Which integrations are available for this server?

Provides persistent storage for conversations and generated tools, with vector search support for semantic tool discovery.

How do I use Universal Adapter?

1. Click on "Deploy Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@Universal Adapter Scrape the latest news from HackerNews and summarize it" That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

Universal Adapter

by shnkreddy98

Overview Schema Source Related Servers Discussions Admin

Python

Hybrid

YC Hack 2 - Serverless MCP Marketplace

Overview

An autonomous AI agent that can dynamically generate and execute tools by searching API documentation, creating Python code, and running it on-demand. Built for hackathons - no database setup required, works out of the box with just API keys.

Related MCP server: AgentMCP

Key Features

🤖 Fully Autonomous: Give it a task, it completes it end-to-end without user intervention
🔧 Dynamic Tool Generation: Searches for API docs, generates working Python code, and executes it
📁 File Operations: Can read, write, and manage files in the artifacts/ directory
🔄 Auto-Reload: Generated tools are immediately available in the same conversation
💾 MongoDB Storage: All conversations and tools stored in MongoDB for persistence
🌐 Web Integration: Built-in web search, scraping, and crawling via Firecrawl

Quick Start

1. Prerequisites

Python 3.13+
uv package manager
OpenRouter API key (get one here)
Firecrawl API key (get one here)

2. Installation

# Clone the repository
git clone <your-repo-url>
cd yc-hack2

# Install dependencies
uv sync

3. Configuration

Create a dev.env file in the project root:

cp .env.example dev.env

Edit dev.env and add your API keys:

# Required
OPENROUTER_API_KEY=your_openrouter_api_key_here
FIRECRAWL_API_KEY=your_firecrawl_api_key_here

# Optional - for semantic tool search (app works without it)
VOYAGE_API_KEY=your_voyage_api_key_here

# Optional - defaults to localhost:27017 if not set
MONGODB_URI=mongodb://localhost:27017/agent_db

4. Run the Agent

Pass your question as a command-line argument:

uv run main.py "Download an image of a cat and save it to the cat directory"

Or try other examples:

# Get real-time data
uv run main.py "Get me the real-time stream flow data for the Mississippi River"

# Fetch weather data
uv run main.py "Get current weather for Austin, TX and save it to a file"

# Download and process content
uv run main.py "Scrape the latest news from HackerNews and summarize it"

The agent will autonomously:

Search for relevant API documentation
Scrape the API specs
Generate a Python tool with working code
Execute the tool to complete your request
Save results to the artifacts/ directory

MongoDB Storage

The application uses MongoDB to store:

Conversations: Complete conversation history with all messages, tool calls, and results
Tools: Generated tool definitions with executable Python code and vector embeddings

Vector Search for Tools

The system uses vector embeddings to intelligently search through the tool marketplace. Instead of showing the agent all 1000+ tools (which would confuse it), it uses semantic search to find the top 10 most relevant tools for each task.

How it works:

When a tool is saved, the system generates a vector embedding from its name and description
Embeddings are generated using Voyage AI's voyage-4 model (1024 dimensions)
When searching, the query is embedded and compared using cosine similarity
The top 10 most semantically similar tools are returned

Benefits:

Agent sees only relevant tools, reducing confusion
Faster tool discovery with semantic understanding
Scales to thousands of tools without performance issues
More accurate than keyword matching

Local Development with MongoDB

If running locally (without Docker), you'll need MongoDB installed:

# Install MongoDB (macOS)
brew install mongodb-community

# Start MongoDB
brew services start mongodb-community

# Set MongoDB URI in dev.env
MONGODB_URI=mongodb://localhost:27017/agent_db

Note: When using Docker Compose, MongoDB is automatically set up and configured.

MongoDB Atlas Vector Search (Optional)

For production deployments, you can use MongoDB Atlas with native vector search:

Create a free MongoDB Atlas cluster at https://www.mongodb.com/cloud/atlas

Create a vector search index on the tools collection:

{
  "fields": [
    {
      "type": "vector",
      "path": "embedding",
      "numDimensions": 1024,
      "similarity": "cosine"
    }
  ]
}

Update MONGODB_URI in your dev.env to use Atlas connection string

With Atlas Vector Search, queries will use MongoDB's optimized Hierarchical Navigable Small Worlds algorithm for even faster similarity search.

