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chaandannn

nable (finops-mcp)

pin_view

Pin a cost slice to the dashboard as a saved card. The card re-runs its slice live on each load, showing fresh numbers for the trailing days.

Instructions

Pin a cost slice to the dashboard as a saved card. Takes the same slicing arguments as slice_costs (dimensions / filters / exclusions / metric / etc.), plus a title and a rolling lookback days. The pinned card re-runs its slice live on each dashboard load over the trailing days, so it always shows fresh numbers. scope: "instance" (shared on this nable) or "me". Read-only on the cloud: this only saves a view definition locally.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
daysNo
limitNo
scopeNoinstance
titleYes
metricNoEffectiveCost
filtersNo
order_byNometric
dimensionsNo
exclusionsNo
granularityNoTOTAL
Behavior4/5

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

No annotations provided, so description carries full burden. It discloses that the tool is read-only on the cloud ('only saves a view definition locally') and that the pinned card re-runs live on each dashboard load, describing the dynamic behavior.

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?

Description is multi-sentence but efficient, front-loading the main action. It avoids unnecessary details and adds value beyond the schema.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a complex tool with 10 parameters and no output schema, the description covers the core purpose, behavioral traits (read-only, live refresh), and key parameters (days, scope, slicing arguments). It provides sufficient context for correct usage.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so description must add meaning. It explains the 'days' parameter as a rolling lookback and 'scope' values ('instance' vs 'me'). It mentions dimensions, filters, exclusions, metric, and title. However, parameters like 'limit', 'order_by', and 'granularity' are not explained.

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?

Description clearly states the action ('Pin a cost slice to the dashboard as a saved card') and the resource ('cost slice'). It differentiates from sibling tool 'slice_costs' by noting it pins the view and re-runs live.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Describes when to use (to pin a view to dashboard) and mentions live refresh behavior. However, it does not explicitly guide when not to use or compare to alternatives like 'get_pinned_view', 'list_pinned_views', or 'unpin_view'.

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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