PyNet Bridge
PyNet Bridge MCP server enables AI models to control Autodesk applications (Navisworks, Revit, AutoCAD) in real-time by executing validated Python scripts and managing the PyNet plugin UI.
System Monitoring: Scan for running Autodesk instances with active PyNet listeners (
list_active_instances) and verify plugin responsiveness via a handshake ping (check_plugin_status).Script Execution: Send and execute Python scripts directly inside the PyNet engine within an Autodesk application (
send_command), targeting a specific process by PID, with built-in static analysis blocking unsafe imports, calls, and attribute access before execution.UI Layout Inspection: Retrieve the full PyNet ribbon UI structure including all modules and buttons (
get_pynet_ui_layout), or list buttons for a specific module (get_buttons_data).Module (Tab) Management: Create new custom ribbon tabs (
create_pynet_module) or permanently delete existing ones along with their contents (delete_pynet_module).Script Button Management: Deploy new script buttons with a name, script path, icon, and tooltip (
deploy_script_button); update metadata or move buttons between modules (update_script_button); or permanently remove buttons (delete_script_button).Output Window Control: Check visibility of the PyNet log/output window (
get_output_window_status) and toggle it on or off (configure_output_window).
Enables interaction with Autodesk Tools (Navisworks and Revit) through the PyNet Platform, allowing for dynamic UI deployment (custom Ribbon tabs and buttons), Python script execution, BIM process automation, and real-time detection of active Autodesk instances.
PyNet Bridge is the execution layer that allows AI models to control Autodesk tools in real-time.
It connects Natural Language â Python â Autodesk desktop tools (Navisworks, Revit, AutoCAD), enabling AI to generate, execute, and refine BIM workflows autonomously.
Available integrations include Navisworks Manage, Revit, and Civil 3D.
This bridge acts as the connective tissue between AI logic and Autodesk desktop APIs, allowing for dynamic UI creation, script execution, and BIM process automation using natural language.
đŦ Demos & Tutorials
See PyNet Bridge in action â these recordings show the full natural-language â BIM-action workflow inside live Autodesk models.
Demo | Description | Video |
â Autonomous BIM Coordination (Revit + Navisworks) | Claude Code drives Revit and Navisworks simultaneously to audit models, resolve clashes by tolerance rules, generate geometry, and self-correct API errors in real time. | |
PyNET + Codex Integration | How to configure PyNet Bridge with Codex and query into Navisworks. |
â Featured: Autonomous BIM Coordination
In this technical demo, Claude Code connects to Navisworks and Revit at the same time to run a full coordination workflow end-to-end:
Engineering criteria as a Skill â the AI applies tolerance rules to decide which interferences to auto-approve.
Real-time data auditing â validates model integrity and detects missing parameters (
PYNET_Classification) before coordinating.Autonomous geometry generation â generates architectural sleeves (pasatubos) directly in Revit, with precise spatial rotation to match pipe angles.
Self-healing code â hits a live Revit API data-type exception, analyzes the failure, rewrites the script on the fly, and completes the task.
Related MCP server: A2A MCP Server
đ How it works
The user describes a task in natural language.
The AI generates a Python script.
PyNet Bridge validates and sends the script.
The PyNet plugin executes it inside Autodesk.
Results are returned back to the AI.
This is what turns AI from a chatbot into an execution engine for BIM.
đ What makes PyNet Bridge powerful
AI â Action: Turns AI-generated code into real actions inside Navisworks/Revit
Real-time Execution: Run scripts instantly without leaving the BIM environment
Dynamic UI Creation: Let AI create tools, buttons and workflows on the fly
Reliable Communication: Fast and stable, entirely local
Model-Aware Automation: Operates directly on live BIM models
đ ī¸ Installation
â Option A â Automatic installer (recommended)
Open PowerShell and run:
irm https://raw.githubusercontent.com/RAEN-DT/PyNetBridge/main/install.ps1 | iexThis will automatically:
Check Python 3.10+ is installed
Install
pynet-mcp-bridgefrom PyPI (viauvorpip)Auto-detect and configure all installed AI clients:
Claude Desktop (standard and Microsoft Store versions)
Claude Code (VS Code extension / CLI)
Cline (VS Code extension)
Roo Code (VS Code extension)
Codex CLI (
~/.codex/config.toml)
The pynet-mcp-bridge package includes:
Package | Purpose |
pynet-mcp-bridge | MCP server that connects AI models with Autodesk tools via PyNET |
mcp[cli] | Model Context Protocol SDK and CLI tools |
fastmcp | High-level MCP server framework |
psutil | System process detection (finds running Autodesk instances) |
Restart your AI client(s) after installation to apply changes.
đĻ Python Libraries Starter Pack (optional)
Install the recommended Python libraries for Navisworks, Revit and Civil 3D scripting with PyNET:
irm https://raw.githubusercontent.com/RAEN-DT/PyNetBridge/main/install-libraries.ps1 | iexThis installs:
Library | Purpose |
pandas | Data analysis and manipulation |
plotly | Interactive charts and visualizations |
matplotlib | Static plots and graphs |
dash | Web dashboards from Python |
These are the third-party libraries listed under Allowed Python Imports. Standard library modules (
json,sys,re, etc.) are already included with Python.