Architecture

Core Components

LLM Agent (services/llm.py)
- OpenRouter-powered (Claude Haiku 4.5 by default)
- Autonomous workflow: search → scrape → generate → execute
- Multi-turn tool calling with auto-reload
- Conversation logging and error recovery
Base Tools (services/tools.py)
- file_read/write/list: File operations in artifacts/ directory
- firecrawl_search: Search the web for API docs
- firecrawl_scrape: Extract content from webpages
- firecrawl_crawl: Crawl entire websites
- generate_tool: Create executable Python tools
Tool Execution
- Generated tools stored in MongoDB
- Code executed using Python's exec() with base64 support
- Auto-reload makes new tools immediately available
- Supports both text and binary file operations
Storage
- MongoDB: Conversations and tool definitions
- artifacts/ - Agent's workspace for all file operations
- logs/ - Application logs

How It Works

Example: "Download an image of a cat"

1. Agent searches for image APIs
   → firecrawl_search("cat image API download")

2. Agent scrapes API documentation
   → firecrawl_scrape("https://thecatapi.com/docs")

3. Agent generates a download tool
   → generate_tool(name="download_cat_image", code="...", ...)
   → Tool auto-reloads, immediately available

4. Agent executes the new tool
   → download_cat_image(output_path="cat/image.jpg")

5. Agent saves the result
   → file_write("cat/image.jpg", base64_data, mode="wb")

6. Done! Image saved to artifacts/cat/image.jpg

All of this happens autonomously in one execution, no user intervention required.

API Server

Run as a REST API

Start the FastAPI server:

uv run server.py

The API will be available at http://localhost:8001 (default)

Interactive API docs: http://localhost:8001/docs

Change port: Set the PORT environment variable:

PORT=9000 uv run server.py

API Endpoints

POST /agent

Full-featured endpoint with all options:

curl -X POST http://localhost:8001/agent \
  -H "Content-Type: application/json" \
  -d '{
    "prompt": "Get the current Bitcoin price",
    "max_iterations": 25,
    "model": "anthropic/claude-haiku-4.5",
    "save_conversation": true
  }'

POST /agent/simple

Simplified endpoint with query parameters:

curl -X POST "http://localhost:8001/agent/simple?prompt=Get%20Bitcoin%20price&max_iterations=10"

GET /health

Health check:

curl http://localhost:8001/health

Example Response

{
  "success": true,
  "output": "The current Bitcoin price is $45,234.56 USD...",
  "usage": {
    "prompt_tokens": 1234,
    "completion_tokens": 567,
    "total_tokens": 1801
  },
  "error": null
}

MCP Server (Local Access)

Access the Universal Adapter via MCP (Model Context Protocol) for use with Claude Desktop and other local MCP clients.

Architecture

Two access methods:

Local (MCP): mcp_server.py - stdio transport for Claude Desktop
Remote (HTTP): server.py - FastAPI REST API for web access

Both use the same Agent with all marketplace tools.

Claude Desktop Setup

The MCP server exposes your agent as a single chat tool. The agent internally has access to all marketplace tools.

1. Configure Claude Desktop:

Add to your config file:

macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
Windows: %APPDATA%\Claude\claude_desktop_config.json
Linux: ~/.config/Claude/claude_desktop_config.json

{
  "mcpServers": {
    "universal-adapter": {
      "command": "uv",
      "args": ["run", "mcp_server.py"],
      "cwd": "/path/to/yc-hack2",
      "env": {
        "OPENROUTER_API_KEY": "your-key",
        "FIRECRAWL_API_KEY": "your-key",
        "MONGODB_URI": "your-mongodb-uri",
        "VOYAGE_API_KEY": "your-key"
      }
    }
  }
}

2. Restart Claude Desktop - The Universal Adapter tools will appear!

Available MCP Tools

chat - Chat with the agent (primary tool)
list_marketplace_tools - Browse marketplace
health - Health check

How It Works

Claude Desktop → MCP (stdio) → chat(message) → Agent → Tools → Response

The agent handles all tool orchestration internally (search, generate, execute).

