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Cloud FinOps Skill & MCP

by io.github.OptimNow

Browse the cloud waste runbooks

list_playbooks
Read-onlyIdempotent

See every ready-made runbook for finding and fixing cloud waste: idle, orphaned and overprovisioned resources, egress surprises, schedule blindness and AI/ML inefficiency across AWS, Azure and GCP.

Use this to discover which waste patterns have a runbook. When the question already names a provider, waste category, or confidence tier, call find_playbooks instead.

Each playbook is a small (~80-130 line) runbook scoped to one waste pattern (e.g. aws-zombie-nat-gateway, azure-orphan-disks). Returns {"playbooks": [...], "total": N} where each entry includes name, title, scope (aws/azure/gcp/cross-cloud), service, waste_category, confidence (obvious/likely/possible), and approx_tokens - the same size hint the reference listing carries, so a multi-playbook answer can be budgeted before fetching.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  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=true, idempotentHint=true, and destructiveHint=false, so the safe read behavior is covered. The description adds meaningful context beyond that: it returns all runbooks unfiltered, describes the playbook size range, and details the response envelope and entry fields, which helps an agent understand the result before invoking.

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 well-structured: purpose first, usage guidance second, then output details. It is slightly longer than strictly necessary, but the extra detail about response shape and token budgeting earns its place for a tool that feeds into multi-playbook planning.

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?

For a zero-parameter, read-only listing tool, the description is complete. It explains purpose, alternative routing, output shape, entry fields, and even the approximate size of each runbook. An agent has everything needed to decide whether to call this tool and to interpret its result.

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 has zero parameters, so there is no parameter semantics to document. The base expectation for a zero-parameter tool is 4, and the description appropriately explains what the returned list will contain rather than spending space on nonexistent arguments.

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 opens with a specific verb and resource: 'See every ready-made runbook for finding and fixing cloud waste'. It clearly scopes the tool to listing runbooks across AWS, Azure, and GCP, and differentiates it from find_playbooks by naming the filtering behavior this tool does not have.

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?

The description explicitly states when to use this tool: to discover which waste patterns have a runbook. It also names the alternative, find_playbooks, and gives the exact condition for choosing it ('when the question already names a provider, waste category, or confidence tier'). This is direct, actionable guidance.

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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TDQS

A4.6/5.0
Disambiguation4/5

The two domains (playbooks/references) are cleanly separated, but within each, list_ vs find_ has genuine boundary ambiguity since find_ with no filters behaves like list_. The descriptions mitigate this with explicit cross-references, and get_ is clearly distinct (requires a name from the others).

Naming Consistency5/5

Flawless verb_noun pattern: three verbs (find_, get_, list_) applied identically across both content types, all snake_case, plural for collections and singular for single-item fetches. The convention is perfectly predictable.

Tool Count5/5

Six tools is ideal for a read-only content library with two resource types: list/find/get for playbooks and list/find/get for references. Each tool earns its place, and the symmetry makes the set easy to reason about.

Completeness4/5

The full discovery-to-retrieval lifecycle is covered for both content types: list (enumerate), find (filter), get (fetch detail). The main gap is the absence of a full-text or fuzzy search tool (find_ uses exact-match only), which could require agents to over-fetch and filter manually. There are no dead ends, and error hints help self-correction.

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