Prerequisites
PyNet Platform plugin installed in Navisworks/Revit.
Python 3.10 or higher â python.org â Python 3.14 is supported.
uv â docs.astral.sh/uv â required. The Claude Desktop extension (
.mcpb) launches the server withuvx pynet-mcp-bridge, souvmust be installed and on yourPATH. Install it withwinget install astral-sh.uv. Withoutuv, the extension will fail to start.Git â git-scm.com â required for VS Code extensions (Claude Code, Cline, Roo Code) to function correctly.
For Cline / Roo Code: VS Code â code.visualstudio.com
đ§ Option B â Manual installation
1. Install the package:
uv tool install pynet-mcp-bridgeOr with pip:
pip install pynet-mcp-bridge2. Configure Claude Desktop:
Add the following to your claude_desktop_config.json:
Standard:
%APPDATA%\Claude\claude_desktop_config.jsonMicrosoft Store:
%LOCALAPPDATA%\Packages\Claude_*\LocalCache\Roaming\Claude\claude_desktop_config.json
{
"mcpServers": {
"pynet-bridge": {
"command": "pynet-bridge",
"args": []
}
}
}3. Configure Claude Code (VS Code extension):
Add to %USERPROFILE%\.claude.json:
{
"mcpServers": {
"pynet-bridge": {
"type": "stdio",
"command": "pynet-bridge",
"args": []
}
}
}4. Configure Cline:
Add to %APPDATA%\Code\User\globalStorage\saoudrizwan.claude-dev\settings\cline_mcp_settings.json:
{
"mcpServers": {
"pynet-bridge": {
"type": "stdio",
"command": "pynet-bridge",
"args": []
}
}
}5. Configure Roo Code:
Add to %APPDATA%\Code\User\globalStorage\rooveterinaryinc.roo-cline\settings\mcp_settings.json:
{
"mcpServers": {
"pynet-bridge": {
"type": "stdio",
"command": "pynet-bridge",
"args": []
}
}
}6. Configure Codex CLI:
Add to %USERPROFILE%\.codex\config.toml:
[mcp_servers.pynet-bridge]
command = "C:/Users/<user>/.local/bin/pynet-bridge.exe"
args = []đ ī¸ Available MCP Tools
These tools allow AI to fully control the PyNet environment, from UI creation to script execution and system monitoring. Once connected, the AI will have access to the full suite of PyNet tools:
đ§ Core capabilities exposed to AI
đ System & Connection
list_active_instances: Scans the system for running Autodesk processes with an active PyNet connection.
check_plugin_status: Handshake ping to verify the plugin listener is responsive.
đī¸ Module (Tab) Management
get_pynet_ui_layout: Fetches the full UI structure (ButtonsModules and ScriptButtons).
create_pynet_module: Creates a new custom Tab (ButtonsModule) in the Ribbon.
delete_pynet_module: Permanently deletes a module and all its contents.
đ Button Management
get_buttons_data: Lists all script buttons for a specific module ID.
deploy_script_button: Installs a new ScriptButton into a specific module (Name, Script, Icon, Tooltip).
update_script_button: Updates metadata for an existing ScriptButton or moves it to another module.
delete_script_button: Permanently removes a ScriptButton from a module by Id.
đģ Execution & Console Control
send_command: Direct script execution in the PyNet engine (Target PID, Script Name, Content).
get_output_window_status: Checks if the output window is currently available/visible.
configure_output_window: Toggles the visibility of the PyNet log/output window.
đ§ BIM Viewer Control (VS Code)
Drives the PyNet BIM Viewer open in VS Code. Reads clash data from the loaded .pnt package and controls the 3D view over a local (127.0.0.1) connection â nothing leaves your machine.
viewer_status: Reports whether a PyNet BIM Viewer is open (port, package, data dir).
viewer_list_clashes: Reads the loaded package's
clashes.jsonand returns each clash with itspnt_ididentifiers.viewer_load_package: Loads a
.pntinto the open viewer, pushes the models, and returns a summary.viewer_highlight_clash: Highlights a clash pair by
pnt_id(element A red, element B green).viewer_fit: Fits the camera to all models in the viewer.
viewer_clear: Clears highlights and ghosting in the viewer.
đĄ Usage Examples
These are realistic prompts you can give your AI client once PyNet Bridge is connected.
Example 1 â Discover a running model and query it (read-only)
Prompt: "Connect to my open Navisworks model and tell me how many items are in the current selection."
The AI calls list_active_instances to find the running Navisworks process (e.g. Navisworks (PID 12345)), then calls send_command with a short Python script that reads the active document's selection. Expected output: a message like Current selection contains 42 items.
Example 2 â Run a clash-detection summary
Prompt: "Give me a summary of clash test results in the open model and export the counts as a table."
The AI calls list_active_instances, then send_command with a script that iterates the DocumentClash results using the whitelisted Autodesk.Navisworks.Clash assembly and returns counts per test. Expected output: a table such as Test 'Structure vs MEP': 17 active, 3 resolved.
