Greenloom CAD MCP Server
OfficialProvides automation and headless DXF generation for AutoCAD LT, including drawing management, entity CRUD, layer management, block operations, annotations, P&ID symbol library, and viewport/screenshot capture.
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Here is a step-by-step guide with screenshots.
Greenloom CAD MCP Server
MCP server for AutoCAD LT automation and headless DXF generation.
Two backends, one API:
Backend | Runtime | Requires AutoCAD? | Screenshot |
File IPC | Windows Python | Yes — AutoCAD LT 2024+ (Windows) | Win32 PrintWindow |
ezdxf | Any platform | No (headless) | matplotlib render |
The server exposes 8 consolidated tools (drawing, entity, layer, block, annotation, pid, view, system) over the MCP stdio transport. An MCP client (Claude Desktop, Claude Code, etc.) connects and drives AutoCAD through natural-language requests.
Prerequisites (File IPC backend)
Windows 10/11 (the File IPC backend uses Win32 APIs for focus-free window messaging)
AutoCAD LT 2024 or newer — AutoLISP support was added in LT 2024 for Windows. AutoCAD LT for Mac exists but does not support AutoLISP.
Python 3.10+ (Windows native — not WSL Python)
uv package manager (install guide)
The ezdxf headless backend works on any platform (Linux, macOS, WSL) for offline DXF generation without AutoCAD installed.
Related MCP server: AutoCAD LT AutoLISP MCP Server
Quick Start
1. Clone and install
git clone https://github.com/Greenmint-labs/greenloom_CAD_MCP.git
cd greenloom_CAD_MCP
uv sync2. Load the LISP dispatcher in AutoCAD LT
Open AutoCAD LT and load mcp_dispatch.lsp using APPLOAD:
Type
APPLOADin the AutoCAD command lineBrowse to
<repo>/lisp-code/mcp_dispatch.lspClick Load
You should see:
=== MCP Dispatch v3.1 loaded ===andReady for commands via (c:mcp-dispatch)
Tip: Add the file to your AutoCAD Startup Suite (in the APPLOAD dialog) so it loads automatically with every drawing.
3. Configure your MCP client
Add to your MCP client configuration (e.g. Claude Desktop claude_desktop_config.json):
{
"mcpServers": {
"greenloom-cad-mcp": {
"command": "C:\\path\\to\\greenloom-cad-mcp\\.venv\\Scripts\\python.exe",
"args": ["-m", "greenloom_cad_mcp"],
"env": { "GREENLOOM_CAD_BACKEND": "auto" }
}
}
}Key points:
The
commandmust point to the Windows Python inside the project venv (not WSL python).GREENLOOM_CAD_BACKENDcan beauto(default — tries File IPC, falls back to ezdxf),file_ipc(requires AutoCAD), orezdxf(headless only).
Running from WSL
If your MCP client runs in WSL (e.g. Claude Code), launch the server through cmd.exe so it runs as a native Windows process:
{
"mcpServers": {
"greenloom-cad-mcp": {
"type": "stdio",
"command": "cmd.exe",
"args": ["/d", "/s", "/c", "cd /d C:\\path\\to\\greenloom-cad-mcp && .venv\\Scripts\\python.exe -m greenloom_cad_mcp"],
"env": { "GREENLOOM_CAD_BACKEND": "auto" }
}
}
}4. Verify
From your MCP client, call:
system(operation="status")You should see backend: "file_ipc" if AutoCAD is running, or backend: "ezdxf" for headless mode.
Tools
drawing — File/drawing management
Operation | Description | File IPC | ezdxf |
| Reset to clean drawing (erase all + purge) | Yes | Yes |
| Open an existing drawing | Yes | Yes (DXF) |
| Get entity count and layers | Yes | Yes |
| Save current drawing (to path if given) | Yes | Yes |
| Export as DXF | Yes | Yes |
| Plot to PDF | Yes | No |
| Purge unused objects | Yes | Yes |
| Get system variables by name | Yes | Yes |
| Undo last operation | Yes | No |
| Redo last undone operation | Yes | No |
entity — Entity CRUD + modification
Create: create_line, create_circle, create_polyline, create_rectangle, create_arc, create_ellipse, create_mtext, create_hatch
Read: list, count, get
Modify: copy, move, rotate, scale, mirror, offset*, array, fillet*, chamfer*, erase
*
offset,fillet,chamferare File IPC only (not supported in ezdxf headless backend).
