Skip to main content
Glama
cloudsealed

cloudsealed-mcp

Official
by cloudsealed

cloudsealed_analyze_billing_waste

Read-onlyIdempotent

Detect cost anomalies in cloud billing exports and identify wasted spend with robust statistical analysis. Get actionable savings recommendations from AWS, GCP, or Azure CSV data.

Instructions

Detect cost anomalies in a cloud billing export (AWS/GCP/Azure/generic).

Models the expected daily spend for each day as a rolling-median baseline times a day-of-week factor, then flags days whose actual spend deviates from that baseline by a robust (median-absolute-deviation-based) modified z-score. This is resistant to the "masking effect" that causes textbook mean+standard-deviation detectors to miss anomalies once a few large spikes have inflated the standard deviation. It does NOT call any cloud provider API — the caller must already have exported the billing data to a CSV/text string and pass its contents directly.

Args: params (AnalyzeBillingWasteInput): Validated input containing: - csv_content (str): Raw billing export text (see field description for supported provider formats). - analysis_type (AnalysisType): 'waste-audit' (default), 'cost-forecast', or 'efficiency'. - response_format (ResponseFormat): 'markdown' (default) or 'json'.

Returns: str: Markdown report, or a JSON object with this schema: { "anomalies": [ {"date": str, "expectedCost": float, "actualCost": float, "deviation": float, "zScore": float, "severity": "LOW"|"MEDIUM"|"HIGH"|"CRITICAL", "description": str} ], "metrics": {"averageDailyCost": float, "stdDeviation": float, "sharpeRatio": float, "wastePercentage": float}, "recommendations": [ {"title": str, "description": str, "potentialSavings": float, "effort": "LOW"|"MEDIUM"|"HIGH"} ], "summary": str }

Error response: "Error: <message>" when the CSV cannot be parsed
(e.g. no recognizable date/cost columns).

Examples: - Use when: "Why did our AWS bill spike last month?" -> paste the CUR export contents as csv_content. - Use when: "What will we spend next month at this rate?" -> analysis_type="cost-forecast". - Don't use when: you need architecture/reliability risk instead of cost — use cloudsealed_score_architecture_risk.

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?

Annotations already declare readOnlyHint=true and destructiveHint=false, but the description adds valuable behavior: it explicitly states it does NOT call any cloud provider API, explains the rolling-median and z-score algorithm, describes the error response format, and warns about the masking effect. This goes well beyond annotations and provides deep insight into the tool's 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 longer than average, the description is organized into clear sections (Args, Returns, Error, Examples) with every sentence earning its place. It is front-loaded with the purpose and maintains focus without fluff, making it highly scannable despite its length.

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?

The description is exceptionally complete for a complex tool: it includes the full output JSON schema, error handling, provider-specific input notes, and practical usage examples. It covers all aspects an agent needs to select and invoke the tool correctly, leaving no gaps.

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?

The description enumerates all parameters (csv_content, analysis_type, response_format) with their defaults and contextual meaning, including examples like analysis_type='cost-forecast'. This fully compensates for the 0% schema coverage on the top-level 'params' object and adds value beyond the nested schema descriptions.

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 a specific verb and resource: 'Detect cost anomalies in a cloud billing export (AWS/GCP/Azure/generic).' It clearly distinguishes from the sibling tool by explicitly naming cloudsealed_score_architecture_risk as an alternative for architecture/reliability risk, making the purpose unambiguous.

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 when-to-use examples with concrete questions and how to set csv_content and analysis_type. Clearly states a prerequisite ('caller must already have exported the billing data') and gives a direct 'Don't use when' with the alternative tool, leaving no ambiguity about appropriate usage.

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/cloudsealed/cloudsealed-mcp'

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