Example 3 â Build a custom ribbon tool on the fly
Prompt: "Create a new ribbon tab called 'QA' and add a button that runs my hide-empty-layers script."
The AI calls create_pynet_module (returns a new module ID), then deploy_script_button with the button name, script path, icon and tooltip. Expected output: Button 'Hide Empty Layers' deployed to module QA. â visible immediately in the Autodesk ribbon.
âšī¸ Every script sent via
send_command/send_command_by_pathis first validated by the built-in static analyzer (see Safe AI Execution). Scripts that violate the import/call whitelist are rejected before reaching Autodesk.
đĄī¸ Safe AI Execution
PyNet Bridge includes a built-in validation layer that ensures all AI-generated scripts are safe and controlled before execution.
â Prevents unsafe operations
â Blocks unauthorized system access
â Guarantees controlled interaction with BIM models
AI remains powerful, but within safe boundaries
Starting from v1.1.1, the MCP server includes a built-in static analyzer that validates every script before it reaches the Autodesk host. All scripts are parsed and inspected at the bridge level â rejected scripts never leave the MCP server.
Allowed CLR Assemblies
Only these .NET references are permitted via clr.AddReference:
Common:
System,System.Windows.Forms,System.Drawing,System.Collections.GenericNavisworks:
Autodesk.Navisworks.Api,.ComApi,.Interop.ComApi,.ClashRevit:
RevitAPI,RevitAPIUIAutoCAD / Civil 3D:
AcMgd,AcCoreMgd,AcDbMgd,AecBaseMgd,AecPropDataMgd,AeccDbMgdPyNet plugins:
Raen.Core.Pynet.*,Raen.{Product}.Pynet.*(any version â e.g.Raen.Core.Pynet.Resources,Raen.Navisworks.Pynet.2024,Raen.Civil3D.Pynet.2026)
Allowed Python Imports
clr, sys, json, re, time, datetime, pathlib, typing, threading, collections, xml, math, pandas, plotly, matplotlib, dash, webbrowser, psutil, functools
Allowed Python Submodules
Some modules are allowed at the submodule level only, preventing access to dangerous siblings:
Allowed | Blocked | Reason |
|
| Allow local HTTP serving, block outbound requests |
Blocked Python Imports
os, subprocess, shutil, socket, ctypes, pickle, importlib, urllib, signal, multiprocessing, tempfile, glob, inspect, code, codeop
Blocked Calls
eval, exec, compile, __import__, getattr, setattr, delattr, globals, locals, vars, breakpoint, open
Blocked Attribute Access
__builtins__, __subclasses__, __globals__, __code__
Any script that violates these rules is immediately rejected with a descriptive error message, without ever being sent to the plugin.
đ Project Structure
pynet_mcp/: Core MCP server logic (FastMCP).
pyproject.toml: Package configuration and dependency management.
đĨ Getting Started
Start building autonomous BIM workflows in minutes.
Install the bridge, connect your AI client, and turn natural language into real actions inside your models.
â FAQs
Have questions about installation, configuration, or usage? Check the full FAQ page:
đ PyNet FAQs
đ How This MCP Fits Into the Ecosystem
This MCP is part of a modular system designed to enable AI-driven BIM automation across Autodesk tools.
This repository is designed to work alongside:
PyNet Platform â Executes scripts inside Navisworks, Revit & Civil 3D via Python.NET
PyNet Library â Gives the AI context with a Python scripts library
Together, these components enable:
Natural Language â AI â Python Script â PyNet â Autodesk â BIM Action
Component | Repository | Purpose |
PyNet Platform | Navisworks, Revit & Civil 3D plugin â hosts the Python.NET engine | |
PyNet Bridge (MCP) | This repo | MCP server - connects AI models to PyNET with including secure scripts validation |
PyNet Library | Script reference library and AI context |
đ Privacy Policy
PyNet Bridge runs entirely on your local machine. It does not collect, store, or transmit any personal data, telemetry, or analytics.
No data collection: The server does not send any information to RAEN Digital Tools or any third party.
Local-only communication: All communication between the MCP server and the PyNet plugin running inside Autodesk stays on your machine. No network sockets, no remote endpoints â nothing leaves your computer.
Your scripts and model data stay on your machine and are only processed by the AI client you have connected.
Full privacy policy: https://privacy.raendt.com/
đ Support
Issues / bug reports: GitHub Issues
FAQ: PyNet FAQs
Contact: info@raendt.com
đĨī¸ Platform Support
PyNet Bridge is Windows-only. It relies on Windows-specific local communication facilities and on Autodesk desktop applications (Navisworks, Revit, Civil 3D), which are Windows products. macOS and Linux are not supported.