layer — Layer management
list, create, set_current, set_properties, freeze, thaw, lock, unlock
block — Block operations
Operation | File IPC | ezdxf |
| Yes | Yes |
| Yes | Yes |
| Yes | Yes |
| Yes | Yes |
| Yes | Yes |
| No | Yes |
annotation — Text, dimensions, leaders
create_text, create_dimension_linear, create_dimension_aligned, create_dimension_angular, create_dimension_radius, create_leader
pid — P&ID operations (CTO symbol library)
setup_layers, insert_symbol, list_symbols, draw_process_line, connect_equipment, add_flow_arrow, add_equipment_tag, add_line_number, insert_valve, insert_instrument, insert_pump, insert_tank
P&ID symbol insertion requires the CAD Tools Online (CTO) P&ID Symbol Library installed at
C:\PIDv4-CTO\. The ezdxf backend has built-in CTO library support. For the File IPC backend, some P&ID operations require additional LISP helpers — see the P&ID section in the wiki for setup details.
view — Viewport and screenshot
Operation | Description |
| Zoom to show all entities |
| Zoom to a specified window |
| Capture current AutoCAD view as PNG |
Screenshots use PrintWindow (Win32) for the File IPC backend — works even when AutoCAD is minimized or in the background. The ezdxf backend renders via matplotlib.
system — Server management
status, health, get_backend, runtime, init, execute_lisp
execute_lispruns arbitrary AutoLISP code (File IPC only). Passdata: {code: "(+ 1 2)"}. This turns the server into an extensible automation platform — any valid AutoLISP expression can be executed.
Architecture
MCP Client (Claude)
│ stdio (JSON-RPC)
▼
Python MCP Server (greenloom_cad_mcp)
│
├── File IPC Backend ──► C:/temp/*.json ──► mcp_dispatch.lsp (AutoCAD LT)
│ PostMessageW(WM_CHAR) to MDIClient — no focus steal
│
└── ezdxf Backend ──► in-memory DXF (headless, no AutoCAD needed)The File IPC backend sends keystrokes to AutoCAD's MDIClient window via PostMessageW(WM_CHAR), triggering the (c:mcp-dispatch) AutoLISP command. This approach does not steal window focus — you can continue working in other applications while automation runs.
Environment Variables
Variable | Default | Description |
|
| Backend selection: |
|
| Directory for IPC command/result JSON files (must match on both Python and LISP sides) |
|
| IPC command timeout in seconds (1-300) |
|
| Disable screenshot capture (text feedback only) |
Note: If you change
GREENLOOM_CAD_IPC_DIR, you must also update the*mcp-ipc-dir*variable inmcp_dispatch.lspto match.
Development
uv sync
uv run pytest tests/ -vAutoCAD LT AutoLISP Compatibility
AutoLISP was added to AutoCAD LT in the 2024 release (Windows only). AutoCAD LT for Mac does not support AutoLISP.
Supported (LT 2024+ Windows) | Not Supported |
| VLIDE (Visual LISP IDE) |
All |
|
File I/O ( | Express Tools |
Entity access ( | 3D operations |
Selection sets | AutoLISP on Mac |
The mcp_dispatch.lsp dispatcher is fully compatible with LT 2024+.
What's New in v3.1
execute_lisp— Run arbitrary AutoLISP code via temp file pattern. Turns the server from a fixed command set into an extensible automation platform.Undo / Redo — Single-step undo and redo via
drawingtool.Drawing open — Open existing
.dwgfiles programmatically (FILEDIA suppressed).Drawing create — Now resets current drawing (erase all + purge) instead of
_.NEW, preserving the LISP dispatcher namespace.Drawing save with path —
savewith apathparameter uses SAVEAS; without path uses QSAVE.get_variablesfix — Respects thenamesparameter; returns requested variables with proper type handling.Polyline/leader fix — Point arrays properly encoded via semicolon-delimited format.
ESC prefix — Sends 2x ESC before each dispatch to cancel stale pending commands from prior timeouts.
UTF-8/cp1252 fallback — Handles non-ASCII characters in LISP result files (AutoCAD writes Windows-1252).
Configurable IPC timeout —
GREENLOOM_CAD_IPC_TIMEOUTenv var (1–300 seconds, default 10).Thread-safe backend init —
asyncio.Lockprevents parallel initialization races.
