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create_prompt

Create a new prompt version for the authenticated application. The new prompt is automatically set as active and the previously active prompt of the same type and subtype is deactivated. Use this whenever you want to change prompt text.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
typeYesTypes of prompts in the system.
promptYes
subtypeNo'routing_instructions' or 'output_instructions'. Returns all when omitted.
interface_typeNoInterface scope of the prompt: 'chat', 'navigator', 'search', or 'api'. Required for 'chat' and 'search_answering' prompt types; omit for 'suggested_questions' and 'chat_evaluation'.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

TDQS

A4/5.0
Behavior4/5

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

The description goes beyond the false annotation flags by disclosing the key side effect: the new prompt is automatically set active and the previously active prompt of the same type/subtype is deactivated. It does not cover secondary details such as idempotency or what happens to the deactivated version, but the primary behavioral risk is clearly stated.

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 tight sentences front-load the action, disclose the important side effect, and end with an explicit usage cue. There is no filler or redundant repetition of schema details.

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?

Core behavior, side effects, and primary use case are all covered, and the output schema removes the need to describe return values. The main gap is the unresolved relationship with the sibling update_prompt, which matters for an agent choosing between the two tools.

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 75% and the schema already explains type, subtype, and interface_type, including conditional requirements. The description adds only marginal parameter context ('prompt text' and the relevance of type/subtype), so it meets the baseline but does not compensate for the undocumented prompt parameter.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description names a specific action ('Create a new prompt version') and target resource ('prompt for the authenticated application'), and it explains the activation/deactivation behavior. It does not explicitly distinguish this tool from the sibling update_prompt, so it stops short of full sibling differentiation.

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?

'Use this whenever you want to change prompt text' is an explicit usage directive with clear context. However, it provides no when-not-to-use guidance and no mention of update_prompt as an alternative, leaving some selection ambiguity.

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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TDQS

A3.6/5.0
Disambiguation5/5

Each tool targets a distinct resource or metric, and even the closely related analytics tools (e.g. get_top_languages vs get_top_locales, get_top_interaction_sources vs get_top_clicked_urls) are explicitly differentiated in their descriptions. There is no real overlap that would cause an agent to misselect.

Naming Consistency4/5

The verb prefixes create_, get_, list_, read_, and update_ are used predictably, and there is no mixing of camelCase or other conventions. The main inconsistency is that read_sessions is actually a list operation while list_nodes is the equivalent pattern for nodes, and read_session_detail is the singular read.

Tool Count2/5

At 33 tools, this set is well beyond the 16-25 'heavy' range and far above the typical well-scoped 3-15 range. Many of the get_top_* analytics endpoints are individually distinct but could likely be consolidated into fewer parameterized tools to reduce agent selection overhead.

Completeness3/5

The read and analytics side is comprehensive, but the management lifecycle has notable gaps: knowledge nodes support create/read/update but no delete, and data sources/tools lack create/delete operations. Agents can work around some gaps, but content deletion is a clear dead end for a knowledge-base management surface.

Resources