Skip to main content
Glama

Read one FinOps guide

get_reference
Read-onlyIdempotent

Fetches the full content or a single section of a known FinOps reference, returning decision rules and worked examples for cost questions like commitment sizing, chargeback, or allocation.

Instructions

Fetch the guidance on one FinOps topic - the billing mechanics, decision rules and worked examples behind a defensible answer - either whole or one section at a time.

Use this when you need the actual content of one known reference,
after ``list_references`` or ``find_references`` told you which one
serves the question. Advisory questions (commitment sizing, chargeback
design, allocation methodology) are served by the reference's decision
rules and worked examples, which a summary does not carry.

Pass ``section`` when the question is narrower than the file. The
``approx_tokens`` hint in the listing tells you when this matters: the
largest references run to about 15,000 tokens and the Azure pattern catalogue
is an enumerated list, so a question about storage lifecycle wants one
section of ``finops-azure-patterns``, not all of it. Omit ``section`` for the whole
file when you need the cross-cutting reasoning.

Args:
    name: Reference name as returned by ``list_references`` (e.g.
        ``"finops-aws"``, ``"finops-genai-capacity"``,
        ``"optimnow-methodology"``).
    section: Optional H2 or H3 heading to return on its own. Matched
        case-insensitively and partially against the headings, so a
        natural phrase works - ``"storage"``, ``"commitment decision
        tree"``. A heading's trailing count is ignored, so
        ``"storage optimization patterns"`` matches
        ``"Storage Optimization Patterns (28)"``. If it matches nothing
        you get the list of available headings back, not the whole file.

Without ``section``, returns ``{"name": ..., "content": "...",
"lines": N}`` where ``content`` is the file verbatim. With ``section``,
returns ``{"name", "title", "section", "section_level", "partial": true,
"content", "lines", "full_lines"}`` where ``content`` is that section
prefixed by the reference's title, plus ``other_matching_sections`` when
the phrase matched more than one heading.

On a miss, returns ``{"error": ..., "suggestions": [...]}``. An unknown
name gives up to three string-distance matches; an unmatched ``section``
gives ``available_sections`` - every heading in the file - so the retry
is exact.

Input Schema

NameRequiredDescriptionDefault
nameYes
sectionNo

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.9/5.0
Behavior5/5

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

Annotations already cover the safety profile (readOnly/idempotent/non-destructive), and the description adds what they cannot: partial-return semantics with 'partial': true, the fact that a section result is prefixed by the reference title, and precise miss behavior (unknown name → up to three distance matches; unmatched section → available_sections). That is exactly the error/retry context an agent needs.

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 purpose, then usage, then params — good ordering with no filler sentences. It is somewhat long, and the paragraph spelling out both return shapes partially duplicates the existing output schema, which is the only trimming opportunity.

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?

Two-parameter read with rich annotations and an output schema; the description still adds the routing decision, section-selection heuristic tied to approx_tokens, and the miss/retry contract. Nothing an agent needs to call this correctly is missing.

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 coverage is 0%, and the description fully compensates: it gives concrete name examples, then defines section matching rules (H2 or H3, case-insensitive, partial, trailing count ignored) with a natural-phrase example and the 'storage optimization patterns' → 'Storage Optimization Patterns (28)' case, plus the fallback when nothing matches.

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?

States a specific verb ('Fetch the guidance on one FinOps topic') with the exact resource, and immediately scopes it to a *known* reference, which distinguishes it from the discovery siblings find_references and list_references.

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?

Explicitly sequences itself after list_references/find_references ('after ... told you which one serves the question'), names the class of questions it serves (advisory: commitment sizing, chargeback design, allocation methodology), and gives a concrete rule for section vs. whole file with a worked example (storage lifecycle vs. finops-azure-patterns).

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.