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6 Commits

Author SHA1 Message Date
Twisha Bansal
04615acdac fix new docs 2026-01-28 15:41:42 +05:30
Twisha Bansal
6921c1b377 Merge branch 'main' into twishabansal-patch-2 2026-01-28 13:43:59 +05:30
Twisha Bansal
f8f47ce4f8 Merge branch 'main' into twishabansal-patch-2 2026-01-27 22:26:01 +05:30
Twisha Bansal
09f21ad5b8 add commands to run manually 2026-01-27 21:52:17 +05:30
Twisha Bansal
d8ae1f257c docs: Standardize installation note and use @latest tag 2026-01-27 21:44:13 +05:30
Twisha Bansal
58b5c9951c Update TOOLBOX_README.md 2026-01-27 21:16:19 +05:30
20 changed files with 71 additions and 265 deletions

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@@ -23,6 +23,12 @@ To connect to the database to explore and query data, search the MCP store for t
In the Antigravity MCP Store, click the "Install" button.
> [!NOTE]
> Installation automatically uses the
> [toolbox server package](https://www.npmjs.com/package/@toolbox-sdk/server)
> (version `>=0.26.0`). You can run the latest server manually with command
> ```npx -y @toolbox-sdk/server@latest --prebuilt alloydb-postgres-admin```.
You'll now be able to see all enabled tools in the "Tools" tab.
> [!NOTE]

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@@ -27,6 +27,11 @@ For AlloyDB infrastructure management, search the MCP store for the AlloyDB for
## Install & Configuration
1. In the Antigravity MCP Store, click the "Install" button.
> [!NOTE]
> Installation automatically uses the
> [toolbox server package](https://www.npmjs.com/package/@toolbox-sdk/server)
> (version `>=0.26.0`). You can run the latest server manually with command
> ```npx -y @toolbox-sdk/server@latest --prebuilt alloydb-postgres```.
2. Add the required inputs for your [cluster](https://docs.cloud.google.com/alloydb/docs/cluster-list) in the configuration pop-up, then click "Save". You can update this configuration at any time in the "Configure" tab.

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@@ -21,6 +21,11 @@ An editor configured to use the BigQuery MCP server can use its AI capabilities
## Install & Configuration
1. In the Antigravity MCP Store, click the "Install" button.
> [!NOTE]
> Installation automatically uses the
> [toolbox server package](https://www.npmjs.com/package/@toolbox-sdk/server)
> (version `>=0.26.0`). You can run the latest server manually with command
> ```npx -y @toolbox-sdk/server@latest --prebuilt bigquery```.
2. Add the required inputs in the configuration pop-up, then click "Save". You can update this configuration at any time in the "Configure" tab.

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@@ -23,6 +23,12 @@ To connect to the database to explore and query data, search the MCP store for t
In the Antigravity MCP Store, click the "Install" button.
> [!NOTE]
> Installation automatically uses the
> [toolbox server package](https://www.npmjs.com/package/@toolbox-sdk/server)
> (version `>=0.26.0`). You can run the latest server manually with command
> ```npx -y @toolbox-sdk/server@latest --prebuilt cloud-sql-mssql-admin```.
You'll now be able to see all enabled tools in the "Tools" tab.
> [!NOTE]

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@@ -24,6 +24,11 @@ For Cloud SQL infrastructure management, search the MCP store for the Cloud SQL
## Install & Configuration
1. In the Antigravity MCP Store, click the "Install" button.
> [!NOTE]
> Installation automatically uses the
> [toolbox server package](https://www.npmjs.com/package/@toolbox-sdk/server)
> (version `>=0.26.0`). You can run the latest server manually with command
> ```npx -y @toolbox-sdk/server@latest --prebuilt cloud-sql-mssql```.
2. Add the required inputs for your [instance](https://cloud.google.com/sql/docs/sqlserver/instance-info) in the configuration pop-up, then click "Save". You can update this configuration at any time in the "Configure" tab.

