Merge pull request #6 from deeleeramone/feature/fastmcp-3

refactor for fastmcp3

Author: deeleeramone

.github/workflows/test-pywry.yml

@@ -103,6 +103,12 @@ jobs:
         if: runner.os == 'Linux'
         uses: dtolnay/rust-toolchain@stable
 
+      - name: Set up Rust (Windows ARM64)
+        if: runner.os == 'Windows' && runner.arch == 'ARM64'
+        uses: dtolnay/rust-toolchain@stable
+        with:
+          targets: aarch64-pc-windows-msvc
+
       - name: Install Linux dependencies
         if: runner.os == 'Linux'
         run: |
@@ -114,21 +120,20 @@ jobs:
         uses: actions/cache@v4
         with:
           path: C:/vcpkg/installed
-          key: vcpkg-arm64-windows-openssl-lua-${{ hashFiles('.github/workflows/test-pywry.yml') }}
+          key: vcpkg-arm64-windows-openssl-static-md-lua-${{ hashFiles('.github/workflows/test-pywry.yml') }}
           restore-keys: |
-            vcpkg-arm64-windows-openssl-lua-
+            vcpkg-arm64-windows-openssl-static-md-lua-
 
       - name: Install OpenSSL via vcpkg (Windows ARM)
         if: runner.os == 'Windows' && runner.arch == 'ARM64'
         shell: pwsh
         run: |
           $env:VCPKG_ROOT = "$env:VCPKG_INSTALLATION_ROOT"
-          & "$env:VCPKG_ROOT\vcpkg" install openssl:arm64-windows
-          $opensslDir = "$env:VCPKG_ROOT\installed\arm64-windows"
+          & "$env:VCPKG_ROOT\vcpkg" install openssl:arm64-windows-static-md
+          $opensslDir = "$env:VCPKG_ROOT\installed\arm64-windows-static-md"
           echo "OPENSSL_DIR=$opensslDir" >> $env:GITHUB_ENV
           echo "OPENSSL_LIB_DIR=$opensslDir\lib" >> $env:GITHUB_ENV
           echo "OPENSSL_INCLUDE_DIR=$opensslDir\include" >> $env:GITHUB_ENV
-          echo "$opensslDir\bin" >> $env:GITHUB_PATH
 
       - name: Setup build environment (Windows ARM)
         if: runner.os == 'Windows' && runner.arch == 'ARM64'
@@ -145,6 +150,13 @@ jobs:
           echo "LUA_LIB=$luaDir\lib" >> $env:GITHUB_ENV
           echo "LUA_INC=$luaDir\include" >> $env:GITHUB_ENV
 
+      - name: Build cryptography from source (Windows ARM)
+        if: runner.os == 'Windows' && runner.arch == 'ARM64'
+        shell: pwsh
+        run: |
+          pip install --no-cache-dir --no-binary=cryptography cryptography
+          python -c "from cryptography.hazmat.bindings._rust import openssl; print('cryptography OK')"
+
       - name: Install dependencies
         run: |
           python -m pip install --upgrade pip
@@ -178,6 +190,7 @@ jobs:
         env:
           PYWRY_HEADLESS: "1"
           PYWRY_DEPLOY__STATE_BACKEND: "memory"
+          PYTHONUTF8: "1"
         run: |
           python -m pytest -c pytest.ini tests/ -v --tb=short --ignore=tests/test_state_redis_integration.py --ignore=tests/test_auth_rbac_integration.py --ignore=tests/test_deploy_mode_integration.py --ignore=tests/test_e2e_deploy_mode.py --ignore=tests/test_e2e_rbac_widgets.py -m "not redis and not container"
 
@@ -186,6 +199,7 @@ jobs:
         env:
           PYWRY_HEADLESS: "1"
           PYWRY_DEPLOY__STATE_BACKEND: "memory"
+          PYTHONUTF8: "1"
         run: |
           python -m pytest -c pytest.ini tests/ -v --tb=short --ignore=tests/test_state_redis_integration.py --ignore=tests/test_auth_rbac_integration.py --ignore=tests/test_deploy_mode_integration.py --ignore=tests/test_e2e_deploy_mode.py --ignore=tests/test_e2e_rbac_widgets.py --ignore=tests/test_inline_ssl.py -m "not redis and not container"
 

pywry/docs/docs/guides/tauri-plugins.md

@@ -11,11 +11,16 @@ sequenceDiagram
     participant Sub as __main__.py (subprocess)
     participant Tauri as Tauri Engine
 
