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Cloud FinOps Skill & MCP

Browse the FinOps knowledge library

list_references
Read-onlyIdempotent

See what FinOps guidance is available: billing mechanics, commitment strategy, allocation and chargeback, AI cost management, and per-provider cost handbooks (AWS, Azure, GCP, OCI, Databricks, Snowflake, ...).

Use this to discover what the library covers before deciding what to
fetch. When the question already names a FinOps domain, phase, persona
or maturity, call ``find_references`` instead of scanning this full
list.

Returns a dict shaped ``{"references": [...], "total": N}`` where each
entry includes ``name``, ``title``, a one-line ``description``, the
discriminating FCP facets (``fcp_domain``, ``fcp_capability``,
``fcp_phases``, ``fcp_personas_primary``, ``fcp_maturity_entry``) and
``approx_tokens``.

Read ``approx_tokens`` before fetching: the library runs from about 2,000
to over 25,000 tokens per file. Above roughly 10,000, prefer
``get_reference(name, section=...)`` and pull the part you need.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.6/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint and destructiveHint=false, so the safety profile is covered. The description goes beyond that with genuinely useful operational context: per-file token sizes (2,000-25,000), the ~10,000 threshold, and the recommendation to switch to get_reference(name, section=...) above it. It stops short of describing pagination or whether the list is exhaustive for very large libraries, which keeps it at a 4 rather than a 5.

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?

Front-loaded with the coverage list, then the usage rule, then the return shape and token guidance; every block is doing work. The return-shape paragraph ('Returns a dict shaped ... where each entry includes name, title, ...') partially restates what the output schema already declares, which is the one place a sentence could be trimmed.

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?

An output schema exists, so return-value documentation is not strictly required, yet the description still tells the agent which fields discriminate entries and how to budget tokens before fetching. Combined with the named alternative and the no-parameter contract, an agent has everything needed to call this correctly on the first try.

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

Parameters4/5

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

The tool takes zero parameters, so the baseline is 4 and there is no schema semantics to compensate for. The description's discussion of section= on the sibling get_reference is routing help rather than parameter documentation for this tool.

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 states a specific verb and resource ('See what FinOps guidance is available') and enumerates the actual coverage areas, so an agent knows exactly what this returns. It also names the sibling it is not ('find_references') and the condition that separates them, which is more than most definitions manage.

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?

Explicit when-to-use ('discover what the library covers before deciding what to fetch') and explicit when-not-to-use ('When the question already names a FinOps domain, phase, persona or maturity, call find_references instead of scanning this full list'). The alternative is named and the selecting condition is unambiguous.

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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