For remote access via HTTP, use server.py instead (see API Server section).

Documentation

See docs/MCP_SETUP.md for detailed setup instructions and troubleshooting.

Tool Marketplace API

The server includes endpoints for browsing and searching the tool marketplace.

List All Tools

# Get all tools with pagination
curl http://localhost:8001/tools?limit=50&skip=0

# Response
[
  {
    "name": "get_bitcoin_price",
    "description": "Fetches the current Bitcoin price from CoinGecko API...",
    "parameters": {...},
    "code": "async def get_bitcoin_price()...",
    "created_at": "2026-01-31T08:52:52.949000"
  }
]

Search Tools (Vector Similarity)

# Search for tools using semantic search
curl "http://localhost:8001/tools/search?q=cryptocurrency%20price&limit=10"

# Response
{
  "query": "cryptocurrency price",
  "count": 1,
  "tools": [
    {
      "name": "get_bitcoin_price",
      "description": "Fetches the current Bitcoin price...",
      "similarity_score": 0.485,
      "parameters": {...}
    }
  ]
}

Get Specific Tool

# Get complete tool definition
curl http://localhost:8001/tools/get_bitcoin_price

# Response includes full code
{
  "name": "get_bitcoin_price",
  "description": "...",
  "parameters": {...},
  "code": "import httpx\n\nasync def get_bitcoin_price()..."
}

Delete Tool

# Remove a tool from marketplace
curl -X DELETE http://localhost:8001/tools/get_bitcoin_price

# Response
{
  "success": true,
  "message": "Tool 'get_bitcoin_price' deleted successfully"
}

List Conversations

# Get recent conversations
curl http://localhost:8001/conversations?limit=20&skip=0

# Response
[
  {
    "id": "697dc2e45e09a3756310636a",
    "conversation_id": "20260131_085206",
    "start_time": "2026-01-31T08:52:06.862134",
    "model": "anthropic/claude-haiku-4.5",
    "final_output": "I successfully fetched the current Bitcoin price..."
  }
]

Get Specific Conversation

# Get full conversation with all messages
curl http://localhost:8001/conversations/697dc2e45e09a3756310636a

# Returns complete conversation including all tool calls and results

Advanced Usage

Using the API Module

Import and use the agent programmatically in your Python scripts:

from services.api import ask, run_agent_sync, run_agent

# Quick usage - returns string output
output = ask("Get the current Bitcoin price")
print(output)

# Full control - returns structured response
result = run_agent_sync(
    prompt="Download a cat image",
    max_iterations=20,
    model="anthropic/claude-haiku-4.5"
)

if result.success:
    print(result.output)
    print(f"Tokens used: {result.usage}")
else:
    print(f"Error: {result.error}")

# Async usage
import asyncio
result = await run_agent("Your question here")

Running Different Tasks via CLI

Simply pass different questions as command-line arguments:

# API data retrieval
uv run main.py "Fetch Bitcoin price from CoinGecko API"

# Web scraping and analysis
uv run main.py "Find and download the top 5 Python repos on GitHub"

# Data processing
uv run main.py "Get COVID-19 statistics and create a summary report"

# Image/file operations
uv run main.py "Download the Eiffel Tower image and save it"

The agent will automatically:

Search for relevant APIs
Generate tools to interact with them
Execute the tools
Save results to artifacts/

Modify Behavior

To change max iterations or other settings, edit main.py:

# Increase iterations for complex tasks
res = await agent.run(question, max_iterations=50)

# Change the model
agent = Agent(model="anthropic/claude-opus-4.5")

# Disable conversation logging
agent = Agent(save_conversations=False)

Configuration

Environment Variables

Variable	Required	Description
`OPENROUTER_API_KEY`	Yes	API key from OpenRouter.ai
`FIRECRAWL_API_KEY`	Yes	API key from Firecrawl.dev
`VOYAGE_API_KEY`	Optional	API key from Voyage AI for semantic tool search embeddings. App works without it (falls back to zero-vector).
`MONGODB_URI`	Optional	MongoDB connection string. Defaults to `mongodb://localhost:27017/agent_db` if not set. Required for production deployments.
`MCP_PORT`	Optional	Port for MCP server (default: 8002)
`MCP_HOST`	Optional	Host for MCP server (default: 0.0.0.0)