License
MIT
Available Tools
8 toolsannotationA
Annotation: text, dimensions, and leaders.
Operations: create_text — data: {x, y, text, height?, rotation?, layer?} create_dimension_linear — data: {x1, y1, x2, y2, dim_x, dim_y} create_dimension_aligned — data: {x1, y1, x2, y2, offset} create_dimension_angular — data: {cx, cy, x1, y1, x2, y2} create_dimension_radius — data: {cx, cy, radius, angle} create_leader — data: {points: [[x,y],...], text}
| Name | Required | Description | Default |
|---|---|---|---|
| data | No | ||
| operation | Yes | ||
| include_screenshot | 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 mark the tool as non-read-only, and the operation names imply creation, but the description adds no behavioral context beyond that. It does not mention side effects on the drawing, error behavior, coordinate-system assumptions, or what happens after a successful operation.
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 compact, well-structured as a scannable operation list, and every line adds useful information. There is no filler or redundant restating of the schema.
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 generic schema, the description is nearly complete: it covers all operations and their payload shapes. Minor gaps include undocumented behavior of include_screenshot and lack of explicit units or coordinate context, but these do not prevent correct operation 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?
The input schema is generic and has 0% schema description coverage, so the description carries the full burden. It compensates thoroughly by defining the exact data object shape expected for every operation, including required fields and optional markers.
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 names the resource ('Annotation') and enumerates six specific creation operations (text, dimension variants, leader), so an agent knows exactly what the tool does. It is clearly distinguishable from sibling tools like drawing, entity, and layer by its scope.
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 opening line 'Annotation: text, dimensions, and leaders' plus the operation list gives a clear context for when to use this tool. It does not explicitly name alternatives or exclusions, but the intended usage is obvious enough for an agent to select it appropriately.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
blockA
Block definition, insertion, and attribute management.
Operations: list — List all block definitions. insert — data: {name, x, y, scale?, rotation?, block_id?} insert_with_attributes — data: {name, x, y, scale?, rotation?, attributes: {tag: value}} get_attributes — data: {entity_id} update_attribute — data: {entity_id, tag, value} define — data: {name, entities: [{type, ...}]}
| Name | Required | Description | Default |
|---|---|---|---|
| data | No | ||
| operation | Yes | ||
| include_screenshot | No |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With readOnlyHint=false, the description's listing of mutating operations (insert, update_attribute, define) is consistent with the annotation. It adds operation-level context but does not disclose side effects, coordinate assumptions, failure behavior, or what happens when metadata is updated.
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 compact and front-loaded with a one-line summary followed by a structured operation list. Every operation gets a single line with its data payload, and there is no filler or redundant restating of schema fields.
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 six-operation dispatch tool with a generic data field and no enums, the description provides the necessary operation vocabulary and rough data contracts. However, requiredness of fields like x/y/name is not marked, and deeper semantics for define and attribute operations are left vague.
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%, so the description carries the parameter documentation burden, and it does so with per-operation data shapes including optional markers for scale, rotation, and block_id. It still leaves some fields under-specified, such as the entities array in define, attribute value types, and include_screenshot semantics, but the core data contracts are understandable.
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 opening line scopes the tool to block definition, insertion, and attribute management, and the operation list enumerates distinct verbs such as list, insert, define, get_attributes, and update_attribute. This clearly identifies the resource and actions, distinguishing it from sibling drawing, entity, and layer 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 implies block-related usage through the operation list, but it does not explicitly state when to use this tool versus siblings like entity or layer. There is no exclusion guidance or comparison to alternatives, so an agent must infer the appropriate context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
drawingA
Drawing file management.
Operations: create — Create a new empty drawing. data: {name?} open — Open an existing drawing. data: {path} info — Get drawing extents, entity count, layers, blocks. save — Save current drawing. data: {path?} (saves to path if given, else QSAVE) save_as_dxf — Export as DXF. data: {path} plot_pdf — Plot to PDF. data: {path} purge — Purge unused objects. get_variables — Get system variables. data: {names: [...]} undo — Undo last operation. redo — Redo last undone operation.
| Name | Required | Description | Default |
|---|---|---|---|
| data | No | ||
| operation | Yes | ||
| include_screenshot | No |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With readOnlyHint=false, the tool is already marked as potentially mutating, and the description adds meaningful behavioral details: save mentions QSAVE fallback, undo/redo state operations, and purge targets unused objects. It does not disclose destructive side effects (e.g., purge deleting data permanently), but the per-operation explanations go well beyond the bare 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 tightly formatted bullet list with no filler. Each line adds one distinct operation plus a brief explanation and data hint, and the overall purpose is front-loaded in the first line.