đ License
This project is licensed under the MIT License.
mcp-name: io.github.Rafael-NunezDeArenas/pynet-mcp-bridge
Available Tools
23 toolscheck_plugin_statusBRead-only
Handshake ping to verify the plugin listener is responsive.
| Name | Required | Description | Default |
|---|---|---|---|
| pid | Yes |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, so the description's 'handshake ping' adds mild behavioral context (non-destructive). However, it does not discuss rate limits or failure modes.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single concise sentence that efficiently conveys the tool's essence without extraneous information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple ping tool with an output schema, the description is too sparse. It fails to mention what the output indicates (success/failure) or error handling, leaving the agent underinformed.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, and the description provides no explanation of the 'pid' parameter. The agent cannot infer what the integer represents or how to use it.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it is a 'handshake ping' to verify plugin listener responsiveness, which is a specific and distinct purpose. It distinguishes from sibling tools that perform other functions.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool or when not to. Lacks mention of prerequisites or alternatives, leaving the agent without usage context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
configure_output_windowCDestructive
Toggles the visibility of the PyNet log/output window.
| Name | Required | Description | Default |
|---|---|---|---|
| pid | Yes | ||
| is_available | Yes |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The annotations include destructiveHint: true, but the description adds no context beyond 'toggles visibility'. It fails to explain what the destructive nature refers to (e.g., state reset, data loss) or any side effects, leaving the agent without critical behavioral insight.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise at one sentence. While it lacks detail, it is front-loaded and direct, with no wasted words. However, it could be longer to add necessary context without sacrificing conciseness.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has two required parameters and an output schema, the description is insufficient. It does not explain what the parameters mean (e.g., pid identifier, is_available toggles to true/false), nor does it describe the return value. The destructive hint is not addressed, making the overall completeness low.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 0% description coverage for its two required parameters (pid, is_available). The description provides no additional meaning or constraints for these parameters, leaving their purpose ambiguous.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb 'toggles' and identifies the resource 'visibility of the PyNet log/output window'. It clearly distinguishes the tool's function from sibling tools like get_output_window_status, which likely reads the status without modifying it.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives such as get_output_window_status. The description does not mention prerequisites, context, or when not to use it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
create_pynet_moduleCDestructive
Creates a new custom Tab (ButtonsModule) in the Ribbon.
| Name | Required | Description | Default |
|---|---|---|---|
| pid | Yes | ||
| name | Yes |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide destructiveHint. The description adds minimal behavioral context beyond 'creates', such as effects on existing modules or permissions needed.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
A single sentence is concise but lacks necessary detail, achieving a middle ground.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given only 1 sentence and no parameter info, the description is incomplete for a 2-parameter tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, but the description does not explain the parameters (pid, name) at all, leaving the agent without semantic clues.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action 'Creates' and the resource 'new custom Tab (ButtonsModule) in the Ribbon', distinguishing it from siblings like delete_pynet_module.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use or alternatives. Lacks context for decision-making.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
delete_pynet_moduleCDestructive
Permanently deletes a module and all its contents.
| Name | Required | Description | Default |
|---|---|---|---|
| pid | Yes | ||
| module_id | Yes |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds 'permanently' to the existing destructiveHint annotation, emphasizing irreversibility but not disclosing other traits like required permissions or effects on dependencies. The annotation already signals destructiveness, so the description adds minimal new insight.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single 7-word sentence, very concise. However, it is too brief to inform the agent adequately, sacrificing substance for brevity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a destructive tool with two required parameters, the description lacks important context such as error handling (e.g., if module doesn't exist), dependencies, and whether the deletion cascades. An output schema exists but does not compensate for missing behavioral context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description does not explain the parameters 'pid' and 'module_id' beyond what is in the schema. With 0% schema description coverage, the tool relies entirely on the description for parameter meaning, which it fails to provide.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states the tool deletes a module and all its contents, clearly specifying the verb 'delete' and resource 'module'. However, it does not differentiate from sibling tools like 'delete_script_button', which also delete but on different resources.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives, such as the sibling tools that also perform deletions. There is no mention of prerequisites or conditions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
delete_script_buttonBDestructive
Permanently removes a ScriptButton from a module by Id.
| Name | Required | Description | Default |
|---|---|---|---|
| pid | Yes | ||
| button_id | Yes | ||
| module_id | Yes |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The annotation already provides 'destructiveHint: true', which signals destructiveness. The description adds 'permanently removes', consistent with the annotation. However, it does not disclose additional behavioral details like required permissions, cascading effects on other data, or error conditions. The annotation carries most of the burden here.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence with no superfluous words. It is front-loaded with the action and resource. Every word earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has three required parameters with no descriptions and a destructive action, the description should provide more context about the removal process, return value, or prerequisites. The output schema is present but its content is unknown; the description does not compensate for the lack of parameter documentation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 0% description coverage on parameters, and the description only says 'by Id', failing to explain what each parameter represents (e.g., pid likely a project ID, module_id a module, button_id the button identifier). The field names offer some clue but are insufficient for an agent to correctly map parameters without additional context.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states that the tool 'permanently removes a ScriptButton from a module by Id'. It uses a specific verb ('removes'), identifies the resource ('ScriptButton'), and specifies the scope ('from a module by Id'). This distinguishes it from siblings like 'deploy_script_button' (create) and 'update_script_button' (modify).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no explicit guidance on when to use this tool versus alternatives like 'deploy_script_button' or 'update_script_button'. It does not mention prerequisites, when not to use, or how it fits into a workflow. The word 'permanently' hints at irreversibility but is not a substitute for explicit guidelines.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
deploy_script_buttonBDestructive
Installs a new ScriptButton into a specific module (Name, Script, Icon, Tooltip).