Agent Configuration

Customize the agent in services/llm.py:

agent = Agent(
    model="anthropic/claude-haiku-4.5",  # Model to use
    save_conversations=True,              # Save conversation logs
    firecrawl_api_key="...",             # Optional: override env var
)

Project Structure

yc-hack2/
├── main.py                      # CLI entry point
├── server.py                    # FastAPI server
├── docker-compose.yml           # Docker setup with MongoDB
├── Dockerfile                   # Container image
├── dev.env                      # Your API keys (gitignored)
├── .env.example                 # Template for dev.env
├── services/
│   ├── api.py                  # API interface module
│   ├── llm.py                  # Agent with autonomous workflow
│   ├── tools.py                # Base tools + tool execution
│   ├── db.py                   # MongoDB client and operations
│   ├── env.py                  # Environment variables
│   └── logging.py              # Logging configuration
├── artifacts/                   # Agent's file workspace (gitignored)
├── logs/                       # Application logs (gitignored)
└── understand/                 # Reference documentation (temporary)

Troubleshooting

"No module named 'services'"

Run uv sync to install dependencies.

"OPENROUTER_API_KEY not found"

Create dev.env file with your API keys (see .env.example).

Firecrawl Timeout Errors

Normal for large documentation pages. The agent handles these gracefully and continues with alternate URLs.

Agent Hits Max Iterations

Increase max_iterations in main.py for complex tasks.

Documentation

The understand/ directory contains reference documentation:

architecture.md - System architecture and components
data-models.md - Data structures and schemas
flows.md - Workflow diagrams and sequences
implementation-plan.md - Development roadmap
prompts-reference.md - System prompts and examples

Docker Deployment

Build and Run with Docker

# Build the image
docker build -t autonomous-agent .

# Run with environment variables
docker run -d \
  -p 8001:8001 \
  -e OPENROUTER_API_KEY=your_key \
  -e FIRECRAWL_API_KEY=your_key \
  --name agent \
  autonomous-agent

# Check logs
docker logs -f agent

# Stop
docker stop agent

Using Docker Compose

Create a dev.env file with your API keys (see .env.example)
Start the service:

docker-compose up -d

View logs:

docker-compose logs -f

Stop the service:

docker-compose down

The agent will be available at http://localhost:8001

Docker Features

✅ MongoDB database included
✅ Health checks configured
✅ Auto-restart on failure
✅ Persistent volumes for artifacts and MongoDB data
✅ Optimized layer caching
✅ Non-blocking unbuffered output

Deploy to Railway

Connect your repo to Railway and use the Dockerfile (or Nixpacks; a Procfile is included).
Set environment variables in the Railway dashboard (no dev.env in deploy):
- OPENROUTER_API_KEY (required for chat)
- FIRECRAWL_API_KEY (required for web search/scrape)
- MONGODB_URI (required; use MongoDB Atlas or Railway MongoDB)
- VOYAGE_API_KEY (optional; for semantic tool search; app runs without it)
PORT is set by Railway; the app binds to 0.0.0.0:$PORT.
Health: Railway can probe / or /health; both return 200 when the app is up.

If you see "Application failed to respond", check deploy logs for startup errors (e.g. missing MONGODB_URI or invalid keys) and ensure all required env vars are set.

Contributing

This is a hackathon project. Feel free to extend it with:

MongoDB integration for tool storage
Enhanced sandboxing for code execution
Tool versioning and marketplace features
Multi-agent collaboration
Web UI for agent interaction

License

MIT

Available Tools

3 tools

chatA

Chat with the Universal Adapter agent.

The agent has access to all marketplace tools including:

File operations (read, write, list)
Web scraping (firecrawl search, scrape, crawl)
Tool generation (generate new tools from API docs)
Tool search (semantic search of marketplace)
All dynamically generated tools (crypto prices, APIs, etc.)

