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prompts.chat MCP Server

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JavaScript
Hybrid

Features

  • MCP Prompts - Browse and use prompts directly via MCP prompts capability

  • Search Prompts - Search for AI prompts by keyword, category, or tag

  • Get Prompt - Retrieve prompt details by ID with variable substitution

  • Variable Support - Prompts with ${variable} syntax are automatically handled

Related MCP server: Prompt Bookmarks

🛠️ Installation

Requirements

  • Node.js >= v18.0.0

  • An MCP-compatible client (Cursor, Windsurf, VS Code, Claude Code, etc.)

Add this to your Cursor MCP config file (~/.cursor/mcp.json):

Remote Server (Recommended)

{
  "mcpServers": {
    "prompts-chat": {
      "url": "https://prompts.chat/api/mcp"
    }
  }
}

Local Server

{
  "mcpServers": {
    "prompts-chat": {
      "command": "npx",
      "args": ["-y", "@fkadev/prompts.chat-mcp"]
    }
  }
}

Add this to your Windsurf MCP config file:

Remote Server (Recommended)

{
  "mcpServers": {
    "prompts-chat": {
      "serverUrl": "https://prompts.chat/api/mcp"
    }
  }
}

Local Server

{
  "mcpServers": {
    "prompts-chat": {
      "command": "npx",
      "args": ["-y", "@fkadev/prompts.chat-mcp"]
    }
  }
}

Add this to your VS Code MCP settings:

Remote Server (Recommended)

"mcp": {
  "servers": {
    "prompts-chat": {
      "type": "http",
      "url": "https://prompts.chat/api/mcp"
    }
  }
}

Local Server

"mcp": {
  "servers": {
    "prompts-chat": {
      "type": "stdio",
      "command": "npx",
      "args": ["-y", "@fkadev/prompts.chat-mcp"]
    }
  }
}

Remote Server (Recommended)

claude mcp add --transport http prompts-chat https://prompts.chat/api/mcp

Local Server

claude mcp add prompts-chat -- npx -y @fkadev/prompts.chat-mcp

Add this to your Zed settings.json:

{
  "context_servers": {
    "prompts-chat": {
      "command": {
        "path": "npx",
        "args": ["-y", "@fkadev/prompts.chat-mcp"]
      }
    }
  }
}

⚙️ Configuration

The local server supports the following environment variables:

Variable

Description

PROMPTS_API_KEY

Optional API key for authenticated requests

PROMPTS_QUERY

Optional query string to filter prompts (e.g., users=a,b&categories=c,d&tags=e,f)

Example with Environment Variables

{
  "mcpServers": {
    "prompts-chat": {
      "command": "npx",
      "args": ["-y", "@fkadev/prompts.chat-mcp"],
      "env": {
        "PROMPTS_API_KEY": "your-api-key",
        "PROMPTS_QUERY": "users=username&categories=coding&tags=productivity"
      }
    }
  }
}

🔨 Available Tools

Tool

Description

search_prompts

Search for AI prompts by keyword. Supports filtering by type, category, and tag.

get_prompt

Get a prompt by ID. Supports variable elicitation for prompts with template variables.

save_prompt

Save a new prompt to your account. Requires PROMPTS_API_KEY.

📚 MCP Prompts

This server exposes all public prompts from prompts.chat as MCP prompts. Use prompts/list to browse available prompts and prompts/get to retrieve them with variable substitution.

📖 Example Usage

Ask your AI assistant:

Search for prompts about code review
Get the prompt for "act as a linux terminal"

🔗 Links

📄 License

ISC

Available Tools

2 tools
get_promptGet PromptB

Get a prompt by ID. If the prompt contains template variables (like ${variable} or ${variable:default}), you may need to provide values for them.

