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get_collection

Read-onlyIdempotent

Fetch a specific collection by ID or slug to return its name, workspace, parent, child prompt counts, and timestamps; use this when you know the target rather than browsing.

Instructions

Fetch one collection by id or slug and return its name, slug, workspace, parent collection id, child collections with prompt counts, and timestamps. Use list_collections when browsing and get_collection when you already know the target.

Input Schema

NameRequiredDescriptionDefault
collection_idYesCollection ID or slug to retrieve

Output Schema

NameRequiredDescriptionDefault
okYesWhether the tool call succeeded and returned structured data
dataNoStructured success payload when ok is true
errorNoStructured error payload when ok is false

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changedv1.0.9
    • changedInput schema / $schema
      Previous value: -"http://json-schema.org/draft-07/schema#"New value: +"https://json-schema.org/draft/2020-12/schema"
    • changedOutput schema / $schema
      Previous value: -"http://json-schema.org/draft-07/schema#"New value: +"https://json-schema.org/draft/2020-12/schema"
  2. Addedv1.0.1
  3. Removed
  4. First observed

TDQS

A4.2/5.0
Behavior3/5

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

Annotations already declare readOnlyHint, idempotentHint, destructiveHint=false and openWorldHint, so safety and idempotency are covered structurally. The description adds the result shape, but that is largely duplicated by the existing output schema, and it says nothing about failure behavior (e.g., id/slug not found) or how id vs slug resolution differs.

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?

Two sentences, zero filler, with the core action and result contents front-loaded ahead of the disambiguation sentence. Every clause earns its place.

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?

With one fully documented parameter, rich annotations, and an output schema, the description covers what an agent needs to select and call it. The only gap is unstated error behavior for a missing collection, which is minor given the surrounding structured data.

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?

There is one parameter with 100% schema description coverage ('Collection ID or slug to retrieve'), so the schema already carries the semantics. The description's 'by id or slug' restates rather than extends it — baseline 3 is appropriate.

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?

Names a specific verb+resource (fetch one collection) and enumerates the returned fields — name, slug, workspace, parent id, child collections with prompt counts, timestamps — so the agent knows exactly what shape of data arrives. It also explicitly contrasts with the list_collections sibling, removing ambiguity.

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?

States the routing rule directly: use list_collections when browsing, get_collection when the target is already known. That is an explicit when-to-use/when-not pairing with a named alternative, leaving nothing to inference.

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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