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assiststudio-runner

assiststudio-runner

Local
github.com/fieldcureAI & Agents1Updated 22w ago

About assiststudio-runner

assiststudio-runner is a Windows-only engine for scheduling and executing natural-language LLM tasks, aimed at AssistStudio users and MCP clients. It combines task management tools with Windows Task Scheduler execution and configurable result delivery.

Key features

  1. 01Manage tasks through seven MCP tools
  2. 02Schedule recurring and one-time runs
  3. 03Execute tasks headlessly via CLI
  4. 04Support multiple LLM providers
  5. 05Deliver results through Outbox channels
  6. 06Store credentials in Windows Credential Manager

Use cases

  1. 01Schedule weekday research summaries
  2. 02Run a task immediately for testing
  3. 03Track execution status and history

MCP support

It runs as an MCP server over stdio and exposes tools to create, update, delete, list, and execute tasks, and inspect their status and history. Tasks can also start configured MCP servers.

Configuration

Local server config is the same for every client. Copy it into your client’s MCP config file.

No install method listed yet

We haven’t collected a package or remote endpoint for this server. Follow the instructions in the project README.

Repository
Packages
nugetFieldCure.AssistStudio.Runner2.0.4

Tools

No tool list yet

Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.

Details

From the project README

AssistStudio Runner

NuGet License: MIT

A Windows-only headless LLM task automation engine that executes natural language tasks on schedule and delivers results through configured channels. Built as a Model Context Protocol (MCP) server with the official MCP C# SDK.

Features

  • Dual-mode operation — MCP server (serve) for task management, headless CLI (exec) for scheduled execution
  • 7 MCP tools — create_task, update_task, delete_task, list_tasks, run_task, get_task_history, get_execution_status
  • Windows Task Scheduler integration — cron expressions automatically mapped to schtasks entries
  • Shared AgentLoop — LLM execution powered by Ai.Execution (same loop used by SubAgentExecutor)
  • Multi-provider LLM support — Claude, OpenAI, Gemini, Ollama, Groq via Ai.Providers
  • MCP server orchestration — tasks can bootstrap any MCP servers (Outbox, RAG, Filesystem, custom)
  • Flexible tool control — AllowedTools null = all tools permitted; explicit list for fine-grained control; empty list = safe tools only
  • Secure credentials — API keys in Windows Credential Manager (DPAPI), shared with AssistStudio
  • Execution logging — DB summary + detailed JSON logs with full conversation history
  • One-time scheduling — schedule_once with ISO 8601 datetime for single-execution tasks (“in 5 minutes”, “tomorrow at 9am”)
  • Result delivery — send results via Outbox channels (Slack, Telegram, Email, KakaoTalk, Discord)

Installation

dotnet tool (recommended)
dotnet tool install -g FieldCure.AssistStudio.Runner

After installation, the assiststudio-runner command is available globally. The published package is Windows-only because scheduling is implemented via Windows Task Scheduler and credentials are stored in Windows Credential Manager.

From source
git clone https://github.com/fieldcure/fieldcure-assiststudio-runner.git
cd fieldcure-assiststudio-runner
dotnet build

Requirements

  • .NET 8.0 Runtime or later
  • Windows (required for Task Scheduler and Credential Manager)

Configuration

Auto-configuration (recommended)

When launched in serve mode with no runner.json, Runner automatically scans Windows Credential Manager for known provider API keys and generates the config file. If you use AssistStudio, API keys are already stored — no manual setup needed.

Manual Setup
# Create runner.json config template
assiststudio-runner config init

# Set API key for a provider model
assiststudio-runner config set-credential "Claude" sk-ant-api03-...

# Verify (displays masked value)
assiststudio-runner config get-credential "Claude"

The config file is created at %LOCALAPPDATA%/FieldCure/AssistStudio/Runner/runner.json:

{
  "defaultModelName": "Claude",
  "models": {
    "Claude": {
      "providerType": "Claude",
      "modelId": "claude-sonnet-4-20250514"
    }
  },
  "fallbackChannel": "runner-alerts",
  "logRetentionDays": 30
}
Claude Desktop

Add to claude_desktop_config.json:

{
  "mcpServers": {
    "runner": {
      "command": "assiststudio-runner",
      "args": ["serve"]
    }
  }
}
VS Code (Copilot)

Add to .vscode/mcp.json:

{
  "servers": {
    "runner": {
      "command": "assiststudio-runner",
      "args": ["serve"]
    }
  }
}
From source (without dotnet tool)
{
  "mcpServers": {
    "runner": {
      "command": "dotnet",
      "args": [
        "run",
        "--project", "C:\\path\\to\\fieldcure-assiststudio-runner\\src\\FieldCure.AssistStudio.Runner",
        "--", "serve"
      ]
    }
  }
}

