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CSOAI-ORG

MEOK DORA Tlpt Planner MCP

remediation_milestones

Generate a remediation milestone plan for TLPT findings, assigning closure timelines (90/180/365 days) by severity level.

Instructions

Generate a 90/180/365-day remediation milestone plan based on TLPT findings.

Args: findings_count: Total number of findings from the red-team report. severity_distribution: Dict with keys 'critical', 'high', 'medium', 'low' and counts.

Returns: Milestone plan with required closure timelines per severity.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
findings_countYes
severity_distributionYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • The handler/implementation of the 'remediation_milestones' tool. It takes findings_count and severity_distribution, validates the total, and returns a 30/90/180/365-day remediation milestone plan per DORA Art. 26(7).
    @mcp.tool()
    def remediation_milestones(findings_count: int, severity_distribution: dict[str, int]) -> dict[str, Any]:
        """Generate a 90/180/365-day remediation milestone plan based on TLPT findings.
    
        Args:
            findings_count: Total number of findings from the red-team report.
            severity_distribution: Dict with keys 'critical', 'high', 'medium', 'low' and counts.
    
        Returns:
            Milestone plan with required closure timelines per severity.
        """
        sev = {**{"critical": 0, "high": 0, "medium": 0, "low": 0}, **severity_distribution}
        total = sev["critical"] + sev["high"] + sev["medium"] + sev["low"]
        if total != findings_count:
            return {
                "error": f"severity_distribution sum ({total}) != findings_count ({findings_count})",
            }
    
        return {
            "milestones": {
                "30_days": {
                    "scope": f"All {sev['critical']} CRITICAL findings closed or compensating controls deployed",
                    "evidence_required": "Per-finding remediation ticket with code-change PR or config-change record",
                },
                "90_days": {
                    "scope": f"All {sev['high']} HIGH findings closed; {sev['medium'] // 2} MEDIUM remediation in flight",
                    "evidence_required": "Purple-team replay test confirms CRITICAL+HIGH closures",
                },
                "180_days": {
                    "scope": f"All {sev['medium']} MEDIUM findings closed; {sev['low'] // 2} LOW remediation in flight",
                    "evidence_required": "Updated control framework reflecting structural fixes",
                },
                "365_days": {
                    "scope": f"All {sev['low']} LOW findings closed or formally accepted with risk-owner sign-off",
                    "evidence_required": "Final remediation pack signed by CRO + CISO; ready for next-cycle TLPT",
                },
            },
            "total_findings": findings_count,
            "severity_distribution": sev,
            "regulatory_basis": "DORA Art. 26(7) — remediation closure required before next TLPT cycle",
        }
  • The tool registration via @mcp.tool() decorator on FastMCP instance 'mcp' (defined at line 22). This registers 'remediation_milestones' as an MCP tool.
    @mcp.tool()
  • Implicit schema via type hints: 'findings_count: int' and 'severity_distribution: dict[str, int]' as input parameters; returns 'dict[str, Any]'.
    def remediation_milestones(findings_count: int, severity_distribution: dict[str, int]) -> dict[str, Any]:
Behavior3/5

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

No annotations are present, so the description must disclose behavioral traits. It explains the output (a milestone plan with closure timelines) but does not mention side effects, authentication needs, or read-only nature. It is adequate but not fully transparent.

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?

The description is well-structured with a clear purpose line followed by Args and Returns sections. It is efficient and front-loaded, though could be slightly more concise.

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 simplicity (2 parameters, output schema exists), the description covers inputs and output sufficiently. It provides a complete picture for usage without needing additional context.

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?

Despite 0% schema description coverage, the description adds significant meaning by explaining 'findings_count' as total findings and 'severity_distribution' as a dict with keys like critical, high, etc. This compensates for the schema gap.

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's function: 'Generate a 90/180/365-day remediation milestone plan based on TLPT findings.' It uses a specific verb ('Generate') and specifies the resource and context, distinguishing it from siblings like list_phases or pricing.

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

Usage Guidelines3/5

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

The description implies usage for generating plans from TLPT findings but does not explicitly state when to use this tool over alternatives, nor does it provide exclusionary guidance. Adequate but lacks comparative context.

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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