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 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.
ToolServer holds no NamespaceCollection and has no constructor parameter to take one: register_tool / deregister_tool publish the manifest and nothing else. The pod-side emitter that once wrote and deleted these rows is deleted -- its write could not land (the agent's L3 proxy resolves platform-scoped writes to the per-agent agent_<hex> schema, which has no namespaces table) and its delete raised on every call (it passed a bare UUID to a Collection keyed on the composite (row_scope, namespace_id)). packages/agent/tools/tests/enforcement/test_no_agent_side_namespace_writes.py fails a build that brings any of it back.
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
1 maintainer
Release files for 3tears-agent-tools 0.47.1
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