-    App->>RT: set_tauri_plugins(["dialog", "fs", "notification"])
-    RT->>Sub: env PYWRY_TAURI_PLUGINS="dialog,fs,notification"
-    Sub->>Sub: _load_plugins() — check flags, import modules
-    Sub->>Tauri: builder.build(plugins=[dialog.init(), fs.init(), notification.init()])
-    Tauri->>Tauri: Register plugins + grant capabilities
+    App->>RT: set_tauri_plugins([...])
+    Note over App,RT: ["dialog", "fs", "notification"]
+    RT->>Sub: PYWRY_TAURI_PLUGINS env var
+    Note over RT,Sub: "dialog,fs,notification"
+    Sub->>Sub: _load_plugins()
+    Note right of Sub: Check flags & import modules
+    Sub->>Tauri: builder.build(plugins=[...])
+    Note over Sub,Tauri: dialog.init(), fs.init(), notification.init()
+    Tauri->>Tauri: Register plugins
+    Note right of Tauri: Grant capabilities
 ```
 
 1. You list the plugins you want in config (Python, TOML, or env var).

pywry/docs/docs/mcp/examples.md

@@ -417,6 +417,86 @@ Toolbar events that don't have a `callbacks` entry are still captured and queued
 
 ---
 
+## Autonomous Building
+
+The agentic tools use LLM sampling to generate complete widget apps from plain-English descriptions.
+They require a sampling-capable client (Claude, etc.) and produce structured `WidgetPlan` output.
+
+### Quick build — one call
+
+**Prompt:** *Build a task tracker with add/remove buttons and a progress bar.*
+
+```json title="build_app"
+{
+  "description": "A task tracker with add/remove buttons and a progress bar",
+  "open_window": false
+}
+```
+
+Returns a `widget_id`, complete `python_code`, and a `files` map ready to save to disk.
+
+### Plan first, then inspect
+
+**Step 1 — plan:**
+
+```json title="plan_widget"
+{
+  "description": "A crypto price dashboard showing BTC, ETH, and SOL with 24h change indicators"
+}
+```
+
+Returns a `WidgetPlan` JSON the agent can inspect or modify before building.
+
+**Step 2 — build from the plan:**
+
+```json title="build_app"
+{
+  "description": "A crypto price dashboard showing BTC, ETH, and SOL with 24h change indicators"
+}
+```
+
+### Export an existing widget as a project
+
+After working interactively with a widget:
+
+```json title="export_project"
+{
+  "widget_ids": ["w-abc123"],
+  "project_name": "my_dashboard",
+  "output_dir": "/Users/me/projects/my_dashboard"
+}
+```
+
+Writes `main.py`, `requirements.txt`, `README.md`, and `widgets/w_abc123.py` to disk.
+
+### Interactive scaffolding
+
+Use `scaffold_app` to guide the user through requirements step-by-step via prompts:
+
+```
+scaffold_app()
+```
+
+The agent will elicit:
+
+1. App title
+2. Description
+3. Display mode (native / inline)
+4. Libraries needed (Plotly, AG-Grid, neither)
+5. Toolbar placement
+
+Then automatically delegates to `plan_widget` with the gathered requirements.
+
+!!! tip "Which tool to use"
+    | Situation | Tool |
+    |:---|:---|
+    | Clear description in hand | `build_app` |
+    | Want to inspect the plan first | `plan_widget` → `build_app` |
+    | Want to save to disk | `export_project` |
+    | User isn't sure what they want | `scaffold_app` |
+
+---
+
 ## Tips for Effective Prompts
 
 | Do | Don't |

pywry/docs/docs/mcp/index.md

@@ -74,9 +74,9 @@ The MCP server bridges AI agents and PyWry's rendering engine:
 ```mermaid
 flowchart LR
     A["AI Agent<br>(Claude, etc.)"] <-->|"MCP Protocol<br>(stdio / SSE)"| B["PyWry MCP Server"]
-    B --> C["Tools<br>25 widget operations"]
+    B --> C["Tools<br>29 widget operations"]
     B --> D["Resources<br>Docs, source, exports"]
-    B --> E["Skills<br>11 guidance prompts"]
+    B --> E["Skills<br>12 guidance prompts"]
     C --> F["PyWry Widgets<br>(native or browser)"]
     D --> B
     E --> B
@@ -104,9 +104,9 @@ In headless mode, `list_widgets` returns URLs like `http://127.0.0.1:PORT/widget
 
 The MCP server exposes three types of capabilities:
 
-### Tools (25)
+### Tools (29)
 
-Operations the agent can call — creating widgets, updating content, managing state. Organized into five groups:
+Operations the agent can call — creating widgets, updating content, managing state. Organized into six groups:
 
 | Group | Tools | Purpose |
 |:---|:---|:---|
@@ -115,6 +115,7 @@ Operations the agent can call — creating widgets, updating content, managing s
 | **Widget manipulation** | `set_content`, `set_style`, `show_toast`, `update_theme`, `inject_css`, `remove_css`, `navigate`, `download`, `update_plotly`, `update_marquee`, `update_ticker_item`, `send_event` | Modify existing widgets |
 | **Widget management** | `list_widgets`, `get_events`, `destroy_widget` | Track and clean up widgets |
 | **Resources & export** | `get_component_docs`, `get_component_source`, `export_widget`, `list_resources` | Documentation and code generation |
+| **Autonomous building** | `plan_widget`, `build_app`, `export_project`, `scaffold_app` | LLM-powered end-to-end app creation |
 
