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ComplianceCow

ComplianceCow MCP Server

create_design_notes

Create and save design notes for compliance rules after user confirmation, storing the complete Jupyter notebook structure to document governance controls and maintain audit trails.

Instructions

Create and save design notes after user confirmation.

DESIGN NOTES CREATION:

This tool actually creates and saves the design notes after the user has reviewed and confirmed the preview structure from generate_design_notes_preview().

WORKFLOW:

  1. Before creating new design notes, call fetch_rule_design_notes() to check if already exist and continue the flow, if not then continue this flow

  2. User has already reviewed notebook structure from preview

  3. User confirmed the structure is acceptable

  4. This tool receives the complete design notes dictionary structure

  5. MCP saves the notebook and returns access details

Args: rule_name: Name of the rule for which to create design notes design_notes_structure: Complete Jupyter notebook structure as dictionary

Returns: Dict containing design notes creation status and access details

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
rule_nameYes
design_notes_structureYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior4/5

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

No annotations provided; description carries full burden by disclosing it persists data (saves notebook), requires confirmed input, and returns access details, though lacks error condition specifics.

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

Conciseness4/5

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

Well-structured with clear headers; WORKFLOW section is valuable, though 'DESIGN NOTES CREATION:' header is slightly redundant with opening sentence.

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?

Adequately covers the two-step creation flow (preview→create) and mentions Jupyter notebook type; output schema exists so minimal return description is acceptable.

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?

Schema has 0% description coverage; Args section compensates perfectly by explaining rule_name context and clarifying design_notes_structure contains 'Complete Jupyter notebook structure'.

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?

Explicitly states 'Create and save design notes' and clearly distinguishes from sibling tools by referencing generate_design_notes_preview() and fetch_rule_design_notes().

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?

Provides explicit numbered WORKFLOW with preconditions (check existence first, user confirmation required) and sequential relationship to preview tool.

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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