| Name | Required | Description | Default |
|---|---|---|---|
| pid | Yes | ||
| name | Yes | ||
| tooltip | No | ||
| icon_name | No | Default | |
| module_id | Yes | ||
| script_Path | Yes |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations mark destructiveHint=true, but description doesn't disclose what makes it destructive (e.g., overwriting existing buttons). No contradiction, but missing detail.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Single sentence with 10 words, no fluff. Clearly states purpose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With 6 parameters, a destructive hint, and an output schema, the description is too sparse. Does not explain critical parameters like pid and module_id, nor return behavior.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, but description lists Name, Script, Icon, Tooltip which helps map to parameters. However, pid and module_id remain unexplained.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Description uses specific verb 'Installs' with clear resource 'ScriptButton into a specific module', and mentions parameters in parentheses. Distinct from sibling tools like update_script_button.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool vs alternatives like update_script_button or delete_script_button. Doesn't mention prerequisites or context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_buttons_dataARead-only
Lists all script buttons for a specific module ID.
| Name | Required | Description | Default |
|---|---|---|---|
| pid | Yes | ||
| module_id | Yes |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, so the description adds little beyond that. It mentions the tool lists buttons by module ID, which is consistent but not additional behavioral context.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Single sentence, concise and front-loaded. No unnecessary words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Description is minimal given two parameters without explanation. Output schema exists but is not shown; return values may be documented there. However, more context about the purpose of pid would improve completeness.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%; description does not explain the parameters pid or module_id beyond implying module_id is the module ID. With low coverage, the description fails to add meaning to the parameters.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states verb 'Lists' and resource 'script buttons' with a qualifier 'for a specific module ID'. It is specific and distinguishes from sibling tools like delete_script_button or deploy_script_button.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No explicit when-to-use or when-not-to-use guidance. However, the read-only nature is implied by the description and annotations, distinguishing it from mutation tools among siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_output_window_statusBRead-only
Checks if the output window is currently available/visible.
| Name | Required | Description | Default |
|---|---|---|---|
| pid | Yes |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true. Description adds minimal context about checking availability/visibility, but does not specify return format or edge cases. Adequate but not comprehensive.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Single sentence with no wasted words. Front-loaded and efficient.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple tool with a single parameter and an output schema, the description is minimal but acceptable. However, the lack of parameter explanation and return value context leaves room for improvement.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0% with no parameter descriptions. The description does not mention the 'pid' parameter, leaving the agent without any context on what value to provide.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'checks' and the resource 'output window status' with specific scope 'available/visible'. It uniquely distinguishes this tool from sibling tools like viewer_status or check_plugin_status.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool versus alternatives. No mention of prerequisites or context. Usage context is implied from the tool name alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_pynet_ui_layoutCRead-only
Fetches the full UI structure (ButtonsModules and ScriptButtons).
| Name | Required | Description | Default |
|---|---|---|---|
| pid | Yes |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations provide readOnlyHint=true, so the tool is safe. The description adds context about the fetched content (ButtonsModules and ScriptButtons). It does not disclose potential errors, performance implications, or authorization needs, but annotations cover the safety aspect.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single concise sentence, front-loaded with the core action. No wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
While the output schema exists, the description omits explanation of the required 'pid' parameter. It also lacks comparison with sibling tools. For a tool with one required parameter, this is insufficient.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, and the description does not explain the 'pid' parameter. The agent has no information about what 'pid' represents or how to use it.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool fetches the full UI structure (ButtonsModules and ScriptButtons). The verb 'Fetches' and resource are specific, but it does not explicitly differentiate from sibling tools like 'get_buttons_data'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives. The description implies it is for obtaining the full UI layout, but does not mention exclusions or context (e.g., compare with get_buttons_data).
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list_active_instancesARead-only
Scans the system for running Autodesk processes with an active PyNet IPC pipe.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide readOnlyHint=true, and description adds behavioral context by specifying the condition of an active PyNet IPC pipe, which is valuable beyond the annotation. No contradictions.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Single sentence, front-loaded with action, no unnecessary words. Highly efficient.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given zero parameters and an existing output schema, the description fully explains the tool's purpose and behavior. It is complete for an agent to invoke without additional context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
No parameters exist, so schema coverage is 100%. The description adds meaning by defining the scope of scanning (processes with active pipe), which compensates for the absence of parameters.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly specifies the verb 'scans' and the resource 'running Autodesk processes with an active PyNet IPC pipe', distinguishing it from sibling tools which focus on plugin, UI, or viewer operations.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No explicit guidance on when to use this tool versus alternatives. While sibling tools are different, the description implies its usage for discovering active instances, but no when-not-to or prerequisites are stated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
send_commandBDestructive
Direct script execution in the PyNet engine (Target PID, Script Name, Content).
