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Glama

Cloud FinOps Skill & MCP

by io.github.OptimNow

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 3,000 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. Dates show when Glama detected each change.

  1. First observed

TDQS

A4.7/5.0
Behavior4/5

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

Annotations already establish read-only, non-destructive, idempotent behavior. The description adds value by disclosing the returned dict shape with exact fields, behavioral context about token sizes (3,000 to 25,000+), and an explicit threshold for switching to sectioned fetching. This exceeds annotation coverage without duplicating it.

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 organized in clear front-loaded blocks: what the library contains, when to use this tool versus siblings, the return format, and token guidance. Every sentence earns its place; no filler or repetition of the schema appears.

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 parameterless listing tool, the description covers scope, alternatives, return structure, and practical usage constraints (token sizes). An output schema exists, and the description complements it rather than relying solely on it. Nothing material is missing for an agent to successfully decide and invoke this tool.

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 the baseline of 4 applies. There is no parameter schema to augment, and the description appropriately focuses on behavior and return shape rather than inventing parameter-related details.

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 ('See what FinOps guidance is available') and a distinct resource (the FinOps knowledge library). It enumerates concrete content areas and explicitly names the sibling alternative it is not, making the tool easy to distinguish from find_references and list_playbooks.

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 gives direct when-to-use guidance: use this for discovery before fetching, and explicitly says to call find_references instead when a specific FinOps domain, phase, persona, or maturity is already named. It also advises using get_reference with section=... for large files, providing a clear decision path.

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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