Skip to main content
Glama
ComplianceCow

ComplianceCow MCP Server

create_rule_readme

Create and save a README.md file for a compliance rule after user confirms the previewed content.

Instructions

Create and save README.md file after user confirmation.

README CREATION:

This tool actually creates and saves the README.md file after the user has reviewed and confirmed the preview content from generate_rule_readme_preview().

WORKFLOW:

  1. User has already reviewed README content from preview

  2. User confirmed the content is acceptable

  3. This tool receives the complete README.md content as string

  4. MCP saves the README file and returns access details

Args: rule_name: Name of the rule for which to create README readme_content: Complete README.md content as string

Returns: Dict containing README creation status and access details

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
rule_nameYes
readme_contentYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior3/5

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

With no annotations, the description carries full burden. It explains the tool saves the file and returns access details, but does not address potential overwrites, permissions, or side effects. The behavior is adequately disclosed but lacks deeper safety context.

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

Conciseness5/5

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

The description is well-structured with sections (README CREATION, WORKFLOW, Args, Returns). It is front-loaded with purpose, each sentence adds value, and there is no extraneous information.

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?

For a creation tool with 2 string parameters and an output schema, the description covers the workflow and return details. It lacks error scenarios (e.g., invalid content), but is largely sufficient for an AI agent to understand the tool's role.

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?

Input schema has 0% description coverage. The description adds 'Name of the rule' and 'Complete README.md content as string', which clarifies the parameters beyond the schema. This compensates well, though more detail (e.g., format constraints) would improve.

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 'Create and save README.md file after user confirmation.' It explicitly differentiates from the sibling tool generate_rule_readme_preview and update_rule_readme by specifying it is the final step after preview and confirmation.

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 a clear workflow: user must have reviewed preview and confirmed. It implies usage only after preview, but does not explicitly state when not to use or list alternatives. The context is clear and actionable.

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