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knishioka

Cost Management MCP

by knishioka

cost_get

Retrieve cost data for AWS, OpenAI, or Anthropic services within a specified time period to analyze expenditures and usage trends.

Instructions

Get cost data for a specific provider and time period

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
providerNoThe provider to get costs for (optional, defaults to all)
startDateYesStart date in YYYY-MM-DD format
endDateYesEnd date in YYYY-MM-DD format
granularityNoCost aggregation granularitytotal
groupByNoDimensions to group costs by (e.g., SERVICE, REGION)
Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. While 'Get' implies a read operation, the description doesn't mention authentication requirements, rate limits, error conditions, response format, or whether this is a real-time query versus cached data. For a cost query tool with no annotation coverage, this leaves significant behavioral gaps.

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 a single, efficient sentence that gets straight to the point with zero wasted words. It's appropriately sized for the tool's complexity and front-loads the essential information.

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

Completeness2/5

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

For a cost query tool with 5 parameters, no annotations, no output schema, and many sibling tools offering similar functionality, the description is inadequate. It doesn't explain what the tool returns, how it differs from siblings, or important behavioral aspects like authentication, rate limits, or data freshness.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the schema already documents all 5 parameters thoroughly. The description adds minimal value beyond what's in the schema - it mentions 'provider and time period' which aligns with the provider, startDate, and endDate parameters, but doesn't provide additional context about parameter interactions or usage patterns.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the verb 'Get' and resource 'cost data' with scope 'for a specific provider and time period', making the purpose immediately understandable. However, it doesn't distinguish this tool from its many siblings (like aws_costs, openai_costs, cost_trends, etc.), which appear to offer similar or overlapping functionality.

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

Usage Guidelines2/5

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

The description provides no guidance on when to use this tool versus its many siblings. With tools like aws_costs, openai_costs, cost_trends, cost_breakdown, and provider_compare available, there's no indication of what makes this tool distinct or when it should be preferred over alternatives.

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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