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by cloudsealed

cloudsealed_score_architecture_risk

Read-onlyIdempotent

Score your declared system inventory for single-point-of-failure, excessive coupling, and scalability-gap risks, using explicit weighted rules with auditable breakdowns.

Instructions

Score architecture risk from a declared system inventory.

Scores single-point-of-failure, excessive-coupling, and scalability-gap risk (0-100 each) for every declared system using explicit, weighted rules — not a trained model. Every score ships with a rule-by-rule breakdown so the reasoning is auditable, not a black box. Calls the Predictive-ML-Core production HTTP service (or a self-hosted instance if PREDICTIVE_ML_CORE_URL is set).

Args: params (ScoreArchitectureRiskInput): Validated input containing: - company_name (str): Name of the company/project. - systems (list[SystemInput]): Declared inventory — each with name, type (APPLICATION|DATABASE|API|THIRD_PARTY_SERVICE), criticality (LOW|MEDIUM|HIGH|CRITICAL), public_facing, and optional data_sensitivity/auth_method. - historical_metrics (Optional[HistoricalMetricsInput]): Observed latency/throughput, improves the scalability-gap score. - response_format (ResponseFormat): 'markdown' (default) or 'json'.

Returns: str: Markdown report, or a JSON object with this schema: { "predictions": [ {"systemName": str, "riskScores": {"singlePointOfFailure": int, "excessiveCoupling": int, "scalabilityGap": int}, "scoreBreakdown": {...rule-by-rule points and rationale...}, "findings": [{"title": str, "severity": str, "description": str, "remediation": str}], "recommendations": [{"title": str, "description": str, "effort": str}]} ], "architectureSummary": str, "overallArchitectureScore": int }

Error response: "Error: <message>" if the service is unreachable or
rejects the request (e.g. empty systems list).

Examples: - Use when: "Is our checkout service a single point of failure?" -> declare it with criticality=CRITICAL, type=API. - Use when: "Which of these services should we harden first?" -> declare the whole inventory and compare riskScores. - Don't use when: you need cost/billing analysis — use cloudsealed_analyze_billing_waste.

Error Handling: - Returns "Error: Request timed out..." if the service doesn't respond within 30s. - Returns "Error: ..." with the upstream message on 4xx/5xx responses.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
paramsYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior5/5

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

Beyond the annotations (readOnlyHint, idempotentHint, destructiveHint), the description discloses that scores come from explicit weighted rules, not a trained model, and provides an auditable rule-by-rule breakdown. It also reveals the underlying HTTP service dependency, configurability via env var, and timeout/error behavior.

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?

Though lengthy, every sentence serves a purpose. The structure is front-loaded with the core purpose, then follows with parameters, return schema, error handling, and usage examples. No filler or redundancy.

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

Completeness5/5

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

Given the tool's complexity (nested input schemas, multiple risk dimensions, output format options, error cases), this description is complete. It includes the return schema, error response format, timeout behavior, and concrete usage scenarios. The annotations already cover safety, so no gaps remain.

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 coverage is 0%, so the description must carry full parameter meaning. It thoroughly explains each field within the params object, including types, constraints, and semantics (e.g., historical_metrics improves the scalability-gap score). It adds value beyond the raw schema by explaining input effects and defaults.

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 opens with a specific verb+resource: 'Score architecture risk from a declared system inventory.' It clearly distinguishes from the sibling tool by explicitly saying not to use it for cost/billing analysis and directing to cloudsealed_analyze_billing_waste.

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 'Use when' examples with concrete queries and an explicit 'Don't use when' alternative. This gives the agent clear decision criteria for tool selection.

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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