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@@ -23,6 +23,12 @@ To connect to the database to explore and query data, search the MCP store for t
In the Antigravity MCP Store, click the "Install" button.
> [!NOTE]
> Installation automatically uses the
> [toolbox server package](https://www.npmjs.com/package/@toolbox-sdk/server)
> (version `>=0.26.0`). You can run the latest server manually with command
> ```npx -y @toolbox-sdk/server@latest --prebuilt cloud-sql-mysql-admin```.
You'll now be able to see all enabled tools in the "Tools" tab.
> [!NOTE]

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@@ -26,6 +26,11 @@ For Cloud SQL infrastructure management, search the MCP store for the Cloud SQL
## Install & Configuration
1. In the Antigravity MCP Store, click the "Install" button.
> [!NOTE]
> Installation automatically uses the
> [toolbox server package](https://www.npmjs.com/package/@toolbox-sdk/server)
> (version `>=0.26.0`). You can run the latest server manually with command
> ```npx -y @toolbox-sdk/server@latest --prebuilt cloud-sql-mysql```.
2. Add the required inputs for your [instance](https://cloud.google.com/sql/docs/mysql/instance-info) in the configuration pop-up, then click "Save". You can update this configuration at any time in the "Configure" tab.

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@@ -23,6 +23,12 @@ To connect to the database to explore and query data, search the MCP store for t
In the Antigravity MCP Store, click the "Install" button.
> [!NOTE]
> Installation automatically uses the
> [toolbox server package](https://www.npmjs.com/package/@toolbox-sdk/server)
> (version `>=0.26.0`). You can run the latest server manually with command
> ```npx -y @toolbox-sdk/server@latest --prebuilt cloud-sql-postgres-admin```.
You'll now be able to see all enabled tools in the "Tools" tab.
> [!NOTE]

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@@ -26,6 +26,11 @@ For Cloud SQL infrastructure management, search the MCP store for the Cloud SQL
## Install & Configuration
1. In the Antigravity MCP Store, click the "Install" button.
> [!NOTE]
> Installation automatically uses the
> [toolbox server package](https://www.npmjs.com/package/@toolbox-sdk/server)
> (version `>=0.26.0`). You can run the latest server manually with command
> ```npx -y @toolbox-sdk/server@latest --prebuilt cloud-sql-postgres```.
2. Add the required inputs for your [instance](https://cloud.google.com/sql/docs/postgres/instance-info) in the configuration pop-up, then click "Save". You can update this configuration at any time in the "Configure" tab.

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@@ -20,6 +20,11 @@ An editor configured to use the Dataplex MCP server can use its AI capabilities
## Install & Configuration
1. In the Antigravity MCP Store, click the "Install" button.
> [!NOTE]
> Installation automatically uses the
> [toolbox server package](https://www.npmjs.com/package/@toolbox-sdk/server)
> (version `>=0.26.0`). You can run the latest server manually with command
> ```npx -y @toolbox-sdk/server@latest --prebuilt dataplex```.
2. Add the required inputs in the configuration pop-up, then click "Save". You can update this configuration at any time in the "Configure" tab.

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@@ -21,6 +21,11 @@ An editor configured to use the Looker MCP server can use its AI capabilities to
## Install & Configuration
1. In the Antigravity MCP Store, click the "Install" button.
> [!NOTE]
> Installation automatically uses the
> [toolbox server package](https://www.npmjs.com/package/@toolbox-sdk/server)
> (version `>=0.26.0`). You can run the latest server manually with command
> ```npx -y @toolbox-sdk/server@latest --prebuilt looker```.
2. Add the required inputs for your [instance](https://docs.cloud.google.com/looker/docs/set-up-and-administer-looker) in the configuration pop-up, then click "Save". You can update this configuration at any time in the "Configure" tab.

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@@ -21,6 +21,11 @@ An editor configured to use the Cloud Spanner MCP server can use its AI capabili
## Install & Configuration
1. In the Antigravity MCP Store, click the "Install" button.
> [!NOTE]
> Installation automatically uses the
> [toolbox server package](https://www.npmjs.com/package/@toolbox-sdk/server)
> (version `>=0.26.0`). You can run the latest server manually with command
> ```npx -y @toolbox-sdk/server@latest --prebuilt spanner```.
2. Add the required inputs for your [instance](https://docs.cloud.google.com/spanner/docs/instances) in the configuration pop-up, then click "Save". You can update this configuration at any time in the "Configure" tab.

