Generate Server Card logo

Generate Server Card

Organization
agentic-community
generate-server-card

Generate an MCP server card JSON (mcp-gateway-registry format) by analyzing server source code in a folder or GitHub URL. Studies code to detect server name, tools, transport, auth, and generates a registry-compatible config.

Overview

Publisheragentic-community
Repositorymcp-gateway-registry
Skill namegenerate-server-card
Stars
969
Forks
244
Bundled files
Instructions only
LicenseApache-2.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by agentic-community on GitHub. Read the source before you install it.

Installation

Install the Generate Server Card AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/agentic-community/mcp-gateway-registry/tree/main/.claude/skills/generate-server-card
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/agentic-community/mcp-gateway-registry.git /tmp/mcp-gateway-registry
mkdir -p .claude/skills
cp -r /tmp/mcp-gateway-registry/.claude/skills/generate-server-card .claude/skills/generate-server-card
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Generate Server Card in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Generate Server Card on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Generate Server Card is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Generate MCP Server Card

Generate an MCP server card JSON file in the mcp-gateway-registry format by analyzing the source code at $ARGUMENTS.

This format is used by https://github.com/agentic-community/mcp-gateway-registry for registering MCP servers in the gateway registry.

Steps

1. Carefully study the source code

Do NOT just skim the top-level files. MCP servers can be complex, multi-file projects. You MUST thoroughly explore the entire folder structure before generating the card.

If $ARGUMENTS is a local folder path, use Glob to find ALL Python files, YAML/JSON configs, and README files in the folder and its subfolders. Read every relevant file.

If $ARGUMENTS is a GitHub URL, use WebFetch to read the raw file contents. Follow imports to discover additional files.

Where to look (MCP servers are not always a single file):

  • Entrypoints: main.py, app.py, server.py, __main__.py, mcp_server.py
  • Tool definitions: look for @mcp.tool(), @server.tool(), @tool, tool handler functions, tool registrations
  • OpenAPI specs: .json or .yaml files in openapi-specs/, specs/, api/ folders - these define the tools when an API is exposed as MCP
  • Config files: pyproject.toml, requirements.txt, .bedrock_agentcore.yaml, deploy_*.py, docker-compose.yml
  • Deployment scripts: these reveal the gateway URL, auth scheme, transport type
  • Gateway configs: .deployment_info.json, .roo.json - these have the actual MCP endpoint URL
  • Sub-folders: check ALL subdirectories for additional tools, resources, or shared utilities
  • Resource definitions: MCP servers can expose resources and prompts in addition to tools

From the code, detect:

  • Server name: from constants, config files, class names, gateway name
  • Description: from docstrings, README, module-level comments, OpenAPI spec info
  • Tools: from @tool decorators, tool registrations, OpenAPI operationIds, function definitions
  • Tool schemas: parameter types, required fields, descriptions from function signatures or OpenAPI specs
  • Supported transports: streamable-http, sse, stdio - from server framework config
  • Auth scheme: none, bearer JWT, API key, OAuth2, Cognito - from deployment scripts or middleware
  • MCP endpoint URL: from deployment info, gateway URL, or config files
  • Proxy pass URL: the backend URL the gateway proxies to (may differ from the MCP endpoint)
  • Version: from constants, pyproject.toml, or default to 1.0.0
  • Provider info: organization name and URL if detectable
  • License: from pyproject.toml or LICENSE file
  • Status: active, beta, etc.

2. Generate the server card JSON

Create a JSON file following the mcp-gateway-registry server config format.

