Tool

Overview

Understanding the tools your users work in helps agents and systems deliver context-aware actions.
By retrieving tool data from Fastino, you can determine:

• Which tools the user interacts with most (e.g., Slack, Notion, Figma).

• What the user’s current or active tool context is.

• Typical patterns or preferred workflows across applications.

• How to coordinate tasks between connected environments.

Endpoints Used

Example: Query User’s Most Used Tools

Ask the Personalization API about the tools or apps a user frequently uses.

POST /query
{
  "user_id": "usr_42af7c",
  "question": "What tools does Ash use most frequently?"
}

Response

{
  "answer": "Ash primarily uses Notion for planning, Slack for communication, and Figma for design reviews. Most active in Slack during work hours."
}

Example: Retrieve Tool Summary

Retrieve a structured, deterministic summary of tool data for consistent downstream use.

Response

{
  "summary": "Ash frequently works across Notion, Slack, Gmail, and Figma. Notion is used for planning, Slack for messaging, and Gmail for external communication. Currently active in Slack."
}

This summary can be embedded into reasoning prompts or orchestration logic for adaptive behavior.

Example: Ask About Active Tool Context

POST /query
{
  "user_id": "usr_42af7c",
  "question": "Which tool is Ash currently working in?"
}

Response

{
  "answer": "Ash is currently active in Notion, editing project notes for the new launch sprint."
}

This helps agents understand which environment to operate within when performing contextual actions.

Example Implementation (Python)

import requests

BASE_URL = "https://api.fastino.ai"
HEADERS = {"Authorization": "x-api-key: sk_live_456", "Content-Type": "application/json"}

def query_user_tools(user_id, question):
    payload = {"user_id": user_id, "question": question}
    r = requests.post(f"{BASE_URL}/query", json=payload, headers=HEADERS)
    return r.json()["answer"]

def get_tool_summary(user_id):
    r = requests.get(f"{BASE_URL}/summary?user_id={user_id}&purpose=tool", headers=HEADERS)
    return r.json()["summary"]

print(query_user_tools("usr_42af7c", "Which tools does Ash use most often?"))
print(get_tool_summary("usr_42af7c"))

Integration Ideas

Best Practices

• Use purpose=tool summaries for consistent, structured tool information.

• Combine with purpose=device and purpose=situation for full situational context.

• Let Fastino infer tool activity — explicit ingestion is optional.

• Re-fetch summaries periodically (or on tool-change events) for accuracy.

• When querying, specify the question in natural language for highest precision.

Example: Deterministic Tool Summary

Response

{
  "summary": "Ash’s primary tools are Slack, Notion, and Gmail. Most recent activity detected in Slack. Prefers asynchronous collaboration through Notion comments and quick Slack updates."
}

Summary

The Tool endpoint helps you retrieve structured, contextual information about a user’s preferred and active applications — allowing agents to reason and act within the right environment automatically.

Fastino infers this data from usage patterns and metadata, so you can deliver tool-aware personalization without any manual tagging or ingestion setup.

Next, continue to Personalization Use Cases → Cognitive Style to learn how Fastino adapts reasoning and communication patterns to each user’s unique way of thinking and problem-solving.