Skip to main content
Glama
ComplianceCow

ComplianceCow MCP Server

confirm_template_input

Confirm and process user-validated template input by uploading files or storing content in memory, then automatically update the associated rule.

Instructions

Confirm and process template input after user validation.

CONFIRMATION PROCESSING (Enhanced with Automatic Rule Updates):

  • Handles final confirmation of template content

  • Uploads files for FILE dataType inputs

  • Stores content in memory for non-FILE inputs

  • MANDATORY step before proceeding to next input

  • NEW: Automatically updates the rule with new input after processing

  • Skips confirmation if the user accepts the suggested template

PROCESSING RULES (Enhanced):

  • FILE dataType: Upload content as file, return file URL

  • HTTP_CONFIG dataType: Upload content as file, return file URL

  • Non-FILE dataType: Store content in memory

  • Include metadata about confirmation and timestamp

  • NEW: Automatic rule update with new input data

AUTOMATIC RULE UPDATE PROCESS: After successful input processing, this tool automatically:

  1. Fetches the current rule structure

  2. Adds the new input to spec.inputs

  3. Updates spec.inputsMeta__ with input metadata

  4. Calls create_rule() to save the updated rule

  5. Rule status will be auto-detected (DRAFT → collecting_inputs → READY_FOR_CREATION)

UI DISPLAY REQUIREMENT:

  • The file URL must ALWAYS be displayed to the user in the UI, allowing the user to view or download the file directly.

Args: rule_name: Descriptive name for the rule based on the user's use case. Note: Use the same rule name for all inputs that belong to this rule. Example: rule_name = "MeaningfulRuleName" task_name: Name of the task this input belongs to input_name: Name of the input parameter rule_input_name: Must be one of the values defined in the rule structure's inputs confirmed_content: The content user confirmed

Returns: Dict containing processing results (file URL or memory reference) and rule update status

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
rule_nameYes
task_nameYes
rule_input_nameYes
input_nameYes
confirmed_contentYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior4/5

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

No annotations are provided, so the description carries full burden. It comprehensively discloses the processing flow (file upload for FILE/HTTP_CONFIG, memory storage for others), automatic rule updates (including the step-by-step process), and UI display requirement. It does not mention error handling or permissions, but the detail is sufficient for transparency.

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?

The description is overly verbose with multiple sections in ALL CAPS, bullet points, and repetition (e.g., 'NEW' and 'Enhanced' markers). While it contains necessary information, the structure is not concise and could be streamlined to improve readability.

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 tool's complexity (5 parameters, no enums, output schema present), the description covers the processing steps, side effects (automatic rule updates), and UI requirement. It mentions the return type as a dict with file URL/memory reference and rule update status. It lacks error handling details or prerequisites, but is otherwise complete for effective use.

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 for its 5 parameters. The description's 'Args' section adds valuable context: it specifies that rule_name should be consistent across inputs, task_name identifies the task, rule_input_name must match rule structure, confirmed_content is the validated content. This compensates for the schema's lack of descriptions, though not all parameters are equally detailed.

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 starts with 'Confirm and process template input after user validation.' It clearly states the verb (confirm/process) and the resource (template input). It distinguishes from siblings like confirm_parameter_input by specifying it's for template inputs, and it details sub-actions like file uploads and memory storage.

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 explicitly states it is a 'MANDATORY step before proceeding to next input' and mentions it skips confirmation if the user accepts the suggested template. This provides clear context on when to use. However, it does not explicitly compare to sibling tools like collect_template_input or confirm_parameter_input, nor does it specify when not to use.

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

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/ComplianceCow/cow-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server