Required fields
json
{
  "server_name": "string - Human-readable server name",
  "description": "string - What the server does",
  "path": "string - URL path prefix on the gateway, must start with /",
  "proxy_pass_url": "string - Backend URL to proxy to",
  "tags": ["string - Relevant tags for discovery"],
  "supported_transports": ["streamable-http"]
}
Recommended optional fields (include when detected)
json
{
  "auth_scheme": "string - none, bearer, api_key, oauth2",
  "mcp_endpoint": "string - Custom MCP endpoint URL if different from proxy_pass_url",
  "version": "string - Server version e.g. 1.0.0",
  "status": "string - active, beta, deprecated",
  "visibility": "string - public or private",
  "provider_organization": "string - Organization name",
  "provider_url": "string - Organization URL",
  "num_tools": 0,
  "license": "string - License identifier e.g. MIT, Apache-2.0",
  "metadata": {
    "category": "string - e.g. documentation, geolocation, utility",
    "official": false,
    "mcp_compatible": "1.0"
  },
  "tool_list": []
}

3. Do NOT populate tool_list

  • Always set "tool_list": [] (empty array)
  • Do NOT add individual tool entries to tool_list - leave it empty
  • Still count the tools and set num_tools to the correct count (for informational purposes)
  • The tool details are intentionally omitted from the server card

4. Set the path

  • The path must start with /
  • Use the gateway name or a short descriptive slug, e.g. /geo-mcp, /cloudflare-docs
  • Check .deployment_info.json for the gateway name if available

5. Set the proxy_pass_url and mcp_endpoint

  • Check for .deployment_info.json, .roo.json, or .bedrock_agentcore.yaml for the actual deployed URL
  • proxy_pass_url: the backend URL the gateway proxies to
  • mcp_endpoint: the full MCP endpoint URL that clients connect to (if different from proxy_pass_url)
  • If no deployment info found, use placeholder: https://<GATEWAY_URL>/mcp

6. Detect transport type

  • streamable-http: most common for remote MCP servers, HTTP-based
  • sse: Server-Sent Events based transport
  • stdio: local process communication (not for remote servers)
  • Default to ["streamable-http"] for gateway-deployed servers

7. Detect auth scheme

  • Cognito JWT / bearer token: "bearer" or describe as "custom_jwt"
  • API key: "api_key"
  • No auth: "none"
  • Check deployment scripts for authorizerType, customJWTAuthorizer, bearer token usage

8. Save the output

  • Name the file {server_name}_server_card.json using the detected server name in snake_case
  • Save it in the server's folder (same folder as the source code)
  • Pretty-print with 2-space indentation

9. Validate the generated JSON

After writing the file, validate it by running this Python script via Bash:

bash
python3 -c "
import json
import sys

file_path = '<OUTPUT_FILE_PATH>'

# Step 1: JSON format check
try:
    with open(file_path) as f:
        card = json.load(f)
    print('PASS: Valid JSON format')
except json.JSONDecodeError as e:
    print(f'FAIL: Invalid JSON - {e}')
    sys.exit(1)

# Step 2: Required fields
errors = []
REQUIRED = ['server_name', 'description', 'path', 'proxy_pass_url', 'tags', 'supported_transports']
for field in REQUIRED:
    if field not in card:
        errors.append(f'Missing required field: {field}')
    elif field in ('server_name', 'description', 'path', 'proxy_pass_url') and not isinstance(card[field], str):
        errors.append(f'{field} must be a string')
    elif field in ('tags', 'supported_transports') and not isinstance(card[field], list):
        errors.append(f'{field} must be an array')

# Step 3: Path must start with /
if 'path' in card and isinstance(card['path'], str) and not card['path'].startswith('/'):
    errors.append('path must start with /')

# Step 4: proxy_pass_url must be a valid URL
if 'proxy_pass_url' in card and isinstance(card['proxy_pass_url'], str):
    if not card['proxy_pass_url'].startswith(('http://', 'https://')):
        errors.append('proxy_pass_url must start with http:// or https://')

# Step 5: tags must be non-empty
if not card.get('tags'):
    errors.append('tags must have at least one entry')

# Step 6: supported_transports must be non-empty
if not card.get('supported_transports'):
    errors.append('supported_transports must have at least one entry')
valid_transports = ['streamable-http', 'sse', 'stdio']
for t in card.get('supported_transports', []):
    if t not in valid_transports:
        errors.append(f'Unknown transport: {t}, must be one of {valid_transports}')

