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vlsky2603

thedailyworkflow-mcp

by vlsky2603
OverviewSchemaRelated ServersScoreDiscussions
Python
Remote

search_pipelines

Search over 100 ready-made AI workflow pipelines to find step-by-step recipes for multi-tool AI tasks. Enter a goal like 'podcast' to discover pipelines with prompts and tool sequences.

Instructions

Search 100+ ready-made AI workflow pipelines (step-by-step recipes).

A pipeline is a complete plan: which AI tools to use in what order, with ready-to-paste prompts at each step, to accomplish a specific goal.

Args: query: Free-text search over pipeline titles and queries. Examples: "podcast", "children book", "youtube channel automation". lang: Language for output ("en" or "ru"). Default: "en". limit: Max results (1-25, default 10).

Returns: Dict with count and results. Each result: slug, title, goal, query, hits (popularity), step_count, page_url.

Use this when the user describes a goal that involves multiple steps with multiple AI tools ("how do I make X using AI", "give me a workflow for Y"). After picking, call get_pipeline_details for full step-by-step plan with prompts.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryNo
langNoen
limitNo

Tool Definition Quality

A4.7/5.0
Behavior4/5

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

Describes return format and structure, but lacks mention of error handling or side effects. Acceptable for a search tool.

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?

Front-loaded purpose, then args and returns. Each part adds value without redundancy.

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?

Covers purpose, usage, parameter semantics, and return format completely for an agent to use, despite no output schema.

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

Parameters4/5

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

Provides examples for query, explains lang and limit with defaults. Compensates well for 0% schema description coverage.

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?

Clearly states it searches ready-made AI workflow pipelines with examples. Distinguishes from sibling tools like get_pipeline_details and catalog_stats.

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?

Explicitly says when to use (multi-step AI goals) and when to switch to get_pipeline_details for full details.

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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