `FOUNDRY_TOOLBOX_ENDPOINT` directly** (recommended for local development):
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```bash
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export FOUNDRY_TOOLBOX_ENDPOINT="https://<account>.services.ai.azure.com/api/projects/<project>/toolsets/<name>/mcp?api-version=v1"
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export FOUNDRY_TOOLBOX_ENDPOINT="https://<account>.services.ai.azure.com/api/projects/<project>/toolboxes/<name>/mcp?api-version=v1"
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```
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Or in PowerShell:
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```powershell
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$env:FOUNDRY_TOOLBOX_ENDPOINT="https://<account>.services.ai.azure.com/api/projects/<project>/toolsets/<name>/mcp?api-version=v1"
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$env:FOUNDRY_TOOLBOX_ENDPOINT="https://<account>.services.ai.azure.com/api/projects/<project>/toolboxes/<name>/mcp?api-version=v1"
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```
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**Option B – Set `TOOLBOX_NAME`** (used automatically by the Foundry hosting scaffolding after `azd provision`):
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The agent derives the endpoint at runtime as:
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```
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{FOUNDRY_PROJECT_ENDPOINT}/toolsets/{TOOLBOX_NAME}/mcp?api-version=v1
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{FOUNDRY_PROJECT_ENDPOINT}/toolboxes/{TOOLBOX_NAME}/mcp?api-version=v1
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```
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When deployed via `azd provision`, the scaffolding injects `TOOLBOX_NAME=agent-tools` and `FOUNDRY_PROJECT_ENDPOINT` automatically from the provisioned resources declared in [`agent.manifest.yaml`](agent.manifest.yaml).
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@@ -76,41 +76,46 @@ async def main():
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# Create the toolbox
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token_provider = get_bearer_token_provider(credential, "https://ai.azure.com/.default")
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http_client = httpx.AsyncClient(
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# Resolve the endpoint once and derive the tool name from the same source: when
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# ``TOOLBOX_NAME`` isn't explicitly set, parse it out of the resolved URL so the
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# tool's local name and the upstream toolbox always agree.
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toolbox_endpoint = resolve_toolbox_endpoint()
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toolbox_name = os.environ.get("TOOLBOX_NAME") or toolbox_endpoint.rsplit("/mcp", 1)[0].rsplit("/", 1)[-1]
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async with httpx.AsyncClient(
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auth=ToolboxAuth(token_provider),
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headers={"Foundry-Features": "Toolboxes=V1Preview"},
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timeout=120.0,
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)
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) as http_client:
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toolbox = MCPStreamableHTTPTool(
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name=toolbox_name,
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url=toolbox_endpoint,
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http_client=http_client,
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load_prompts=False,
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)
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toolbox = MCPStreamableHTTPTool(
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name=os.environ.get("TOOLBOX_NAME", "toolbox"),
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url=resolve_toolbox_endpoint(),
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http_client=http_client,
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load_prompts=False,
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)
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# Create the chat client
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client = FoundryChatClient(
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project_endpoint=os.environ["FOUNDRY_PROJECT_ENDPOINT"],
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model=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
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credential=credential,
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)
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# Create the chat client
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client = FoundryChatClient(
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project_endpoint=os.environ["FOUNDRY_PROJECT_ENDPOINT"],
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model=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
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credential=credential,
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)
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agent = Agent(
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client=client,
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instructions=(
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"You are a friendly assistant. Keep your answers brief. "
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"Make sure all mathematical calculations are performed using the code interpreter "
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"instead of mental arithmetic."
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),
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tools=[get_cwd, list_files, read_file, toolbox],
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# History will be managed by the hosting infrastructure, thus there
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# is no need to store history by the service. Learn more at:
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# https://developers.openai.com/api/reference/resources/responses/methods/create
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default_options={"store": False},
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)
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server = ResponsesHostServer(agent)
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await server.run_async()
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agent = Agent(
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client=client,
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instructions=(
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"You are a friendly assistant. Keep your answers brief. "
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"Make sure all mathematical calculations are performed using the code interpreter "
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"instead of mental arithmetic."
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),
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tools=[get_cwd, list_files, read_file, toolbox],
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# History will be managed by the hosting infrastructure, thus there
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# is no need to store history by the service. Learn more at:
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# https://developers.openai.com/api/reference/resources/responses/methods/create
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default_options={"store": False},
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)
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server = ResponsesHostServer(agent)
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await server.run_async()
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if __name__ == "__main__":
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