| Name | Required | Description | Default |
|---|---|---|---|
| pid | Yes | ||
| content | Yes | ||
| timeout | Yes | ||
| script_name | Yes |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already mark destructiveHint=true; description adds 'direct script execution' but lacks details on side effects or permissions beyond annotation.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Single sentence with parenthetical list; efficient but could better structure parameter explanation.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Destructive tool with 4 required params and no schema descriptions; description lacks scripting constraints, output explanation, and usage context despite having an output schema.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema coverage, description mentions pid, script_name, content but omits timeout and offers no format/type guidance for content or script_name.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Clearly states verb 'execute' and resource 'script' with key parameters (PID, Script Name, Content), distinguishing from sibling send_command_by_path which uses path instead of PID.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Implies usage for direct script execution by PID but provides no explicit conditions or comparison with alternative send_command_by_path.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
send_command_by_pathADestructive
Executes a script file directly by path in the PyNet engine, without sending content inline.
| Name | Required | Description | Default |
|---|---|---|---|
| pid | Yes | ||
| timeout | Yes | ||
| file_path | Yes | ||
| script_name | Yes |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Description aligns with annotations (destructiveHint=true) and adds the behavioral distinction of executing via path vs inline. However, it does not disclose other traits like authentication needs, rate limits, or side effects beyond execution.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Single sentence that is front-loaded and efficient, with no wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Despite having an output schema (not shown), the description omits critical context for a destructive, path-based execution tool: file existence, permissions, error handling, and typical usage scenarios. This is inadequate given the tool's complexity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema has 0% description coverage, and the description provides no meaning for any of the 4 required parameters (pid, script_name, file_path, timeout). The agent must infer solely from parameter names, which is insufficient for correct invocation.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states the action ('Executes a script file directly by path') and the resource ('in the PyNet engine'), and contrasts with inline sending, distinguishing it from sibling tool 'send_command'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Implied usage when a script file path is available, but no explicit guidance on when to use this tool versus alternatives like send_command, nor any exclusions or prerequisites.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
update_script_buttonBDestructive
Updates metadata for an existing ScriptButton or moves it to another module.
| Name | Required | Description | Default |
|---|---|---|---|
| pid | Yes | ||
| name | Yes | ||
| tooltip | Yes | ||
| button_id | Yes | ||
| icon_name | Yes | ||
| module_id | Yes | ||
| script_Path | Yes | ||
| dest_module_id | No |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The annotations already declare destructiveHint=true, and the description confirms mutation by using 'updates' and 'moves'. However, no additional behavioral details are given (e.g., authorization requirements, side effects, or rollback capabilities), so the description adds limited value beyond the annotation.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence that efficiently communicates the two core functions upfront. There is no redundant information, and every word contributes to understanding the tool's purpose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Despite having 8 parameters (7 required) and an output schema, the description does not clarify parameter groupings, the optional nature of dest_module_id for moving, or what the output contains. An agent would need additional context to use the tool correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage, the description should compensate but only weakly connects 'metadata' to parameters like name, tooltip, and icon_name, and 'move' to dest_module_id. It does not explain the role of each parameter or provide examples, leaving the agent to infer the mapping.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states two specific actions (updating metadata and moving to another module) with a concrete resource (ScriptButton). It effectively distinguishes from sibling tools like delete_script_button and deploy_script_button by specifying the operations that are unique to this tool.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for updating or moving a ScriptButton, but it does not explicitly state when to use this tool versus alternatives such as delete_script_button or deploy_script_button. No exclusions or context-specific guidance is provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
viewer_clearARead-only
Clears highlights and ghosting in the open viewer.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations declare readOnlyHint: true, but the operation modifies the viewer's visual state. The description does not elaborate on side effects or whether the action is reversible. However, it is clear that it removes existing highlights/ghosting.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
A single, informative sentence with no unnecessary words. It front-loads the core action and object.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple tool with no parameters and an output schema, the description is sufficient. It could be improved by noting that it affects only the open viewer.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has no parameters, so the baseline is 4. The description does not need to add parameter details since the schema is empty and 100% covered.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('clears') and the target resource ('highlights and ghosting in the open viewer'). It distinguishes from sibling tools like viewer_highlight_clash and viewer_isolate.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No explicit guidance on when to use this tool versus alternatives. The description implies it should be used after highlights or ghosting are applied, but does not mention prerequisites or scenarios.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
viewer_fitARead-only
Fits the camera to all models in the open viewer.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate readOnlyHint=true, so the read-only safety is covered. The description adds value by specifying that the camera fits to 'all models' (not just selected), which is a behavioral trait. No contradictions.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence that is perfectly front-loaded and contains no fluff. Every word earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has no parameters, simple action, and an output schema (mentioned in context), the description is complete. An agent can understand exactly what the tool does without additional context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
There are zero parameters, and schema coverage is 100% trivially. The description does not need to add parameter details. Baseline 4 is appropriate for no parameters.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('fits the camera') and the target ('all models in the open viewer'), distinguishing it from sibling tools like 'viewer_isolate' or 'viewer_clear'. The verb 'fits' is slightly technical but specific enough for an AI agent.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool vs alternatives like 'viewer_isolate' or 'viewer_select'. It lacks context for prerequisites or situations where this tool is appropriate.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
viewer_get_propertiesARead-only
Reads element properties (name, model, psets) from the loaded package's properties.json.
