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Glama

Cloud FinOps Skill & MCP

by io.github.OptimNow

Find the right FinOps guide

find_references
Read-onlyIdempotent

Find which guidance serves a FinOps question - how to commit, size, allocate, charge back, forecast, or govern cloud and AI spend.

Use this for questions like "how should we size Savings Plans", "what should Finance own in chargeback", "what does a Crawl-stage org tackle first" - anything that maps to FinOps Framework facets (domain, capability, phase, persona, maturity) - and you want only the references that serve it, instead of scanning the full list.

All filters are optional and combine with AND semantics. String matching is case-insensitive and exact (not substring). Examples:

  • find_references(domain="Optimize Usage & Cost")

  • find_references(phase="Optimize", persona="Engineering")

  • find_references(persona="Engineering", persona_primary_only=True)

  • find_references(capability="Rate Optimization")

  • find_references(maturity="Crawl")

Args: domain: FinOps Framework domain (e.g. "Optimize Usage & Cost", "Quantify Business Value", "Manage the FinOps Practice"). capability: FinOps capability (matches fcp_capability and fcp_capabilities_secondary). phase: FinOps phase ("Inform", "Optimize", "Operate"). persona: Persona (matches fcp_personas_primary and fcp_personas_collaborating). maturity: Entry maturity level ("Crawl", "Walk", "Run"). persona_primary_only: when True, persona matches only the primary list. Use it when the default match barely narrows the set - broad personas like Engineering collaborate on nearly every file, so filtering on collaboration is descriptive, not discriminating. persona="Engineering", persona_primary_only=True is the engineering reading list; the default is the everything-they-touch view.

Returns {"filters": {...}, "references": [...], "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
phaseNo
domainNo
personaNo
maturityNo
capabilityNo
persona_primary_onlyNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

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

  1. Changed1 schema field changed
    • addedInput schema / properties / persona_primary_only
      Added value: +{
+  "default": false,
+  "title": "Persona Primary Only",
+  "type": "boolean"
+}
  2. First observed

TDQS

A5/5.0
Behavior5/5

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

The description discloses key behavioral traits beyond what annotations could provide: all filters are optional and combine with AND semantics, matching is case-insensitive and exact (not substring), and the nuance of persona_primary_only is explained with example usage. It also reveals what happens on no matches (returns hint and valid_values). Since no annotations were provided, the description carries the full burden and does so thoroughly.

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 front-loaded with the core purpose, then usage guidance, examples, parameter semantics, and return format. Though long, every sentence serves a purpose: no fluff, no repetition. The examples are illustrative and the parameter documentation is compact. It's structured logically and efficiently for the tool's complexity.

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 tool's complexity (6 optional filtering params, matching semantics, and return structure), the description covers all essential aspects: usage scenarios, filter combinations, exact matching rules, and the response format including error/edge-case behavior. It even explains how to recover from typos via valid_values. The description is complete and self-contained.

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?

The input schema provides only titles and types (mostly strings and boolean) with no descriptions. The description adds complete semantics: each parameter's meaning, which schema fields it matches (e.g., fcp_capability), valid enum-like values (Crawl, Optimize, Operate), and the special behavior of persona_primary_only. This is far beyond what the schema conveys, so the description adds substantial value.

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 purpose: finding FinOps guides for questions like committing, sizing, allocating, etc. It specifies the verb 'Find' and the resource 'which FinOps guide', and distinguishes from the alternative of 'scanning the full list'. The examples and facet list (domain, capability, phase, persona, maturity) make the scope unmistakable.

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 says 'Use this for questions like...' and provides concrete examples. It contrasts with the alternative of using the full list, effectively saying when this tool is appropriate. It also notes that all filters are optional and combine with AND semantics, which guides invocation. This is explicit and actionable.

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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