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ComplianceCow

ComplianceCow MCP Server

collect_parameter_input

Collects and validates primitive parameter inputs with user confirmation, supporting optional defaults and required fields.

Instructions

Collect user input for non-template parameter inputs.

PARAMETER INPUT PROCESSING:

  • Collects primitive data type values (STRING, INT, FLOAT, BOOLEAN, DATE, DATETIME)

  • Stores values in memory (NEVER uploads files for primitive types)

  • Handles optional vs required inputs based on 'required' attribute

  • Supports default value confirmation workflow

  • Validates data types and formats

  • MANDATORY: Gets final confirmation for EVERY input before proceeding

INPUT REQUIREMENT RULES:

  • MANDATORY: Only if input.required = true

  • OPTIONAL: If input.required = false, user can skip or provide value

  • DEFAULT VALUES: If user requests defaults, must get confirmation

  • FINAL CONFIRMATION: Always required before proceeding to next input

DEFAULT VALUE WORKFLOW:

  1. User requests to use default values

  2. Show default value to user for confirmation

  3. "I can fill this with the default value: '[default_value]'. Confirm?"

  4. Only proceed after explicit user confirmation

  5. Store confirmed default value in memory

FINAL CONFIRMATION WORKFLOW (MANDATORY):

  1. After user provides value (or confirms default)

  2. Show final confirmation: "You entered: '[value]'. Is this correct? (yes/no)"

  3. If 'yes': Store value and proceed to next input

  4. If 'no': Allow user to re-enter value

  5. NEVER proceed without final confirmation

DATA TYPE VALIDATION:

  • STRING: Any text value

  • INT: Integer numbers only

  • FLOAT: Decimal numbers

  • BOOLEAN: true/false, yes/no, 1/0

  • DATE: YYYY-MM-DD format

  • DATETIME: ISO 8601 format

COLLECTION PRESENTATION: "Now configuring: [X of Y inputs]

Task: {task_name} Input: {input_name} ({data_type}) Description: {description} Required: {Yes/No} Default: {default_value or 'None'}

Please provide a value, type 'default' to use default, or 'skip' if optional:"

CRITICAL RULES:

  • NEVER upload files for primitive data types

  • Store all primitive values in memory only

  • Always confirm default values with user

  • ALWAYS get final confirmation before proceeding to next input

  • Respect required vs optional based on input.required attribute

  • Validate data types before storing

Args: task_name: Name of the task this input belongs to input_name: Name of the input parameter user_value: Value provided by user (optional) use_default: Whether to use default value (requires confirmation)

Returns: Dict containing parameter value and storage info

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
task_nameYes
input_nameYes
user_valueNo
use_defaultNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior5/5

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

No annotations are provided, so the description carries full burden. It extensively discloses behaviors: collects only primitive types, stores in memory, never uploads files, validates types, requires mandatory confirmation, and details the default value workflow. This is comprehensive and transparent.

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

Conciseness2/5

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

The description is excessively long with repeated information, multiple sections, bullet points, and numbered lists. While structured, it is not concise; many details like the final confirmation workflow are reiterated. Every sentence does not earn its place, reducing efficiency for an AI agent.

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

Completeness4/5

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

Given the complexity and lack of annotations, the description covers purpose, parameters, workflows, validation rules, and critical rules comprehensively. It also mentions return type. However, the verbosity slightly hinders quick understanding, but overall completeness is high.

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

Parameters4/5

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

The input schema has 0% description coverage, so the description must compensate. It lists the four parameters (task_name, input_name, user_value, use_default) in an 'Args' section with brief descriptions, adding meaning beyond the schema. However, the descriptions are somewhat terse and could provide more detail on formats or constraints.

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

Purpose5/5

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

The description clearly states the tool collects user input for non-template parameter inputs, specifies it handles primitive types, and distinguishes from sibling tool 'collect_template_input' by explicitly mentioning 'non-template'.

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

Usage Guidelines4/5

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

The description provides clear context for when to use (for parameter inputs) and includes critical rules, but does not explicitly list alternatives or when not to use, though the distinction from template inputs is implied.

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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