Pass one or more pnt_ids to get their properties. Called with no pnt_ids, returns a lightweight index (pnt_id â name, model) so you can discover what's available. The data source is the .pnt's properties.json, NOT the viewer or IFC.
| Name | Required | Description | Default |
|---|---|---|---|
| pnt_ids | No |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide readOnlyHint. Description adds valuable context: the data comes from properties.json, not the viewer; the tool returns an index when called without pnt_ids. No contradictions with annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two short paragraphs, front-loaded with the core purpose. Every sentence adds value; no wasted words. Highly efficient.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the presence of an output schema, description need not detail return structure. It covers dual behavior and data source. Could mention error handling or prerequisites, but for a read-only tool with one optional param, it is sufficiently complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, so description fully explains the single parameter pnt_ids: optional, triggers property retrieval when provided, or returns a lightweight index when omitted. This meaningfully compensates for the lack of schema description.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states it reads element properties from properties.json, with specific verb 'reads' and resource 'element properties (name, model, psets)'. Distinguishes from siblings by specifying the data source and dual behavior (index vs. full properties).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly says when to use with pnt_ids and without, and clarifies the data source is NOT the viewer or IFC. No explicit when-not or alternatives to sibling tools, but the context is clear enough for selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
viewer_get_stateARead-only
Reads the viewer's last reported state (currently the list of loaded models).
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description aligns with the readOnlyHint annotation, confirming a read-only operation. It adds useful detail about the return value ('list of loaded models'), which is beyond what the annotation provides, enhancing transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, clear sentence with no unnecessary words. It front-loads the action and outcome efficiently.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple, parameterless tool with an output schema, the description is complete. It indicates what is returned (the state, currently list of models) and the read-only nature, covering all essential aspects.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
There are no parameters, so per guidelines the baseline is 4. The description does not need to add parameter information and is adequate for a parameterless tool.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool reads the viewer's last reported state and specifies it currently returns the list of loaded models. It uses a specific verb ('Reads') and resource ('viewer's state'), and distinguishes from sibling viewer tools like viewer_get_properties or viewer_isolate by focusing on general state.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Although no explicit when-to-use or when-not-to-use instructions are given, the tool has no parameters and the purpose is straightforward. Given the sibling tools, it's clear this is the generic state reader, so context is sufficient.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
viewer_highlight_clashCDestructive
Highlights a clash pair in the open viewer by pnt_id (element A red, element B green).
| Name | Required | Description | Default |
|---|---|---|---|
| pnt_id_a | No | ||
| pnt_id_b | No |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotation indicates destructiveHint=true, but the description describes a non-destructive highlight operation. This contradiction undermines transparency. No other behavioral traits are disclosed.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
One efficient sentence, front-loaded with the action. No unnecessary words, though the parenthetical could be clearer regarding the color mapping.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given that annotations provide destructiveHint=true (contradicting) and no output description, the tool lacks essential context. It doesn't mention that the viewer must be open, what happens if pnt_ids are invalid, or the return value (output schema exists but unused). Incomplete for effective use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema has 0% description coverage. The description adds mapping: pnt_id_a for element A (red), pnt_id_b for element B (green), which provides some meaning beyond the schema. However, it lacks details on valid values, format, or optionality.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'highlights', the resource 'clash pair', and specifies the visual effect (element A red, element B green). It distinguishes well from sibling tools like viewer_isolate or viewer_select.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool versus alternatives, nor any prerequisites mentioned. The description does not address when highlighting is appropriate compared to viewer_isolate or other tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
viewer_isolateADestructive
Isolates (hides everything except) the given pnt_ids in the open viewer.
Note: the command is broadcast, but the current viewer build ignores the 'isolate' action (frontend handler pending). It will take effect once the viewer wires it.
| Name | Required | Description | Default |
|---|---|---|---|
| pnt_ids | No |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds important behavioral context beyond annotations: it notes that the command is broadcast but currently ignored due to a pending frontend handler. This alerts the agent to a potential no-op. The destructiveHint annotation is consistent with the description.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences: first states the core function, second adds critical behavioral disclosure. No wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description covers purpose, parameter meaning, and current limitation. It assumes the viewer is open (implied by 'in the open viewer'). With an output schema present but not described, the description is sufficient for a simple 1-parameter tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The parameter 'pnt_ids' has 0% schema description coverage, so the description must compensate. It explains that these are the IDs to isolate to, but lacks detail on format or constraints. The parameter is not required, but no default behavior is described.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Isolates (hides everything except) the given pnt_ids in the open viewer.' It uses a specific verb 'isolates' and specifies the resource 'viewer'. This distinguishes it from sibling tools like viewer_clear or viewer_select.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies when to use (to hide all except given point IDs) but does not provide explicit guidance on alternatives or when not to use. No comparison with sibling tools is given.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
viewer_list_clashesARead-only
Reads the loaded package's clashes.json â the data source, NOT the viewer/IFC.
