Tutorial 2: Issue Triage Bot with Custom Tools

Script: src/python/scripts/tutorials/02_issue_triage.py

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What You Will Learn

• How to define custom tools with the @define_tool decorator
• How to use Pydantic models for tool input and output schemas
• How to build a tool-calling agent that classifies and labels GitHub issues

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Prerequisites

• The copilot CLI installed and authenticated (see Getting Started)
• github-copilot-sdk and pydantic installed

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What Are Custom Tools?

Custom tools let you give the Copilot agent access to your own functions. The agent decides when to call them based on their descriptions. You define:

1. The tool's name and description (used by the LLM to decide when to call it)
2. The input schema (a Pydantic BaseModel)
3. The output schema (another Pydantic BaseModel)
4. The implementation (a regular Python function)

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Step 1 — Define Input/Output schemas

from pydantic import BaseModel

class ListIssuesInput(BaseModel):
    pass  # No parameters needed

class IssueItem(BaseModel):
    id: int
    title: str
    body: str
    labels: list[str]

class ListIssuesOutput(BaseModel):
    issues: list[IssueItem]

class LabelIssueInput(BaseModel):
    issue_id: int
    labels: list[str]

class LabelIssueOutput(BaseModel):
    success: bool
    issue_id: int
    applied_labels: list[str]

Clear, typed schemas help the LLM understand what data to pass and what to expect back.

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Step 2 — Implement the tools with @define_tool

from copilot.tools import define_tool

@define_tool(
    name="list_issues",
    description="Return the list of open GitHub issues to triage.",
)
def list_issues(_input: ListIssuesInput) -> ListIssuesOutput:
    return ListIssuesOutput(
        issues=[IssueItem(**issue) for issue in SAMPLE_ISSUES]
    )

@define_tool(
    name="label_issue",
    description="Apply one or more labels to a GitHub issue.",
)
def label_issue(input: LabelIssueInput) -> LabelIssueOutput:
    # In a real scenario, call the GitHub API here
    return LabelIssueOutput(
        success=True,
        issue_id=input.issue_id,
        applied_labels=input.labels,
    )

Tip: Write descriptive description strings. The LLM uses them to decide when to invoke each tool.

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Step 3 — Register tools in the session

session = await client.create_session(
    SessionConfig(
        on_permission_request=approve_all,
        tools=[list_issues, label_issue],  # ← register here
        streaming=False,
        system_message=SystemMessageReplaceConfig(
            mode="replace",
            content=(
                "You are an expert GitHub issue triage assistant. "
                "Use list_issues to fetch open issues, classify each one "
                "as 'bug', 'enhancement', or 'documentation', then call "
                "label_issue to apply the appropriate label."
            ),
        ),
    )
)

Note SystemMessageReplaceConfig — this replaces the default system message entirely, giving the agent a focused persona.

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Step 4 — Send the task prompt

reply = await session.send_and_wait(
    MessageOptions(prompt="Please triage all open issues and apply the appropriate labels."),
    timeout=300,
)
print(reply.data.content)

The agent will:

1. Call list_issues() to fetch the issues
2. Analyse each issue
3. Call label_issue() for each one with the appropriate label
4. Return a summary

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Run the Script

cd src/python
uv run python scripts/tutorials/02_issue_triage.py
uv run python scripts/tutorials/02_issue_triage.py --cli-url localhost:3000  # optional: use a running CLI server

Expected output:

[Tool] Calling: list_issues
[Tool] Calling: label_issue
[Tool] Calling: label_issue
[Tool] Calling: label_issue
=== Triage Summary ===
I've triaged all 3 open issues...

=== Applied Labels ===
[
  {"id": 1, "labels": ["bug"]},
  {"id": 2, "labels": ["enhancement"]},
  {"id": 3, "labels": ["documentation"]}
]

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Key Takeaways

• @define_tool(name, description) registers a function as a callable tool
• Pydantic BaseModel defines strongly-typed input/output contracts
• Tools are registered per-session in SessionConfig(tools=[...])
• The LLM decides when to call tools based on the task and the description strings
• SystemMessageReplaceConfig gives the agent a dedicated persona for the task

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Next Tutorial

Tutorial 3: Streaming Code Review →