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list_prompts

Read-only

List prompts for the authenticated application. Optionally filter by type, subtype, or active status. Returns paginated results ordered by active status then last update.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
pageNo
limitNo
subtypeNo'routing_instructions' or 'output_instructions'. Returns all when omitted.
is_activeNoActivating a prompt automatically deactivates other prompts of the same type.
prompt_typeNo'chat', 'search_answering', 'suggested_questions', or 'chat_evaluation'. 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

A4.2/5.0
Behavior4/5

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

Annotations already mark the operation as read-only, and the description adds useful behavioral context: it returns paginated results ordered by active status then last update, and supports optional filters. This goes beyond the annotation without contradiction.

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 two sentences, front-loaded with the core action, and every clause adds information: scope, optional filters, pagination, and ordering. There is no filler or repetition.

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 read-only list operation with an output schema, the description covers purpose, scope, filtering options, and result ordering. It doesn't explicitly mention the page/limit parameters or interface_type as a filter, but the schema covers those, so the description remains adequate.

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 67% and the schema already provides descriptions for subtype, is_active, prompt_type, and interface_type. The description's mention of filtering by 'type, subtype, or active status' is helpful but redundant and omits interface_type; page and limit are left to the schema, which lacks descriptions for them.

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 opens with a specific verb and resource: "List prompts for the authenticated application." It clearly distinguishes this list operation from sibling creation/update/read tools like create_prompt, update_prompt, and read_node, and adds scoping and result-ordering details.

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 implies when to use the tool: whenever the agent needs to list or filter prompts for the current application. It does not explicitly name alternatives or exclusions, but the list-vs-read/create context among siblings is clear enough for a straightforward enumeration tool.

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

A3.7/5.0
Disambiguation5/5

Each tool targets a distinct resource or metric. The many get_top_* endpoints are differentiated by the specific dimension measured, and read_* / list_* / get_* verbs consistently separate detail retrieval from aggregation and paginated listings. Explicit distinctions like get_top_languages vs get_top_locales and get_top_interaction_sources vs get_top_clicked_urls remove ambiguity.

Naming Consistency5/5

Tool names follow a predictable verb_noun pattern: create_* for mutations that add, update_* for edits, list_* for paginated collections, read_* for detailed record access, and get_* for aggregate analytics. Even with 33 tools the naming convention is uniform and readable.

Tool Count2/5

33 tools exceeds the 25+ threshold for 'too many' and is heavy for a single server surface. While the analytics getters are individually focused, the set is larger than typical for an MCP server and could be consolidated or grouped more tightly.

Completeness3/5

Analytics coverage is thorough, and nodes/prompts have create/read/update lifecycles. However, there are no delete operations anywhere, and data sources and tools support update but not create or delete, leaving notable lifecycle gaps for administrative tasks.

Resources