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chaandannn

nable (finops-mcp)

benchmark_costs

Compare your AWS account's spend profile against anonymized peer group medians to identify areas above or below industry benchmarks across key metrics like EC2%, RDS%, and savings plan coverage.

Instructions

Compare this account's spend profile against anonymised peer group medians.

Shows where you're above or below the median for companies in your industry vertical across metrics like: EC2%, RDS%, savings plan coverage, idle resource %, LLM spend %, data transfer %, and rightsizing opportunity %.

Args: account_id: AWS account ID to analyse vertical: industry peer group, saas, ecommerce, fintech, media, ai_ml, default days: lookback period for metric calculation

Returns per-metric comparisons with assessments (better/similar/worse) and insights.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
daysNo
verticalNodefault
account_idYes
Behavior4/5

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

No annotations are provided, so the description carries the full burden. It clearly states the tool is a read operation (comparison, no destructive actions) and describes the output format (per-metric comparisons with assessments). It does not mention authentication, rate limits, or other behaviors, but the core behavior is transparent.

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 concise (6 lines), front-loads the core purpose, then details metrics and parameters. Every sentence serves a purpose, with no redundancy or fluff.

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 moderate complexity (3 parameters, no output schema), the description fully covers what the tool does, what metrics are compared, and what the output looks like (assessments with better/similar/worse). It is sufficient for an agent to understand and invoke the tool correctly.

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?

With 0% schema description coverage, the tool description compensates by providing clear, brief meanings for all three parameters: account_id (AWS account ID), vertical (industry peer group with examples), and days (lookback period). This adds significant value beyond the schema's type/default info.

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 action ('Compare this account's spend profile'), the resource ('spend profile'), and the baseline ('anonymised peer group medians'). It distinguishes from sibling tools like 'get_cost_summary' or 'explain_cost_change' by focusing specifically on peer benchmarking.

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

Usage Guidelines4/5

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

The description explains what the tool does and lists the metrics and peer verticals, implying usage for benchmarking against peers. However, it lacks explicit guidance on when to use this over alternatives (e.g., 'get_cost_summary') or when not to use it.

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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