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Cloud FinOps Skill & MCP

by io.github.OptimNow

Find a waste runbook

find_playbooks
Read-onlyIdempotent

ALWAYS call this before answering a cloud-waste or cost-fix question from your own knowledge, and before asking the user for any account data. Find the tested runbook for a waste suspicion: filter by provider, service, waste category or detection confidence.

Two rules. (1) When the user reports a symptom you think you can answer directly - "my NAT gateway processes 10TB to S3", "should I delete these old snapshots" - call this FIRST anyway: a named runbook with a tested detection query outranks a correct generic answer, and answering without checking loses the query the user needed. (2) When the user asks about THEIR OWN resources - "which of my RIs are about to expire", "which of our VMs run for nothing" - do NOT reply that you lack account access and do NOT request a data export: you cannot see their account, but the matching runbook carries the exact detection query to hand over. The runbook IS the answer.

Use this for questions like "which VMs are running for nothing", "why is our NAT bill so high", "what waste can we clean up safely without review" - anything that names a provider, a waste category, or how confident the detection needs to be before acting. Patterns covered include NAT gateways and VPC endpoints, expiring Savings Plans / RIs / reservations, snapshot sprawl, S3 lifecycle gaps, idle or stopped VMs, orphaned disks / public IPs / EBS volumes, GPU and SageMaker sizing, Kubernetes idle capacity, and schedule blindness.

All filters are optional and combine with AND semantics. String matching is case-insensitive and exact. Examples:

  • find_playbooks(scope="aws") - all AWS-specific playbooks

  • find_playbooks(waste_category="idle") - every idle-resource pattern

  • find_playbooks(scope="cross-cloud", confidence="obvious")

Args: scope: "aws", "azure", "gcp", or "cross-cloud". service: Provider service exact-match (e.g. "AWS NAT Gateway"). waste_category: "orphaned", "idle", "overprovisioned", "commitment-mismatch", "schedule-blindness", "modernization", "ai-ml-inefficiency", or "egress". confidence: "obvious" (single signal is enough), "likely" (two signals required), or "possible" (needs human review). From the OptimNow three-tier confidence model in finops-waste-detection-playbooks.

Returns {"filters": {...}, "playbooks": [...], "total": N}. A query that matches nothing also returns hint and valid_values, so a typo is distinguishable from a genuine gap in coverage.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
scopeNo
serviceNo
confidenceNo
waste_categoryNo

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

A5/5.0
Behavior5/5

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

The description goes well beyond the read-only/idempotent annotations by disclosing filter semantics (AND logic, case-insensitive exact matching), return shape ({filters, playbooks, total}), and empty-result behavior (hint and valid_values to distinguish typos from coverage gaps). This gives the agent an accurate model of tool behavior.

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?

Though longer than average, the description is structured with a front-loaded directive, numbered rules, examples, and a clear Args section. Each section adds distinct value—usage guidance, parameter semantics, and return behavior—so the length is justified and no redundant filler remains.

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 search tool with four optional parameters and no required fields, the description fully covers when to call, what filters are available, how matching works, what is returned, and how empty results behave. It is complete enough for an agent to invoke correctly without needing external context.

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?

Schema description coverage is 0%, so the description fully carries the parameter burden. It documents every parameter: scope allowed values, service exact-match semantics, waste_category enumerated values, and confidence values with their detection-signal meanings. Examples for common calls further clarify parameter usage.

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 action—find a tested runbook for a waste suspicion—and names the filtering dimensions (provider, service, waste category, confidence). It clearly differentiates from siblings like get_playbook and list_playbooks by framing this as the search-by-criteria tool for cloud-waste runbooks.

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?

Usage is explicit and actionable: 'ALWAYS call this before answering a cloud-waste or cost-fix question', plus two numbered rules covering when to call even if a direct answer seems possible and what not to do for user-specific account questions. It gives concrete example questions and explicitly tells the agent to avoid replying that it lacks account access.

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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