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vlsky2603

thedailyworkflow-mcp

by vlsky2603

search_mcp_servers

Search a curated catalog of over 3,500 MCP servers to find the right tool for your workflow. Filter by category, client, or language to narrow results.

Instructions

Search the catalog of 3500+ MCP servers at thedailyworkflow.com.

Args: query: Free-text search (matches name, description, keywords). Example: "notion", "postgres database". category: Filter by category. Common values: databases, web, filesystem, cloud, productivity, communication, dev, ai, search, monitoring. client: Filter by supported MCP client. Values: claude-desktop, cursor, cline, windsurf, continue, goose. language: Filter by implementation language. Values: typescript, python, go, rust, java. limit: Max results to return (1-25, default 10).

Returns: Dict with count and results (list of MCP server summaries). Each result has: slug, name, description, categories, language, transport, supported_clients, stars, repo_url, page_url (link to full info).

Use this when the user asks for an MCP server matching some need ("find me an MCP for X", "what MCP servers work with Cursor", "Slack MCP servers"). After choosing a candidate, call get_mcp_install_config to get ready-to-paste setup.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryNo
categoryNo
clientNo
languageNo
limitNo
Behavior4/5

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

No annotations exist, so description carries full burden. It discloses behavior: returns dict with count and results, lists result fields. Provides parameter examples. Does not mention destructive actions or auth, but for a search tool this is adequate.

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?

Well-structured with clear sections: purpose, Args with examples, Returns, usage context. Every sentence adds value. No redundancy. Appropriate length for a search tool with 5 parameters.

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?

Given 5 parameters, no enums, and no output schema, the description is highly complete. Covers all parameters, return format, and usage context. Examples for query, category values, and client values are provided. Missing only edge case details but overall excellent.

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 description coverage is 0%, but description adds extensive meaning: explains each parameter with examples, lists common values for category, client, language, and specifies default for limit. This far exceeds minimal requirement.

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 it searches a catalog of 3500+ MCP servers at thedailyworkflow.com. It uses specific verb 'search' and resource 'catalog of MCP servers', distinguishing it from sibling tools like search_ai_tools or search_pipelines.

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: 'when the user asks for an MCP server matching some need' and gives examples. Provides post-search action: 'call get_mcp_install_config'. Does not explicitly state when not to use, but context 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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