Args: message: Your question or command conversation_id: Optional conversation ID for multi-turn chat model: LLM model to use (default: claude-haiku-4.5) max_iterations: Maximum agent iterations (default: 25)

Returns: dict: Response with agent output, conversation_id, usage stats

ParametersJSON Schema

Name	Required	Default
`model`	No	anthropic/claude-haiku-4.5
`message`	Yes
`max_iterations`	No
`conversation_id`	No

TDQS

A4.2/5.0

Behavior3/5

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

There are no annotations, so the description carries full burden. It does disclose the agent's capabilities (including write operations and web scraping), which hints at side effects, and it specifies the return dict. However, it does not explicitly warn that chat interactions can trigger file modifications or external scrapes, nor does it mention authentication, rate limits, or cancellation behavior. This partial disclosure leaves significant gaps.

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 well-structured with a purpose sentence, a bulleted capability list, an Args block, and a Returns line. All sections serve to inform the agent. The capability list is somewhat extensive but relevant given the tool's broad scope; it could be trimmed slightly to focus only on the most consequential behaviors.

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

Completeness4/5

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

Given the tool's complexity (a chat agent with access to all marketplace tools) and the absence of an output schema, the description provides a solid overview: what it does, what capabilities the agent has, parameter semantics, and return shape. It lacks operational details like typical response latency, error patterns, or side-effect warnings, but it is sufficient for basic selection and invocation.

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 the description compensates fully with an Args section explaining each parameter: message ('Your question or command'), conversation_id ('Optional conversation ID for multi-turn chat'), model ('LLM model to use'), and max_iterations ('Maximum agent iterations'). This adds meaning beyond the schema's bare titles and defaults.

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 'Chat with the Universal Adapter agent' with a specific verb and resource, and follows with a detailed list of the agent's capabilities (file ops, web scraping, tool generation, tool search). This differentiates it from sibling tools like list_marketplace_tools and health, which serve distinct purposes.

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?

The description implies usage by explaining the agent's broad access to marketplace tools, suggesting it's the general-purpose interface for leveraging those tools. However, it does not explicitly state when to choose chat over directly invoking tools, nor does it provide exclusions or alternatives. The context of being 'chat with the agent' is clear, but no explicit guidance on alternatives is given.

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

healthA

Check if the MCP server is healthy

ParametersJSON Schema

Name	Required	Description	Default
No parameters

TDQS

A3.7/5.0

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 merely restates the tool's name ('health' -> 'check if healthy') without revealing side effects, return format, or what 'healthy' means. It implies read-only but does not explicitly state it, and gives no information about the response structure.

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, succinct sentence that exactly captures the tool's purpose. It is front-loaded and contains no filler words.

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?

The tool is extremely simple, but it has no output schema and no annotations, so the description must compensate. It tells the agent the action but not what to expect in the response (e.g., status codes, JSON structure, or error behavior). For a health check, this is a moderate gap that keeps the score at a minimally viable 3.

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?

The tool has zero parameters, so no parameter description is necessary. The baseline of 4 applies because there are no parameters to document and the description adds no semantic confusion.

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's purpose: 'Check if the MCP server is healthy.' The verb 'check' and the resource 'MCP server' make the action and scope explicit, and it is easily distinguishable from the sibling tools 'chat' and 'list_marketplace_tools'.

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

Usage Guidelines3/5

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

The usage is implied by the description: one would call this tool to verify server health. However, there is no explicit guidance on when to use it versus any alternative, no exclusions, and no context about prerequisites or typical invocation scenarios.

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

list_marketplace_toolsA

List available tools in the marketplace.

Args: limit: Maximum number of tools to return

Returns: dict: List of tools with names and descriptions

ParametersJSON Schema

Name	Required	Description	Default
`limit`	No

TDQS

A3.9/5.0

Behavior3/5

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

With no annotations, the description carries the behavioral burden. It implies a read-only operation via 'List' and specifies the return type, but it does not mention permissions, potential side effects, or limitations beyond the 'limit' parameter. The return description adds some transparency, but lacks depth.

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 concise and well-structured: a one-line purpose statement followed by explicitly labeled Args and Returns. Every sentence contributes meaning, and the purpose is front-loaded for quick comprehension.