# Step 7: Validate tool_list is an empty array
if 'tool_list' in card:
    if not isinstance(card['tool_list'], list):
        errors.append('tool_list must be an array')
    elif len(card['tool_list']) > 0:
        errors.append('tool_list must be empty ([]) - do not populate individual tool entries')

# Step 8: num_tools must be present and a non-negative integer
if 'num_tools' in card:
    if not isinstance(card['num_tools'], int) or card['num_tools'] < 0:
        errors.append('num_tools must be a non-negative integer')

# Step 9: Report results
if errors:
    print(f'FAIL: {len(errors)} validation error(s):')
    for e in errors:
        print(f'  - {e}')
    sys.exit(1)
else:
    tool_count = len(card.get('tool_list', []))
    transport_count = len(card.get('supported_transports', []))
    print(f'PASS: All required fields present ({tool_count} tool(s), {transport_count} transport(s))')
"

Replace <OUTPUT_FILE_PATH> with the actual path of the generated JSON file.

If validation fails, fix the errors in the JSON and re-run validation until all checks pass. Do NOT report success to the user until validation passes.

10. Report results

After validation passes, output results in EXACTLY this format:

Validation passed. Here's a summary of the generated server card:

Output file: <filename>.json

Detected from code:

- Server name: <ServerName> (from <how it was detected>)
- Tools: <count> - <tool_name> (<brief description>), ...
- Transport: <streamable-http|sse|stdio> (<why>)
- Auth: <none|bearer|api_key|oauth2> (<details>)
- MCP endpoint: <URL or placeholder>
- Proxy pass URL: <URL or placeholder>
- Status: <active|beta>
- Provider: <org name> or <not detected>

If validation fails, show the errors first, fix them, re-validate, and only show the summary above after all checks pass.

Reference: Minimal server card

json
{
  "server_name": "Minimal MCP Server",
  "description": "A minimal server configuration with only required fields",
  "path": "/minimal-server",
  "proxy_pass_url": "http://minimal-server:9001/",
  "supported_transports": ["streamable-http"],
  "tags": ["mcp", "minimal", "example"]
}

Reference: Full server card with tools

json
{
  "server_name": "Geolocation MCP Server",
  "description": "IP geolocation lookup via ipwho.is, deployed on Amazon Bedrock AgentCore Gateway with Cognito JWT auth",
  "path": "/geo-mcp",
  "proxy_pass_url": "https://geo-mcp-abc123.gateway.bedrock-agentcore.us-east-1.amazonaws.com/mcp",
  "supported_transports": ["streamable-http"],
  "auth_scheme": "bearer",
  "tags": ["geolocation", "ip-lookup", "aws", "bedrock-agentcore"],
  "num_tools": 1,
  "version": "1.0.0",
  "status": "active",
  "visibility": "public",
  "provider_organization": "AWS",
  "provider_url": "https://aws.amazon.com",
  "license": "MIT",
  "metadata": {
    "category": "geolocation",
    "official": false,
    "mcp_compatible": "1.0"
  },
  "tool_list": []
}

Frequently asked questions

What does the Generate Server Card AI skill do?

Generate an MCP server card JSON (mcp-gateway-registry format) by analyzing server source code in a folder or GitHub URL. Studies code to detect server name, tools, transport, auth, and generates a registry-compatible config.

Why use Generate Server Card on TypingMind?

Because you install it once and use it with any model. Generate Server Card is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Generate Server Card in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/agentic-community/mcp-gateway-registry/tree/main/.claude/skills/generate-server-card. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Generate Server Card?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Generate Server Card?

As many as you like. As long as a model supports skills, you can use Generate Server Card with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Generate Server Card AI skill free?

Yes. It is published on GitHub by agentic-community under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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