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@@ -12,10 +12,15 @@ The MCP Toolbox for Databases Server gives AI-powered development tools the abil
## Install & Configuration
1. In the Antigravity MCP Store, click the **Install** button. A configuration window will appear.
> [!NOTE]
> Installation automatically uses the
> [toolbox server package](https://www.npmjs.com/package/@toolbox-sdk/server)
> (version `>=0.26.0`). You can run the latest server manually with command
> ```npx -y @toolbox-sdk/server@latest```.
2. Create your [`tools.yaml` configuration file](https://googleapis.github.io/genai-toolbox/getting-started/configure/).
3. Create your [`tools.yaml` configuration file](https://googleapis.github.io/genai-toolbox/getting-started/configure/).
3. In the configuration window, enter the full absolute path to your `tools.yaml` file and click **Save**.
4. In the configuration window, enter the full absolute path to your `tools.yaml` file and click **Save**.
> [!NOTE]
> If you encounter issues with Windows Defender blocking the execution, you may need to configure an allowlist. See [Configure exclusions for Microsoft Defender Antivirus](https://learn.microsoft.com/en-us/microsoft-365/security/defender-endpoint/configure-exclusions-microsoft-defender-antivirus?view=o365-worldwide) for more details.

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@@ -1,52 +0,0 @@
---
title: "Pre and Post processing"
type: docs
weight: 1
description: >
Pre and Post processing in GenAI applications.
---
Pre and post processing allow developers to intercept and modify interactions between the agent and its tools or the user. This capability is essential for building robust, secure, and compliant agents.
## Types of Processing
### Pre-processing
Pre-processing occurs before a tool is executed or an agent processes a message. Key types include:
- **Input Sanitization & Redaction**: Detecting and masking sensitive information (like PII) in user queries or tool arguments to prevent it from being logged or sent to unauthorized systems.
- **Business Logic Validation**: Verifying that the proposed action complies with business rules (e.g., ensuring a requested hotel stay does not exceed 14 days, or checking if a user has sufficient permission).
- **Security Guardrails**: Analyzing inputs for potential prompt injection attacks or malicious payloads.
### Post-processing
Post-processing occurs after a tool has executed or the model has generated a response. Key types include:
- **Response Enrichment**: Injecting additional data into the tool output that wasn't part of the raw API response (e.g., calculating loyalty points earned based on the booking value).
- **Output Formatting**: Transforming raw data (like JSON or XML) into a more human-readable or model-friendly format to improve the agent's understanding.
- **Compliance Auditing**: Logging the final outcome of transactions, including the original request and the result, to a secure audit trail.
## Processing Scopes
Processing logic can be applied at different levels of the application:
### Tool Level
Wraps individual tool executions. This is best for logic specific to a single tool or a set of tools.
- **Scope**: Intercepts the raw inputs (arguments) to a tool and its outputs.
- **Use Cases**: Argument validation, output formatting, specific privacy rules for sensitive tools.
### Model Level
Intercepts individual calls to the Large Language Model (LLM).
- **Scope**: Intercepts the list of messages (prompt) sent to the model and the generation (response) received.
- **Use Cases**: Global PII redaction (across all tools/chat), prompt engineering/injection, token usage tracking, and hallucination detection.
### Agent Level
Wraps the high-level agent execution loop (e.g., a "turn" in the conversation).
- **Scope**: Intercepts the initial user input and the final agent response, enveloping one or more model calls and tool executions.
- **Use Cases**: User authentication, rate limiting, session management, and end-to-end audit logging.

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@@ -1,5 +0,0 @@
Final Client Response:
AI:
Booking Confirmed!
Loyalty Points
POLICY CHECK: Intercepting 'book-hotel'

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@@ -1,31 +0,0 @@
---
title: "(Python) Pre and post processing"
type: docs
weight: 4
description: >
How to add pre and post processing to your Python toolbox applications.
---
## Prerequisites
This tutorial assumes that you have set up a basic toolbox application as described in the [local quickstart](../../getting-started/local_quickstart).
This guide demonstrates how to implement these patterns in your Toolbox applications.
## Python
{{< tabpane persist=header >}}
{{% tab header="ADK" text=true %}}
Coming soon.
{{% /tab %}}
{{% tab header="Langchain" text=true %}}
The following example demonstrates how to use `ToolboxClient` with LangChain's middleware to implement pre and post processing for tool calls.
```py
{{< include "python/langchain/agent.py" >}}
```
For more information, see the [LangChain Middleware documentation](https://docs.langchain.com/oss/python/langchain/middleware/custom#wrap-style-hooks).
You can also add model-level (`wrap_model`) and agent-level (`before_agent`, `after_agent`) hooks to intercept messages at different stages of the execution loop. See the [LangChain Middleware documentation](https://docs.langchain.com/oss/python/langchain/middleware/custom#wrap-style-hooks) for details on these additional hook types.
{{% /tab %}}
{{< /tabpane >}}

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@@ -1,4 +0,0 @@
# This file makes the 'pre_post_processing/python' directory a Python package.
# You can include any package-level initialization logic here if needed.
# For now, this file is empty.

