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search_prompts

Search the PromptSharp prompt library. Returns ready-to-run AI prompts scoped to your license tier (no token = 15 public teasers; vertical Pro = your vertical; all-access = the full cross-vertical library). Locked results are shown as stubs with an upgrade hint.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMax results (1-25, default 10).
queryYesKeywords to match against title, use-case, section, vertical, and prompt body.
sectionNoOptional section slug filter within a vertical.
verticalNoOptional vertical slug filter, e.g. sales, marketing, finance, cpg, dev, product, csuite, consulting, career, personal_finance, law, air.

TDQS

B3.3/5.0
Behavior3/5

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

With no annotations, the description carries the burden. It explains license-tier behavior, stub results for locked items, and an upgrade hint. However, it omits details like whether results include full prompt text or IDs, and doesn't mention pagination or rate limits.

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?

Extremely concise: two sentences that front-load the main purpose and then add key behavioral nuance. Every sentence earns its place with no redundancy.

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 output schema, the description gives a moderate sense of return value (ready-to-run prompts, stubs for locked). But it lacks details on pagination, total count, or exact output structure, leaving gaps for an AI agent.

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 no extra meaning beyond what the schema already provides for each parameter. It merely reiterates the concept of keyword matching.

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 clearly states 'Search the PromptSharp prompt library' with a specific verb and resource. It returns ready-to-run AI prompts, making the purpose obvious. However, it doesn't explicitly differentiate from sibling tools like get_prompt or list_verticals.

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

Usage Guidelines2/5

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

No guidance on when to use this tool versus alternatives (get_prompt, list_verticals). The description does explain license tier scoping, which is contextual, but lacks explicit when-to-use or when-not-to-use advice.

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

A4/5.0
Disambiguation5/5

Each tool has a distinct purpose: fetching a specific prompt, listing verticals, and searching. No overlapping functionality.

Naming Consistency5/5

All tools follow a consistent snake_case verb_noun pattern: get_prompt, list_verticals, search_prompts.

Tool Count5/5

3 tools is well-scoped for a prompt library, covering the essential operations of listing, searching, and retrieving.

Completeness4/5

Covers key operations for a read-only library. Missing a tool to list prompts within a vertical, but search_prompts with license scoping partially addresses this.

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