3tears-agent-tools 0.18.0

Tool framework for LLM agents -- routing, execution, and builtin tools

3tears Agent Tools

Tool framework for LLM agents. Provides tool routing, execution, context management, MCP integration, and a set of builtin tools.

Part of the 3tears framework.

ToolServer baseline audit

ToolServer.handle_call stamps every dispatch with a unified AuditEvent envelope (event_type='tool.call') via threetears.agent.audit.publish_audit. The baseline emission fires in a finally block so success, failure (tool returned success=False), and error (tool raised) outcomes all produce a row. Identity axes carry from the active ToolCallScope (actor_user_id, calling_agent_id, owner_agent_id, customer_id, correlation_id); resource_namespace_id / resource_namespace_type stay None at the baseline layer since the tool resolves its target inside execute. Per-tool additive events (e.g. workspace.fs_write) still publish via publish_audit and ride alongside the baseline row under the same correlation_id. The (correlation_id, event_type) partial unique index on platform_audit.audit_events keeps them distinct. Emission is fire-and-forget: NATS publish failures log WARN and never taint the tool's response.

Tool-as-namespace emission

Tool namespace materialization is platform-owned. ToolServer.publish_registration writes the RegistrationManifest (carrying pod_id + tools + the owner_agent_id / customer_id envelope fields), and a platform-side namespace emitter subscribes to {ns}.tools.register and upserts one platform.namespaces row of type tool per tool. This is the sole writer in the platform.

Agent-spun ToolServers stamp agent_id + customer_id on the RegistrationManifest so the emitter lands rows with the right owner scope; platform-built-in pods (admin tool server, datasource tool pod) leave both None and the row lands with NULL owner columns (admitted under the widened namespaces_row_scope_customer_ck carve-out for tool type alongside system / model).

The canonical name shape is tools.<sanitized-mcp>.<sanitized-version> (per build_namespace_name); metadata carries the pre-sanitized natural-identity fields mcp_name / mcp_version / pod_id so downstream pattern matching (platform access materializer agent.yaml access.tools patterns + registry authorizer canonical-name lookup) does not need to reverse the sanitization rules. Deterministic uuid5 derived from (mcp_name, version, owner_agent_id_hex) keeps concurrent emitters race-safe via ON CONFLICT (id) DO UPDATE.

The ToolServer.register_tool / deregister_tool helpers still emit through an injected namespace_collection for callers that wire one explicitly, but in production deployments the platform-side emitter is the source of truth and the in-process emission is redundant.

Installation

pip install 3tears-agent-tools

# Optional extras for builtin tools
pip install "3tears-agent-tools[calculator]"   # simpleeval
pip install "3tears-agent-tools[units]"        # pint
pip install "3tears-agent-tools[fetch]"        # trafilatura
pip install "3tears-agent-tools[document]"     # PyMuPDF, python-docx, openpyxl
pip install "3tears-agent-tools[all]"          # everything

Components

ToolRouter

Routes user messages to the appropriate tool using a lightweight LLM call. Includes recall-intent detection to avoid re-invoking tools when users ask about previous results.

from threetears.agent.tools import ToolRouter, is_recall_intent

# Quick check -- no LLM call needed
if is_recall_intent("show me what the calculator said"):
    # User wants to recall, not invoke

# Full routing with LLM
router = ToolRouter(chat_model)
decision = await router.route(user_message, tool_descriptions)
# decision.tool_name, decision.reasoning

ToolExecutor

Invokes a tool-LLM: sends the user message to a secondary model configured for a specific task.

from threetears.agent.tools import ToolExecutor

executor = ToolExecutor()
result = await executor.invoke_with_tools(
    chat_model=tool_model,
    user_message="What is 42 * 17?",
    tools=[calculator_tool],
    tool_name="calculator",
)
# result.content, result.tool_calls

ToolContextManager

Tracks tool invocations and results across a conversation for recall support.

from threetears.agent.tools import ToolContextManager

ctx = ToolContextManager()
await ctx.record_invocation("calculator", "42 * 17", "714")
await ctx.get_recall_context("calculator")  # Returns formatted recall string

McpClient

MCP (Model Context Protocol) integration for connecting to external tool servers.

from threetears.agent.tools import McpClient

async with McpClient(server_config) as client:
    tools = await client.list_tools()
    result = await client.invoke_tool("tool_name", {"param": "value"})

Builtin Tools

Register all builtin tools at once:

from threetears.agent.tools import register_builtins, ToolRegistry

registry = ToolRegistry()
register_builtins(registry)
# Registers: calculator, unit_converter, dice_roller, date_time,
#            random_number, web_fetch, text_transform, parse_document

Todo Tools

Todo list management behind a storage protocol:

from threetears.agent.tools import TodoStorage, load_todo_tools_from_storage

class MyTodoStorage(TodoStorage):
    async def add(self, conv_id, user_id, title, list_name, msg_id) -> dict: ...
    async def list_all(self, conv_id) -> list[dict]: ...
    # ... other methods

tools = load_todo_tools_from_storage(my_storage, snapshot_callback=on_snapshot)

Protocols

For media-related capabilities, implement these protocols:

from threetears.agent.tools import (
    ImageGenerationBackend,
    MediaStorage,
    VisionProvider,
    TranscriptionProvider,
)

Document Parsing

Parse PDF, DOCX, XLSX, and plain text with optional OCR:

from threetears.agent.tools import parse_document, OcrConfig

result = await parse_document(
    file_bytes=data,
    filename="report.pdf",
    ocr_config=OcrConfig(enabled=True),
)
# result.sections -- list of DocumentSection with title, content, page numbers