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@@ -1,58 +0,0 @@
# Copyright 2026 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import asyncio
import importlib
import os
from pathlib import Path
import pytest
ORCH_NAME = os.environ.get("ORCH_NAME")
module_path = f"python.{ORCH_NAME}.agent"
agent = importlib.import_module(module_path)
@pytest.fixture(scope="module")
def golden_keywords():
"""Loads expected keywords from the golden.txt file."""
golden_file_path = Path(__file__).resolve().parent.parent / "golden.txt"
if not golden_file_path.exists():
pytest.fail(f"Golden file not found: {golden_file_path}")
try:
with open(golden_file_path, "r") as f:
return [line.strip() for line in f.readlines() if line.strip()]
except Exception as e:
pytest.fail(f"Could not read golden.txt: {e}")
# --- Execution Tests ---
class TestExecution:
"""Test framework execution and output validation."""
@pytest.fixture(scope="function")
def script_output(self, capsys):
"""Run the agent function and return its output."""
asyncio.run(agent.main())
return capsys.readouterr()
def test_script_runs_without_errors(self, script_output):
"""Test that the script runs and produces no stderr."""
assert script_output.err == "", f"Script produced stderr: {script_output.err}"
def test_keywords_in_output(self, script_output, golden_keywords):
"""Test that expected keywords are present in the script's output."""
output = script_output.out
missing_keywords = [kw for kw in golden_keywords if kw not in output]
assert not missing_keywords, f"Missing keywords in output: {missing_keywords}"

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@@ -1,111 +0,0 @@
# Copyright 2026 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import asyncio
from datetime import datetime
from langchain.agents import create_agent
from langchain.agents.middleware import wrap_tool_call
from langchain_core.messages import ToolMessage
from langchain_google_vertexai import ChatVertexAI
from toolbox_langchain import ToolboxClient
system_prompt = """
You're a helpful hotel assistant. You handle hotel searching, booking and
cancellations. When the user searches for a hotel, mention it's name, id,
location and price tier. Always mention hotel ids while performing any
searches. This is very important for any operations. For any bookings or
cancellations, please provide the appropriate confirmation. Be sure to
update checkin or checkout dates if mentioned by the user.
Don't ask for confirmations from the user.
"""
# Pre processing
@wrap_tool_call
async def enforce_business_rules(request, handler):
"""
Business Logic Validation:
Enforces max stay duration (e.g., max 14 days).
"""
tool_call = request.tool_call
name = tool_call["name"]
args = tool_call["args"]
print(f"POLICY CHECK: Intercepting '{name}'")
if name == "update-hotel":
if "checkin_date" in args and "checkout_date" in args:
try:
start = datetime.fromisoformat(args["checkin_date"])
end = datetime.fromisoformat(args["checkout_date"])
duration = (end - start).days
if duration > 14:
print("BLOCKED: Stay too long")
return ToolMessage(
content="Error: Maximum stay duration is 14 days.",
tool_call_id=tool_call["id"],
)
except ValueError:
pass # Ignore invalid date formats
return await handler(request)
# Post processing
@wrap_tool_call
async def enrich_response(request, handler):
"""
Post-Processing & Enrichment:
Adds loyalty points information to successful bookings.
Standardizes output format.
"""
result = await handler(request)
if isinstance(result, ToolMessage):
content = str(result.content)
tool_name = request.tool_call["name"]
if tool_name == "book-hotel" and "Error" not in content:
loyalty_bonus = 500
result.content = f"Booking Confirmed! \n You earned {loyalty_bonus} Loyalty Points with this stay.\n\nSystem Details: {content}"
return result
async def main():
async with ToolboxClient("http://127.0.0.1:5000") as client:
tools = await client.aload_toolset("my-toolset")
model = ChatVertexAI(model="gemini-2.5-flash")
agent = create_agent(
system_prompt=system_prompt,
model=model,
tools=tools,
middleware=[enforce_business_rules, enrich_response],
)
user_input = "Book hotel with id 3."
response = await agent.ainvoke(
{"messages": [{"role": "user", "content": user_input}]}
)
print("-" * 50)
print("Final Client Response:")
last_ai_msg = response["messages"][-1].content
print(f"AI: {last_ai_msg}")
if __name__ == "__main__":
asyncio.run(main())

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@@ -1,2 +0,0 @@
langchain==1.2.6
toolbox-langchain==0.5.7