Returns each clash with the pnt_id identifiers needed to highlight it.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations declare readOnlyHint=true; description adds context that it reads a specific file (clashes.json) rather than viewer data. No contradictions. Clarifies the non-destructive nature and data source.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two concise sentences covering purpose, data source, and key output. No extraneous content.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given zero parameters, presence of output schema, and simple read-only operation, the description provides sufficient context. It mentions the critical return element (pnt_id identifiers) that aids tool selection.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
No parameters (schema coverage 100%). Description adds meaning by specifying that the return includes pnt_id identifiers for highlighting, which is useful beyond the empty schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Clearly states it reads clashes.json (data source) and returns clash data with pnt_id identifiers for highlighting. Distinguishes from viewer/IFC and sibling tools like viewer_highlight_clash.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explains the tool's input (loaded package's clashes.json) and output (pnt_id identifiers). Implicitly contrasts with viewer/IFC and highlighting tool, but does not explicitly state when not to use alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
viewer_load_packageBDestructive
Loads a .pnt into the open viewer and returns a summary read from clashes.json.
| Name | Required | Description | Default |
|---|---|---|---|
| pnt_path | Yes |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate destructive hint, so description should elaborate on side effects. It mentions loading but does not clarify if previous viewer state is cleared, overwritten, or merged. The return of a summary is noted but not the behavioral impact on viewer state.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Single sentence, efficient, front-loaded with action and result. No unnecessary words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the destructive hint and a single parameter, the description lacks completeness. It does not state if viewer must be empty, if loading fails gracefully, or how the summary is used. Output schema is present but not described.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, so description must add meaning. It mentions 'pnt_path' implicitly but provides no details on file format, required permissions, or format constraints. The parameter is not explained beyond its name.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('loads'), the resource ('.pnt file'), the destination ('open viewer'), and the return value ('summary from clashes.json'). It is specific and distinguishes from sibling tools like viewer_clear or viewer_highlight_clash.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool versus alternatives. It does not mention prerequisites (e.g., viewer must be open) or typical workflow (e.g., use after viewer_list_clashes).
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
viewer_selectADestructive
Highlights a set of elements in the open viewer by pnt_id.
Pass a list of pnt_ids in pnt_ids (group A). Optionally pass a second list in
pnt_ids_b to highlight a second group in a distinct colour. Replaces any previous
selection. Use viewer_list_clashes / viewer_get_properties to obtain pnt_ids.
| Name | Required | Description | Default |
|---|---|---|---|
| pnt_ids | No | ||
| pnt_ids_b | No |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Description notes that the tool replaces any previous selection, aligning with destructiveHint: true. Adds color distinction for second group. No contradiction with annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Three sentences, front-loaded with primary function. Could be slightly more concise, but no waste.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Covers key behaviors: highlighting, replacement, and obtaining IDs. Lacks details on error handling or behavior with invalid IDs. Output schema exists, so return value is not needed.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, so description compensates partially. Explains pnt_ids as group A and pnt_ids_b as optional second group with distinct color, but does not detail array format or behavior when both are empty.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('highlights'), the resource ('elements in the open viewer'), and the identifier ('by pnt_id'). It distinguishes from sibling tools like viewer_clear and viewer_isolate by focusing on highlighting.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly says to use viewer_list_clashes or viewer_get_properties to obtain pnt_ids, and describes optional second group. Lacks explicit when-not-to-use, but overall context is clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
viewer_statusARead-only
Reports whether a PyNet BIM Viewer is open in VS Code (port, package, data dir).
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, so the description adds value by specifying the reported status info. No contradictions exist, but the description does not elaborate on other behavioral traits like authentication or permissions.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, well-structured sentence that conveys the core purpose and output immediately. No unnecessary words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the absence of parameters, the presence of readOnlyHint annotation, and an output schema, the description sufficiently explains what the tool does. It lacks only minor context like error conditions or response format, but the output schema covers that.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters, so the description does not need to explain parameter semantics. The baseline score of 4 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: reports whether a PyNet BIM Viewer is open in VS Code, and specifies the details it provides (port, package, data dir). While it differentiates from siblings by focusing on the viewer status, it does not explicitly exclude other status-checking tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives such as check_plugin_status or list_active_instances. Agents must rely on the tool name and context to infer usage.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
TDQS
Each tool has a clear, distinct purpose. Viewer tools (viewer_select, viewer_highlight_clash, viewer_isolate) are differentiated by their specific actions. Module/script tools are well-separated. The only slight overlap is between send_command and send_command_by_path, but their descriptions clarify the difference (inline content vs file path).
All tools follow a consistent verb_noun pattern in snake_case. Viewer tools use a 'viewer_' prefix grouping related functionality. Verbs are imperative and clear (create, delete, get, list, send, etc.). No mixing of camelCase or other conventions.
23 tools is on the higher end but reasonable for the scope of managing a plugin system and a BIM viewer. While borderline, each tool seems necessary for the domain, and there are no redundant tools. The count is slightly above ideal but still manageable.
The tool set covers CRUD for modules and script buttons well, but the viewer side has gaps: no tool to unload a package, close the viewer, or list available script commands. Additionally, viewer_isolate is noted as not yet functional, representing a missing feature. Some operations like updating a script button's content are present, but the overall coverage is not fully comprehensive.
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