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ComplianceCow

ComplianceCow MCP Server

execute_task

Execute compliance tasks with real data and automatically resolve dependencies on previous outputs, returning structured results and execution status.

Instructions

Execute a specific task with real data after collecting all required inputs.

**This tool executes tasks with REAL data, not sample data.**
If any input depends on a previous task's output and that output is not available,
the dependent task(s) MUST be executed first to obtain the real output.

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EXECUTION CONTEXT
===============================================================================
- This tool MUST be called after collecting the inputs for a task.
- Execution is sequential: execute Task 1 → then Task 2 → etc.
- No task may proceed until its dependent tasks have been executed.
- On execution failure, provide detailed error feedback.

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DEPENDENCY & REAL DATA HANDLING
===============================================================================
If a task requires input from a previous task (dataset, file, or structured output):

1. **Use real task output when available**
    - If the dependent task was already executed and produced outputs:
        → Use those outputs as the input.
        → Do NOT generate synthetic/sample data.
        → Do NOT re-run the previous task unnecessarily.

2. **If required previous task output does NOT exist**
    - The assistant MUST:
        - Explain *why* execution of the previous task is required.
        - Automatically execute the previous task (and any required tasks in the chain).
        - **After execution, display all execution results and outputs.**
        - NO user confirmation should be requested—only explanation.
        - Use the REAL output from the executed task as input.

3. **If executing a required previous task fails**
    - The assistant MUST:
        - Explain clearly why the task failed.
        - Ask the user to provide the required input data manually.
    - User-provided data becomes the fallback input.

4. **Only execute what is needed**
    - Execute ONLY the minimal set of tasks whose outputs are required.
    - **Every executed task must have its results shown to the user immediately.**

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APPLICATION CONFIGURATION
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Application credentials are REQUIRED if the task's appType is NOT 'nocredapp'.

If the task requires application credentials (appType != 'nocredapp'):
- Application config must be provided with:
    - appName: Application class name
    - appURL: Application URL (optional, can be empty string)
    - credentialType: Type of credentials
    - credentialValues: Actual credential key-value pairs
- OR applicationId if using existing saved application

If the task's appType is 'nocredapp':
- Application configuration can be omitted (pass None or empty)
- The system will automatically use the hardcoded nocredapp application structure:
  {
      "applicationType": "NoCredApp",
      "appURL": "",
      "credentialType": "NoCred",
      "credentialValues": {"Dummy": ""},
      "appTags": {"appType": ["nocredapp"], "environment": ["logical"], "execlevel": ["app"]}
  }

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TASK EXECUTION FLOW
===============================================================================
1. Receive task name and collected inputs
2. Check if any input depends on previous task output
3. For dependency inputs:
    a. Check if previous task output exists
    b. If not, execute previous task first
    c. Use real output as input value
4. Prepare execution payload with real data
5. Call task execution API
6. Parse and return execution results with output file URLs

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REQUEST BODY FORMAT
===============================================================================
    {
        "taskname": "TaskName",
        "application": {
            "appName": "ApplicationClassName",
            "appURL": "https://app.url.com",
            "credentialType": "CredentialTypeName",
            "credentialValues": {
                "key1": "value1",
                "key2": "value2"
            },
            "appTags": [Complete object from of 'appTags' from the task in the rule]
        },
        "taskInputs": {
            "inputs": {
                "InputName1": "value_or_file_url",
                "InputName2": "value_or_file_url"
            }
        }
    }
===============================================================================
Args:
    task_name: Name of the task to execute
    task_inputs: Dictionary containing key-value pairs of task inputs
                Format: {"input_name": "value" or file_url}
    application: Optional application configuration for tasks requiring credentials
                Format: {
                    "appName": "ApplicationClassName",
                    "appURL": "https://...",
                    "credentialType": "...",
                    "credentialValues": {...},
                    "appTags": [Complete object from of 'appTags' from the task in the rule]
                }
                OR {"applicationId": "existing-app-id", "appTags": [Complete object from of 'appTags' from the task in the rule]}

Returns:
    Dict containing:
    {
        "success": bool,
        "execution_status": "COMPLETED" | "FAILED",
        "task_name": str,
        "task_inputs": dict,
        "outputs": dict,  # Output file URLs and values
        "errors": list,
        "message": str,
        "next_action": str
    }

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
task_nameYes
task_inputsYes
applicationNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, description fully discloses sequential dependency resolution, automatic execution of prerequisite tasks, credential requirements by appType, and immediate result display behavior.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

While well-structured with clear headers, the description is overly verbose with redundant information (REQUEST BODY FORMAT duplicates Args section) and excessive ASCII art separators.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Comprehensive coverage of execution context, dependency chains, authentication rules, and output format appropriate for the tool's complexity and nested object schemas.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Compensates for 0% schema description coverage by providing detailed Args section explaining task_inputs format, application object structure, and nested credential requirements.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

States specific action (execute task with real data) and distinguishes from sibling collection/verification tools via 'after collecting all required inputs' and 'real vs sample data' warnings.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly details when to call (after input collection), execution sequencing, dependency prerequisites, and fallback procedures for missing inputs or failures.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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