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vlsky2603

thedailyworkflow-mcp

by vlsky2603

search_ai_tools

Search a directory of 15,000 AI tools to find one for your task. Use filters like category, pricing, or free-text query.

Instructions

Search 15000+ AI tools in the thedailyworkflow.com catalog.

Args: query: Free-text search (matches name, description, tags). Example: "image generation", "transcription", "chatbot". category: Filter by category. Examples: "Image Generation", "Productivity", "Code & Development", "Audio & Music", "Writing & Content". pricing: Filter by pricing model. Values: "Free", "Freemium", "Paid", "Enterprise". limit: Max results (1-25, default 10).

Returns: Dict with count and results. Each result: slug, name, category, description, pricing, logo_url, official_url, page_url.

Use this when the user asks for an AI tool for a specific task ("find me an AI for X", "what are good free AI tools for Y", "compare AI tools for Z"). Follow up with get_ai_tool_details for the chosen one.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryNo
categoryNo
pricingNo
limitNo
Behavior4/5

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

No annotations provided, but description details return format (dict with count and results, each result fields) and search behavior. It lacks mention of auth or rate limits, but for a search tool this is sufficient.

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?

Description is concise, front-loaded with purpose, then structured Args and Returns sections. No irrelevant sentences.

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

Completeness5/5

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

Despite no output schema, description covers all aspects: parameters, return format, and usage guidance with sibling relation. Complete for its complexity.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 0%, but description adds meaning for all 4 parameters: query with example, category with examples, pricing with values, limit with range and default. This fully compensates for schema gaps.

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 clearly states the tool searches 15000+ AI tools in a specific catalog, with examples and a specific verb (search). It distinguishes from sibling get_ai_tool_details by advising follow-up usage.

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?

Explicitly says when to use the tool (user asks for AI tool for a specific task) and suggests follow-up with get_ai_tool_details. No explicit when-not, but guidance is clear.

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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