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 ten-operation dispatcher, the description covers each operation's function and relevant data requirements, and an output schema exists to cover return values. The main gap is the unexplained `include_screenshot` parameter, which prevents the description from being fully 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 description coverage is 0%, so the description is the only documentation of valid `operation` values and `data` structures. It lists all operations and gives per-operation data hints such as `data: {path}` for open and `data: {names: [...]}` for get_variables. However, the `include_screenshot` parameter is never mentioned, leaving one parameter undocumented.
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 opens with "Drawing file management" and then enumerates ten distinct operations, each with a verb and a resource (e.g., "create — Create a new empty drawing," "plot_pdf — Plot to PDF"). This makes the dispatcher's purpose and each subcommand unambiguous. Although sibling tools are not referenced, the operations are clearly scoped to whole-drawing management, distinguishing it from entity/layer/block 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 operation list implicitly tells an agent when to use this tool (e.g., when saving or opening a drawing), but there are no explicit when-not-to-use statements or pointers to siblings like entity, layer, or block. Usage context is conveyed indirectly through the operation names, not through explicit routing guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
entityA
Entity creation, querying, and modification.
Create operations: create_line — x1, y1, x2, y2, layer? create_circle — data: {cx, cy, radius}, layer? create_polyline — points: [[x,y],...], data: {closed?}, layer? create_rectangle — x1, y1, x2, y2, layer? create_arc — data: {cx, cy, radius, start_angle, end_angle}, layer? create_ellipse — data: {cx, cy, major_x, major_y, ratio}, layer? create_mtext — data: {x, y, width, text, height?}, layer? create_hatch — entity_id, data: {pattern?}
Read operations: list — layer? → list entities count — layer? → count entities get — entity_id → entity details
Modify operations: copy — entity_id, data: {dx, dy} move — entity_id, data: {dx, dy} rotate — entity_id, data: {cx, cy, angle} scale — entity_id, data: {cx, cy, factor} mirror — entity_id, x1, y1, x2, y2 offset — entity_id, data: {distance} array — entity_id, data: {rows, cols, row_dist, col_dist} fillet — data: {id1, id2, radius} chamfer — data: {id1, id2, dist1, dist2} erase — entity_id
| Name | Required | Description | Default |
|---|---|---|---|
| x1 | No | ||
| x2 | No | ||
| y1 | No | ||
| y2 | No | ||
| data | No | ||
| layer | No | ||
| points | No | ||
| entity_id | No | ||
| operation | Yes | ||
| include_screenshot | No |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations only supply readOnlyHint=false, so the description carries the burden. The operation list correctly reflects mutating and read-only behaviors, but does not mention side effects, prerequisites such as an open drawing, or the meaning of the include_screenshot flag.
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 content is organized into Create/Read/Modify groups with one line per operation, and every line adds a signature or behavior. The summary sentence is front-loaded and there is no filler.
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 19 operations and only 1 required schema parameter, the description covers most of what an agent needs to choose and call an operation. It would be more complete if it explicitly stated that operation must be set to one of the listed names and described include_screenshot.
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 schema_description_coverage=0%, the operation-specific parameter shapes such as create_circle data: {cx, cy, radius} and offset data: {distance} provide crucial meaning absent from the schema. It still leaves some semantics implicit, such as angle/factor units and accepted operation strings.
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 opening line 'Entity creation, querying, and modification' names the resource and the three verb families, and the operation list makes the scope concrete. It lacks explicit differentiation from siblings like drawing or block, so it stops short of 5.
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?
It clearly signals this is the tool for entity-level operations such as create_line, list, move, and erase rather than drawing/session-level tasks. There is no explicit 'when-not-to-use' or direct pointer to a sibling, but the context is unambiguous.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
layerA
Layer creation and management.
Operations: list — List all layers with properties. create — data: {name, color?, linetype?} set_current — data: {name} set_properties — data: {name, color?, linetype?, lineweight?} freeze — data: {name} thaw — data: {name} lock — data: {name} unlock — data: {name}
| Name | Required | Description | Default |
|---|---|---|---|
| data | No | ||
| operation | Yes | ||
| include_screenshot | No |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With readOnlyHint=false, the annotations already signal mutation; the description goes further by itemizing mutating operations such as create, set_current, set_properties, freeze, thaw, lock, and unlock. It does not disclose side effects or reversibility, but the operation list provides meaningful behavioral detail 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 compact, front-loaded summary followed by a bulleted operation list with minimal syntax. Every line adds information, and it avoids redundancy with the schema.
