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knishioka

Cost Management MCP

by knishioka

cost_trends

Analyze cost trends over time across AWS, OpenAI, and Anthropic to identify spending patterns and optimize cloud expenditures.

Instructions

Analyze cost trends over time with insights

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
providerNoSpecific provider to analyze (optional)
periodNoTime period to analyze30d
granularityNoData granularitydaily
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. It mentions 'analyze' and 'insights', which imply read-only operations, but doesn't specify whether this tool requires authentication, has rate limits, returns aggregated data, or handles errors. For a 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.

Conciseness4/5

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

The description is a single, efficient sentence with no wasted words, making it appropriately concise. However, it's not front-loaded with critical details like scope or differentiation from siblings, which slightly reduces its effectiveness despite the brevity.

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?

Given the complexity of analyzing cost trends with multiple parameters and no output schema, the description is incomplete. It doesn't explain what 'insights' include, how results are formatted, or any dependencies on other tools. With no annotations and an unspecified output, this leaves the agent under-informed for proper usage.

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?

The input schema has 100% description coverage, with clear enums and defaults for all three parameters. The description adds no additional meaning beyond the schema, such as explaining how 'provider' interacts with 'period' or what 'insights' entail. Since schema coverage is high, the baseline score of 3 is appropriate, as the description doesn't compensate but also doesn't detract.

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

Purpose3/5

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

The description 'Analyze cost trends over time with insights' states a general purpose (analyzing cost trends) but lacks specificity about what resources or data it operates on. It doesn't distinguish itself from sibling tools like 'cost_breakdown' or 'cost_get', making it somewhat vague about its exact function within the toolset.

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?

No guidance is provided on when to use this tool versus alternatives like 'cost_breakdown', 'cost_periods', or provider-specific tools such as 'aws_costs' or 'openai_costs'. The description offers no context about prerequisites, exclusions, or comparative use cases, leaving the agent without direction on 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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