Tools

ToolDescriptionConfirmation
create_taskCreate a task with prompt, schedule, and MCP server configRequired
update_taskModify task fields — partial update, only changed fieldsRequired
delete_taskDelete a task, its executions, and log filesRequired
list_tasksList tasks with filtering and last execution status—
run_taskStart execution (async default, optional 60s wait)Required
get_task_historyQuery execution history with status filtering—
get_execution_statusCheck real-time status of an execution—

Usage

Conversation Example
User: "Summarize competitor news every morning at 9 AM and send it to Slack"
  LLM → create_task (schedule: "0 9 * * 1-5", mcp_servers: [outbox, rag])

User: "Run a test"
  LLM → run_task (wait: true) → reports result

User: "Exclude weekends"
  LLM → update_task (schedule: "0 9 * * 1-5")

User: "What were yesterday's results?"
  LLM → get_task_history (limit: 1)
Execution Modes
ModeCommandPurpose
Serveassiststudio-runner serveMCP server (stdio) for task management
Execassiststudio-runner exec <task-id>Headless execution (called by schtasks)
Configassiststudio-runner config initCreate config template
assiststudio-runner config set-credential <key> <value>Store API key or env var
assiststudio-runner config get-credential <key>Retrieve credential (masked)
Exit Codes (exec mode)
CodeMeaning
0Succeeded
1Failed
2Timed out
3Task not found
4Already running

Scheduling

Cron expressions are automatically mapped to Windows Task Scheduler entries:

ScheduleParameterschtasks
Once at specific timeschedule_once: "2026-04-07T15:30:00+09:00"/SC ONCE /SD 2026/04/07 /ST 15:30
Every 30 minutesschedule: "*/30 * * * *"/SC MINUTE /MO 30
Every 2 hoursschedule: "0 */2 * * *"/SC HOURLY /MO 2
Daily at 9:00 AMschedule: "0 9 * * *"/SC DAILY /ST 09:00
Weekdays at 9:00 AMschedule: "0 9 * * 1-5"/SC WEEKLY /D MON,TUE,WED,THU,FRI /ST 09:00
Monthly on the 1stschedule: "0 9 1 * *"/SC MONTHLY /D 1 /ST 09:00

Scheduled tasks are created with schtasks /IT, so they run in the interactive user context and require the user to be logged in at trigger time.

The schtasks command line uses dnx (NuGet's npx-equivalent, .NET 10+) to fetch and run the worker — dnx FieldCure.AssistStudio.Runner@<major>.* --yes exec <id>. Stateless MCP servers consumed by the worker (Essentials, Outbox) are spawned the same way, pinned at their current major range. Set RunnerConfig.ToolPath to override with a concrete executable when an offline-from-NuGet workflow is required.

Data Storage

DataLocation
Configuration%LOCALAPPDATA%/FieldCure/AssistStudio/Runner/runner.json
Task database%LOCALAPPDATA%/FieldCure/AssistStudio/Runner/runner.db (SQLite, WAL)
Execution logs%LOCALAPPDATA%/FieldCure/AssistStudio/Runner/logs/{id}.json
API keysWindows Credential Manager (FieldCure.AssistStudio)

Project Structure

src/FieldCure.AssistStudio.Runner/
├── Program.cs                    # Dual-mode entry point (serve/exec/config)
├── Models/                       # RunnerTask, TaskExecution, RunnerConfig, ExecutionLog
├── Storage/TaskStore.cs          # SQLite storage with WAL mode
├── Credentials/                  # ICredentialService + Windows PasswordVault
├── Scheduling/                   # CronToSchtasks parser, SchedulerService (schtasks)
├── Execution/                    # TaskExecutor (AgentLoop-based), McpServerPool
├── Tools/                        # 7 MCP tools for serve mode
└── Configuration/ConfigRunner.cs # CLI config subcommands

Development

# Build
dotnet build

# Test
dotnet test

# Pack
dotnet pack -c Release

Designed for AssistStudio

Runner is purpose-built for the AssistStudio ecosystem. Tasks are typically created through natural language conversations in AssistStudio, which handles workflow design, MCP server selection, and tool permissions automatically.

While Runner can be used standalone with Claude Desktop or VS Code (via serve mode), you'll need to construct task parameters (prompt, MCP servers, allowed tools) manually. For the full experience, use AssistStudio.

See Also

Part of the AssistStudio ecosystem.

License

MIT

Comments

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New to MCP?

From what the protocol is to installing your first server, these answers cover most first questions.

An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.