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 multi-operation layer tool with a sparse schema, the description covers the core call shape well: operation names and per-operation data. The output schema presumably handles return-value documentation, so the main remaining gap is the unexplained include_screenshot parameter and lack of explicit required-field notes. Overall it is adequate for an agent to invoke most operations 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?
Schema description coverage is 0%, so the description must carry parameter meaning; it does so by listing valid operation strings and showing the expected data object shape for each operation (e.g., create data: {name, color?, linetype?}). However, it leaves include_screenshot completely undocumented and does not explicitly mark which fields within data are required.
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 opens with 'Layer creation and management' and then enumerates eight specific operations (list, create, set_current, set_properties, freeze, thaw, lock, unlock), making both the resource (layers) and the actions concrete. This clearly separates it from sibling tools like entity, block, or view.
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 operation list implies the tool is for any layer-management task, but the description never explicitly states when to choose it over a sibling tool or when not to use it. No prerequisites, exclusions, or alternative tools are mentioned, so usage guidance is only implicit.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
pidA
P&ID drawing with CTO symbol library.
Operations: setup_layers — Create standard P&ID layers. insert_symbol — data: {category, symbol, x, y, scale?, rotation?} list_symbols — data: {category} draw_process_line — data: {x1, y1, x2, y2} connect_equipment — data: {x1, y1, x2, y2} add_flow_arrow — data: {x, y, rotation?} add_equipment_tag — data: {x, y, tag, description?} add_line_number — data: {x, y, line_num, spec} insert_valve — data: {x, y, valve_type, rotation?, attributes?} insert_instrument — data: {x, y, instrument_type, rotation?, tag_id?, range_value?} insert_pump — data: {x, y, pump_type, rotation?, attributes?} insert_tank — data: {x, y, tank_type, scale?, attributes?}
| Name | Required | Description | Default |
|---|---|---|---|
| data | No | ||
| operation | Yes | ||
| include_screenshot | No |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description reveals that operations mutate the drawing by creating layers, inserting symbols, and connecting equipment, which is consistent with the readOnlyHint=false annotation. It does not disclose prerequisites such as whether setup_layers must be called first, coordinate system expectations, or the effect of include_screenshot. It adds operation-level behavior but not deeper 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?
The description is a tight, consistently formatted operation list with no filler. The domain statement is front-loaded, and every line adds a distinct operation or data shape. It is an excellent model of concise reference documentation.
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 multi-operation dispatch tool, the description covers all operation names and their data payloads, and an output schema exists so return-value documentation is not essential. It is slightly incomplete around the include_screenshot parameter and setup ordering, but otherwise sufficient for correct invocation.
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 lists the expected data keys for each operation, including optional fields marked with '?'. This substantially compensates for the generic schema. The only structured parameter not explained is include_screenshot, which prevents a 5.
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 opening phrase 'P&ID drawing with CTO symbol library' plus the enumerated operations (setup_layers, insert_symbol, draw_process_line, etc.) makes the tool's purpose concrete. It stops short of a clean single verb+resource statement and does not explicitly contrast with sibling drawing/layer tools, so it earns 4 rather than 5.
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 operation list implies when to use this tool, such as when inserting P&ID symbols or drawing process lines. However, it never explicitly states when to prefer this tool over sibling tools like drawing, layer, or block, and it gives no exclusions. Guidance is implied rather than explicit.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
systemCRead-only
Server status and management.
Operations: status — Backend info, capabilities, health check. health — Quick health check (ping backend). get_backend — Return current backend name and capabilities. runtime — Return process/runtime details for spawn diagnostics. init — Re-initialize the backend. execute_lisp — Execute arbitrary AutoLISP code (File IPC only). data: {code}
| Name | Required | Description | Default |
|---|---|---|---|
| data | No | ||
| operation | Yes | ||
| include_screenshot | No |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The annotations declare readOnlyHint=true, but the description includes operations 'init' (re-initialize backend) and 'execute_lisp' (execute arbitrary AutoLISP code) which are clearly mutating or potentially destructive. This directly contradicts the read-only annotation. The description also fails to disclose side effects or safety implications of these operations.