ParametersJSON Schema
NameRequiredDescriptionDefault
idYesThe ID of the prompt to retrieve

TDQS

B3.2/5.0
Behavior3/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions that prompts may contain template variables requiring values, which adds useful context about potential input needs. However, it lacks details on permissions, error handling, or response format, leaving gaps in behavioral understanding.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise and front-loaded, stating the core purpose in the first sentence. The second sentence adds relevant context about template variables without unnecessary elaboration, making it efficient and well-structured.

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 the simple tool with one parameter and no output schema or annotations, the description is adequate but incomplete. It covers the basic operation and hints at template variables, but lacks details on return values, error cases, or integration with sibling tools, leaving room for improvement in context.

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?

The input schema has 100% description coverage, with the 'id' parameter clearly documented. The description adds no additional parameter semantics beyond what the schema provides, such as format examples or constraints, so it meets the baseline for high schema coverage without adding value.

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 the tool's purpose with a specific verb ('Get') and resource ('prompt by ID'), making it easy to understand what it does. However, it doesn't explicitly differentiate from its sibling 'search_prompts', which likely searches for prompts rather than retrieving a specific one by ID.

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?

The description provides no guidance on when to use this tool versus alternatives like 'search_prompts'. It mentions template variables but doesn't clarify if this is a prerequisite or when to choose this over searching, leaving the agent without explicit usage context.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

search_promptsSearch PromptsC

Search for AI prompts by keyword. Returns matching prompts with title, description, content, author, category, and tags.

ParametersJSON Schema
NameRequiredDescriptionDefault
queryYesSearch query to find relevant prompts
limitNoMaximum number of prompts to return (default 10, max 50)
typeNoFilter by prompt type
categoryNoFilter by category slug
tagNoFilter by tag slug

TDQS

C2.9/5.0
Behavior2/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions the return fields but doesn't cover critical aspects like pagination, rate limits, authentication needs, error handling, or whether results are sorted. This leaves significant gaps 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?

The description is a single, efficient sentence that front-loads the purpose and key return information. Every word earns its place with no redundancy or fluff, making it easy to parse quickly.

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

Completeness2/5

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

Given the complexity of a search tool with 5 parameters, no annotations, and no output schema, the description is incomplete. It doesn't address behavioral traits, usage context, or output structure beyond listing return fields, leaving the agent with insufficient guidance for effective tool selection.

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 the schema fully documents all 5 parameters. The description adds no additional parameter semantics beyond what's in the schema, such as search behavior or filter interactions. Baseline 3 is appropriate when schema does the heavy lifting.

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 the verb ('Search for') and resource ('AI prompts'), specifying the action and target. It distinguishes from the sibling 'get_prompt' by implying this is a search/filter operation rather than a direct retrieval, though it doesn't explicitly contrast them.

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?

The description provides no guidance on when to use this tool versus the sibling 'get_prompt' or other alternatives. It lacks context about prerequisites, exclusions, or specific scenarios where this search is preferred over direct retrieval.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections.

  1. 2 tool updates
    • First observedget_prompt
    • First observedsearch_prompts
Deploy Server
One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

TDQS

B3.2/5.0

Scored across 2 tools

Disambiguation5/5

The two tools have clearly distinct purposes: get_prompt retrieves a specific prompt by ID with potential variable handling, while search_prompts finds multiple prompts by keyword with metadata. There is no overlap or ambiguity between these operations.

Naming Consistency5/5

Both tools follow a consistent verb_noun pattern (get_prompt, search_prompts) with clear, descriptive names that align with their functions. The naming style is uniform and predictable.

Tool Count3/5

With only two tools, the server feels thin for a prompt management domain. While get and search are fundamental, typical CRUD operations like create, update, or delete prompts are missing, making the set feel incomplete for full prompt lifecycle management.

Completeness2/5

The toolset is severely incomplete for prompt management. It lacks essential operations such as creating, updating, or deleting prompts, which are core to managing a prompt repository. Agents will hit dead ends when trying to modify or add prompts.

Maintenance

ActivityInactive
ResponsivenessNo issues

Resources

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