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

Completeness4/5

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

For a simple tool with one optional parameter and no output schema, the description provides adequate context: it explains the return format (dict with names/descriptions) and the meaning of the limit. No additional complexity requires further explanation, though it could mention any default behavior or pagination if applicable.

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?

The description adds clear semantic meaning to the 'limit' parameter ('Maximum number of tools to return'), which is absent from the input schema. This fully compensates for the 0% schema description coverage, making the parameter's intent unmistakable.

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's purpose with a specific verb ('List') and resource ('available tools in the marketplace'), distinguishing it from sibling tools like 'chat' and 'health' which serve completely different functions.

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 is provided on when to use this tool versus alternatives, or any prerequisites or conditions. The description simply states what the tool does without contextual cues about appropriate usage scenarios.

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.

3 tool updatesv0.1.0Aug 10, 2026
- First observedchat
- First observedhealth
- First observedlist_marketplace_tools

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

A3.9/5.0

Scored 2026-08-10 07:00 across 3 tools

Disambiguation5/5

The three tools serve completely distinct purposes: interactive chat, listing marketplace tools, and health checking. There is no overlap or ambiguity between them.

Naming Consistency3/5

Naming is a mix of a bare verb (chat), a noun (health), and a verb_noun phrase (list_marketplace_tools). This is not a consistent pattern, though all names are lowercase with underscores and are readable.

Tool Count5/5

Three tools is an appropriate scope for an adapter server whose primary interface is a chat agent. Each tool has a clear role and none feels superfluous.

Completeness4/5

The chat tool exposes broad marketplace capabilities, and list_marketplace_tools provides discovery. A minor gap is the lack of a direct get-tool-details endpoint, but this is not critical since chat can retrieve such information.

Maintenance

ActivityInactive

ResponsivenessNo issues

Resources

GitHub Repository

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Updated a month ago (2026-08-12 21:12 UTC)
1
AGPL 3.0
Blogger Agent MCP
Agent Orchestration AI & Machine Learning Workplace & Productivity
googyaipro
F
license
Not graded
quality
C
maintenance
Enables automated multi-agent content generation, trend analysis, and travel route planning with live web search, Google Maps integration, and publishing to Google Docs and Gmail.
Updated 19 days ago (2026-08-25 10:04 UTC)
-
Desktop Commander MCP
File Systems Command Line OS Automation
temporary111111
A
license
Not graded
quality
C
maintenance
Enables AI assistants to search, update, and manage files, run terminal commands, and automate workflows on a user's computer.
Updated 22 days ago (2026-08-22 17:56 UTC)
95,635 npm
MIT

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File Write and Web Search Tools

YC Hack 2 - Serverless MCP Marketplace

Overview

Key Features

Quick Start

1. Prerequisites

2. Installation

3. Configuration

4. Run the Agent

MongoDB Storage

Vector Search for Tools

Local Development with MongoDB

MongoDB Atlas Vector Search (Optional)

Architecture

Core Components

How It Works

Example: "Download an image of a cat"

API Server

Run as a REST API

API Endpoints

POST /agent

POST /agent/simple

GET /health

Example Response

MCP Server (Local Access)

Architecture

Claude Desktop Setup

Available MCP Tools

How It Works

Documentation

Tool Marketplace API

List All Tools

Search Tools (Vector Similarity)

Get Specific Tool

Delete Tool

List Conversations

Get Specific Conversation

Advanced Usage

Using the API Module

Running Different Tasks via CLI

Modify Behavior

Configuration

Environment Variables

Agent Configuration

Project Structure

Troubleshooting

"No module named 'services'"

"OPENROUTER_API_KEY not found"

Firecrawl Timeout Errors

Agent Hits Max Iterations

Documentation

Docker Deployment

Build and Run with Docker

Using Docker Compose

Docker Features

Deploy to Railway

Contributing

License

Available Tools

TDQS

TDQS

TDQS

Tool Schema Changelog

TDQS

Maintenance

Resources

Related MCP Connectors

Related MCP Servers

UCM MCP Serverofficial

AgentMCP

Blogger Agent MCP

Desktop Commander MCP

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