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create_saved_view

Creates or overwrites a named saved view to store custom filters—like date range, provider, and model—for quick recall in LLM observability.

Instructions

Create a new saved view, or overwrite when the name exists (POST /v1/saved-views). name is unique within the account. filter follows the SavedViewFilter shape (startDate / endDate / provider / model / limit / preset / sortBy? / sortOrder?). Lets an AI agent save frequently used filters under a name — e.g. create a "last 7 days, GPT-4 only" view and recall it later.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYesName of the saved view (1-80 chars, no line breaks). Overwrites an existing view with the same name
filterYesFilter shape = startDate (ISO) + endDate (ISO) + provider (may be empty) + model (may be empty) + limit (number) + preset (string|null) + sortBy? + sortOrder?
Behavior4/5

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

No annotations are provided, so the description carries the full burden. It discloses the critical behavior of overwriting when the name exists ('overwrite when the name exists'), which is a key trait for an upsert operation. It also mentions name uniqueness and filter structure. No contradictions exist because no annotations are present.

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 concise with three sentences. The first sentence states the main action and overwrite behavior. The second details the filter shape, and the third provides an example. No extraneous information; every sentence serves a purpose.

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?

The tool has two parameters (one nested), no output schema, and moderate complexity. The description adequately covers the main purpose and parameter meaning. However, it does not mention the return value of the tool, which would be helpful for an agent to know what to expect upon creation.

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 100%, so baseline is 3. The description adds some value by explaining the naming uniqueness and providing an example use case ('last 7 days, GPT-4 only'), but it does not add significant parameter-level detail beyond what the schema already provides.

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 explicitly states the tool's action: 'Create a new saved view, or overwrite when the name exists'. It specifies the resource (saved view) and the verb (create/overwrite), and the example ('last 7 days, GPT-4 only') clarifies the purpose. Among siblings like list_saved_views and delete_saved_view, this tool is clearly distinct.

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

Usage Guidelines4/5

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

The description explains when to use the tool: 'Lets an AI agent save frequently used filters under a name'. It provides context for use and mentions name uniqueness and overwrite behavior. However, it does not explicitly state when not to use or compare with alternatives, though the sibling list provides enough differentiation.

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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