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 well-structured with a clear summary and a bulleted list of operations, each with a brief explanation. It is concise without unnecessary verbosity, though the inclusion of potentially dangerous operations warrants more cautionary wording.
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 lists operations but fails to explain return values, error handling, permissions, or the behavior of the 'include_screenshot' parameter. Given the tool's complexity and the presence of potentially destructive operations, the description is insufficient for an agent to use all features safely and 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?
The description adds meaning by listing valid values for the 'operation' parameter and specifying that 'execute_lisp' takes data with a 'code' field. However, it omits any discussion of the 'include_screenshot' parameter and does not clarify whether other operations use 'data', leaving gaps in parameter understanding.
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 identifies the tool as 'Server status and management' and enumerates specific operations (status, health, get_backend, runtime, init, execute_lisp), giving a clear idea of the resource and actions. It does not explicitly contrast with sibling tools, but the system focus is evident from the operation list.
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 like drawing, entity, or layer tools. There is no mention of prerequisites, exclusions, or typical use cases beyond listing operations, so agents must infer usage from operation names.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
viewARead-only
Viewport control and screenshot capture.
Operations: zoom_extents — Zoom to show all entities. zoom_window — Zoom to window: x1, y1, x2, y2 get_screenshot — Capture current view as PNG image.
| Name | Required | Description | Default |
|---|---|---|---|
| x1 | No | ||
| x2 | No | ||
| y1 | No | ||
| y2 | No | ||
| operation | 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 readOnlyHint=true, lowering the burden; the description adds operation semantics and notes that get_screenshot returns a PNG. However, it does not disclose the coordinate space for zoom_window (model/world vs screen), whether coordinates are effectively required despite schema null defaults, or what happens if coordinates are omitted or passed with other operations.
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 one-line purpose statement followed by three bullet-style operation lines; every sentence earns its place. The purpose is front-loaded, and each operation is a single scannable line with no redundant filler.
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 dispatcher with an unconstrained operation parameter and no enums, the description supplies the critical operation vocabulary that the schema lacks, and the output schema presumably covers return values. However, it leaves notable gaps: coordinate semantics for zoom_window, whether coordinates are required for that operation, and whether the coordinate parameters should be null for the other two operations.
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 partially compensates: 'zoom_window — Zoom to window: x1, y1, x2, y2' maps the four numeric parameters to the operation that consumes them, and the Operations list is the only source of valid values for the unconstrained operation string. But it leaves coordinate ordering, units, and coordinate system unspecified, and does not clarify that x1/y1/x2/y2 are effectively required for zoom_window even though the schema marks them optional with null defaults.
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 a clear purpose ('Viewport control and screenshot capture') and enumerates three distinct operations, each with a specific verb, resource, and effect (zoom_extents, zoom_window, get_screenshot). It is immediately distinguishable from sibling tools like drawing, entity, or layer, which cover different AutoCAD domains.
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 operation list gives implicit selection guidance — zoom_extents for showing everything, zoom_window for a specific region, get_screenshot for capturing PNG output. However, there is no explicit when-to-use wording, no exclusions, and no stated alternative among the sibling tools; the domain separation from drawing/entity/layer/block is only implied by names.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
8 tool updates
v3.0.0- First observed
annotation - First observed
block - First observed
drawing - First observed
entity - First observed
layer - First observed
pid - First observed
system - First observed
view
TDQS
Scored across 8 tools
The eight domain tools (drawing, entity, layer, block, annotation, pid, view, system) are clearly distinct in purpose, minimizing confusion. Minor overlap exists between entity's create_mtext and annotation's create_text for text creation, but descriptions and context generally disambiguate them.
Tool names follow a consistent noun pattern, but operations within tools mix styles: single-word verbs (copy, list, open), snake_case (save_as_dxf, get_variables), and camelCase verb_noun (create_line, set_current). This mixed convention is readable but lacks uniformity across the set.
Eight tools is well-scoped for a CAD server, covering distinct functional domains without redundancy. Each tool contains a comprehensive set of operations, and the count is within the ideal range for agent navigation.
The tool surface is remarkably complete for CAD workflows: drawing lifecycle, entity creation/modification/query, layer management, block definitions and attributes, annotations, P&ID symbols, viewport control, and system operations. No significant gaps are apparent for typical CAD tasks.
Maintenance
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