mirror of
https://github.com/meta-llama/llama-stack.git
synced 2025-10-04 12:07:34 +00:00
6cce553c93
Some checks failed
Test External Providers Installed via Module / test-external-providers-from-module (venv) (push) Has been skipped
Test External API and Providers / test-external (venv) (push) Failing after 6s
SqlStore Integration Tests / test-postgres (3.12) (push) Failing after 11s
Integration Auth Tests / test-matrix (oauth2_token) (push) Failing after 17s
Unit Tests / unit-tests (3.13) (push) Failing after 14s
Vector IO Integration Tests / test-matrix (push) Failing after 19s
SqlStore Integration Tests / test-postgres (3.13) (push) Failing after 21s
Python Package Build Test / build (3.12) (push) Failing after 20s
Python Package Build Test / build (3.13) (push) Failing after 23s
Integration Tests (Replay) / Integration Tests (, , , client=, ) (push) Failing after 28s
Unit Tests / unit-tests (3.12) (push) Failing after 25s
API Conformance Tests / check-schema-compatibility (push) Successful in 32s
UI Tests / ui-tests (22) (push) Successful in 57s
Pre-commit / pre-commit (push) Successful in 1m18s
# What does this PR do?
Fixes error:
```
[ERROR] Error executing endpoint route='/v1/openai/v1/responses'
method='post': Error code: 400 - {'error': {'message': "Invalid schema for function 'pods_exec': In context=('properties', 'command'), array
schema missing items.", 'type': 'invalid_request_error', 'param': 'tools[7].function.parameters', 'code': 'invalid_function_parameters'}}
```
From script:
```
#!/usr/bin/env python3
"""
Script to test Responses API with kubernetes-mcp-server.
This script:
1. Connects to the llama stack server
2. Uses the Responses API with MCP tools
3. Asks for the list of Kubernetes namespaces using the kubernetes-mcp-server
"""
import json
from openai import OpenAI
# Connect to the llama stack server
base_url = "http://localhost:8321/v1/openai/v1"
client = OpenAI(base_url=base_url, api_key="fake")
# Define the MCP tool pointing to the kubernetes-mcp-server
# The kubernetes-mcp-server is running on port 3000 with SSE endpoint at /sse
mcp_server_url = "http://localhost:3000/sse"
tools = [
{
"type": "mcp",
"server_label": "k8s",
"server_url": mcp_server_url,
}
]
# Create a response request asking for k8s namespaces
print("Sending request to list Kubernetes namespaces...")
print(f"Using MCP server at: {mcp_server_url}")
print("Available tools will be listed automatically by the MCP server.")
print()
response = client.responses.create(
# model="meta-llama/Llama-3.2-3B-Instruct", # Using the vllm model
model="openai/gpt-4o",
input="what are all the Kubernetes namespaces? Use tool call to `namespaces_list`. make sure to adhere to the tool calling format.",
tools=tools,
stream=False,
)
print("\n" + "=" * 80)
print("RESPONSE OUTPUT:")
print("=" * 80)
# Print the output
for i, output in enumerate(response.output):
print(f"\n[Output {i + 1}] Type: {output.type}")
if output.type == "mcp_list_tools":
print(f" Server: {output.server_label}")
print(f" Tools available: {[t.name for t in output.tools]}")
elif output.type == "mcp_call":
print(f" Tool called: {output.name}")
print(f" Arguments: {output.arguments}")
print(f" Result: {output.output}")
if output.error:
print(f" Error: {output.error}")
elif output.type == "message":
print(f" Role: {output.role}")
print(f" Content: {output.content}")
print("\n" + "=" * 80)
print("FINAL RESPONSE TEXT:")
print("=" * 80)
print(response.output_text)
```
## Test Plan
new unit tests
script now runs successfully
|
||
|---|---|---|
| .. | ||
| apis | ||
| cli | ||
| core | ||
| distributions | ||
| models | ||
| providers | ||
| strong_typing | ||
| testing | ||
| ui | ||
| __init__.py | ||
| env.py | ||
| log.py | ||
| schema_utils.py | ||