 ### Resources
 
@@ -132,7 +133,7 @@ Read-only data the agent can access via `pywry://` URIs:
 
 ### Prompts (Skills)
 
-11 guidance prompts that teach the agent how to use PyWry effectively:
+12 guidance prompts that teach the agent how to use PyWry effectively:
 
 | Skill | What it teaches |
 |:---|:---|
@@ -147,6 +148,7 @@ Read-only data the agent can access via `pywry://` URIs:
 | `data_visualization` | Plotly charts, AG Grid tables, live data patterns |
 | `forms_and_inputs` | Form building with validation and event collection |
 | `modals` | Modal dialogs — schema, sizes, open/close/reset |
+| `autonomous_building` | End-to-end app generation with `plan_widget`, `build_app`, `export_project`, `scaffold_app` |
 
 ## Next Steps
 

pywry/docs/docs/mcp/setup.md

@@ -235,8 +235,9 @@ All MCP settings can be configured via environment variables (`PYWRY_MCP__` pref
 | `log_tools` | `PYWRY_MCP__LOG_TOOLS` | `false` | Log tool calls |
 | `log_level` | `PYWRY_MCP__LOG_LEVEL` | `INFO` | Log level |
 | `skills_auto_load` | `PYWRY_MCP__SKILLS_AUTO_LOAD` | `true` | Auto-load skills on connect |
-| `include_tags` | `PYWRY_MCP__INCLUDE_TAGS` | `[]` | Only expose tools with these tags |
-| `exclude_tags` | `PYWRY_MCP__EXCLUDE_TAGS` | `[]` | Exclude tools with these tags |
+
+!!! note "Removed in FastMCP V3"
+    The `include_tags` and `exclude_tags` settings were removed when PyWry migrated to FastMCP V3. Tool filtering is no longer supported via config.
 
 ### Config File
 
@@ -269,12 +270,37 @@ debug = true
 }
 ```
 
+## Installing Skills
+
+PyWry ships bundled skills that can be copied into your MCP client's skills directory:
+
+```bash
+# Preview what will be installed (dry run)
+pywry install-skills --dry-run
+
+# Install to all detected MCP vendor directories
+pywry install-skills
+
+# Install to a specific directory
+pywry install-skills --target custom --custom-dir /path/to/skills
+
+# Overwrite existing skill files
+pywry install-skills --overwrite
+
+# Or via the module
+python -m pywry.mcp install-skills --dry-run
+```
+
+The command copies skill markdown files from PyWry's bundled `pywry/mcp/skills/` directory into the appropriate vendor location for your MCP client (Claude Desktop, Cursor, etc.).
+
+---
+
 ## Verifying the Setup
 
 1. Restart Claude Desktop after editing the config
 2. Open a new conversation
 3. Ask: *"What PyWry tools do you have available?"*
-4. Claude should list 25 tools
+5. Claude should list 29 tools
 
 You can also test manually:
 

pywry/docs/docs/mcp/skills.md

@@ -30,6 +30,7 @@ Skills are **lazy-loaded** from markdown files on disk and cached in memory (LRU
 | `data_visualization` | Medium | Plotly charts, AG Grid tables, Marquee tickers, live data polling and event-driven update patterns |
 | `forms_and_inputs` | Medium | Form building with TextInput, Select, Toggle, etc. — validation patterns, event-based data collection |
 | `modals` | Medium | Modal dialog schemas — sizes, nested components, open/close events, JS API, `reset_on_close` behavior |
+| `autonomous_building` | Medium | End-to-end app generation — `plan_widget`, `build_app`, `export_project`, `scaffold_app` workflows and chaining patterns |
 
 ### The Component Reference
 
@@ -146,13 +147,14 @@ Most of these have dedicated tools (e.g., `set_content`, `inject_css`). The `sen
 
 A typical interaction pattern:
 
-1. **Agent list tools** → sees 25 available tools
+1. **Agent list tools** → sees 29 available tools
 2. **Agent calls `get_skills()`** → receives the skill list with descriptions
 3. **Agent calls `get_skills(skill="component_reference")`** → loads the mandatory component docs
 4. **Agent calls `create_widget`** → builds widget JSON using component reference as guide
 5. **Agent calls `get_events`** → reads user interactions
 6. **Agent calls `set_content` / `set_style`** → updates the widget in response
 7. **Agent calls `export_widget`** → generates Python code the user can save
+8. **Agent calls `build_app`** *(optional)* → fully autonomous end-to-end build from a description
 
 The skills ensure the agent knows the exact JSON structure, event naming conventions, and available properties before making tool calls.