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Skeego

opendata-mcp

by Skeego

create_view_v1_user_views_post

Create a user view for a dataset with custom columns, filters, and sorting. The view is saved as a draft by default.

Instructions

POST /v1/user-views (auth: Bearer OPENDATA_API_KEY) — Create a user view — Create a new user view.

Slugs are unique per creator. Defaults to draft (is_draft=True). Reserved slugs (latest, versions, etc., or any starting with a digit) are rejected with 400.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
bodyYesRequest body (application/json) for POST /v1/user-views
Behavior4/5

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

With no annotations, the description discloses key behaviors: auth, default draft status, slug uniqueness per creator, and rejection of reserved slugs. However, it omits details on conflict handling (e.g., if slug taken) and response format.

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?

Three sentences, 45 words, front-loaded with endpoint and auth. No redundant information. Efficient and clear.

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

Completeness3/5

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

Given no annotations or output schema, the description is adequate but incomplete: it explains creation behavior but does not mention return value, error responses, or idempotency. This is a gap for a creation tool.

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 coverage is high (all 1 parameter body is described), so baseline is 3. The description adds context on slug constraints and draft default, but does not elaborate on parameters like dataset_provider or config. Minimal added value beyond schema.

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?

Clearly states the tool creates a user view via POST endpoint, with specific constraints (slugs unique per creator, default draft, reserved slugs rejection). Distinguishes from siblings like create_and_publish_view or get_view.

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?

Specifies auth requirement (Bearer OPENDATA_API_KEY) and default draft behavior, but does not explicitly state when to use this tool vs alternatives like create_and_publish_view. Guidance is implied (draft vs publish) but lacks explicit when-not or alternative references.

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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