mcphero 2.0.0

Library to use MCP servers natively with AI clients as tools.

MCPHero - MCP as tools / MCP as functions

Library to use MCP as tools / functions in native AI libraries

Inspiration

Everyone uses MCP now, but many still use old-school AI clients with no MCP support. These client libraries like openai or google-genai only have tool/function calls support. This project is created to easily connect MCP servers to these libs as tools.

Concept

Two main flows:

1. list_tools - call the MCP server over http to get the tool definitions, then map them to AI library tool definitions
2. process_tool_calls - get the AI library's tool_calls, parse them, send the requests to mcp servers, return results

Installation

Base (no LLM SDK dependency):

pip install mcphero

For OpenAI support:

pip install "mcphero[openai]"

For Google Gemini support:

pip install "mcphero[google-genai]"

Quick Start

Generic (provider-agnostic)

Use MCPToolAdapter when your framework has its own tool-call loop, or when you just need raw MCP tool execution without any LLM SDK dependency.

import asyncio
from mcphero import MCPToolAdapter, GenericToolCall

async def main():
    adapter = MCPToolAdapter("https://api.mcphero.app/mcp/your-server-id")

    # Discover available tools
    tools = await adapter.discover_tools()
    for tool in tools:
        print(tool.name, tool.description)

    # Execute tool calls directly
    results = await adapter.process_tool_calls([
        GenericToolCall(name="get_weather", arguments={"city": "London"}, id="1"),
    ])
    for result in results:
        print(result.content)

asyncio.run(main())

Or call a single tool directly:

result = await adapter.call_tool("get_weather", {"city": "London"})

OpenAI

import asyncio
from openai import OpenAI
from mcphero import MCPToolAdapterOpenAI

async def main():
    adapter = MCPToolAdapterOpenAI("https://api.mcphero.app/mcp/your-server-id")
    client = OpenAI()

    # Get tool definitions
    tools = await adapter.get_tool_definitions()

    # Make request with tools
    messages = [{"role": "user", "content": "What's the weather in London?"}]
    response = client.chat.completions.create(
        model="gpt-4o",
        messages=messages,
        tools=tools,
    )

    # Process tool calls if present
    if response.choices[0].message.tool_calls:
        tool_results = await adapter.process_tool_calls(
            response.choices[0].message.tool_calls
        )

        # Continue conversation with results
        messages.append(response.choices[0].message)
        messages.extend(tool_results)

        final_response = client.chat.completions.create(
            model="gpt-4o",
            messages=messages,
            tools=tools,
        )
        print(final_response.choices[0].message.content)

asyncio.run(main())

Google Gemini

import asyncio
from google import genai
from google.genai import types
from mcphero import MCPToolAdapterGemini

async def main():
    adapter = MCPToolAdapterGemini("https://api.mcphero.app/mcp/your-server-id")
    client = genai.Client(api_key="your-api-key")

    # Get tool definitions
    tool = await adapter.get_tool()

    # Make request with tools
    response = client.models.generate_content(
        model="gemini-2.5-flash",
        contents="What's the weather in London?",
        config=types.GenerateContentConfig(
            tools=[tool],
            automatic_function_calling=types.AutomaticFunctionCallingConfig(
                disable=True
            ),
        ),
    )

    # Process function calls if present
    if response.function_calls:
        results = await adapter.process_function_calls(response.function_calls)

        # Continue conversation with results
        contents = [
            types.Content(role="user", parts=[types.Part.from_text("What's the weather in London?")]),
            response.candidates[0].content,
            *results,
        ]

        final_response = client.models.generate_content(
            model="gemini-2.5-flash",
            contents=contents,
            config=types.GenerateContentConfig(tools=[tool]),
        )
        print(final_response.text)

asyncio.run(main())

Multiple MCP Servers

Adapters natively support connecting to multiple MCP servers at once. Use MCPServerConfig to configure each server, then pass them as a list.

MCPServerConfig

from mcphero import MCPServerConfig

config = MCPServerConfig(
    url="https://api.mcphero.app/mcp/your-server-id",  # required
    name="weather",              # optional, auto-derived from URL if omitted
    timeout=30.0,                # optional, default 30s
    headers={                    # optional, auth headers for the server
        "Authorization": "Bearer your-token",
    },
    init_mode="auto",            # "auto" | "on_fail" | "none"
    tool_prefix="wx",            # optional, prefix for tool names from this server
)

Field	Type	Default	Description
url	str	required	HTTP endpoint of the MCP server
name	str | None	derived from URL	Identifier for the server (e.g. last path segment)
timeout	float	30.0	Request timeout in seconds
headers	dict[str, str] | None	None	Headers sent with every request (useful for auth)
init_mode	"auto" | "on_fail" | "none"	"auto"	When to run MCP initialization handshake
tool_prefix	str | None	None	Prefix applied to all tool names from this server

init_mode options:

• "auto" - initialize the connection before every request (default, safest)
• "on_fail" - skip initialization, but retry with initialization if a request fails
• "none" - never initialize (for servers that don't require it)

Multi-Server Example

import asyncio
from openai import OpenAI
from mcphero import MCPToolAdapterOpenAI, MCPServerConfig

async def main():
    adapter = MCPToolAdapterOpenAI([
        MCPServerConfig(
            url="https://api.mcphero.app/mcp/weather",
            name="weather",
            headers={"Authorization": "Bearer weather-token"},
        ),
        MCPServerConfig(
            url="https://api.mcphero.app/mcp/calendar",
            name="calendar",
            headers={"Authorization": "Bearer calendar-token"},
        ),
    ])

    client = OpenAI()

    # Tools from ALL servers are fetched in parallel and merged
    tools = await adapter.get_tool_definitions()

    messages = [{"role": "user", "content": "What's the weather today and what's on my calendar?"}]
    response = client.chat.completions.create(
        model="gpt-4o",
        messages=messages,
        tools=tools,
    )

    # Tool calls are automatically routed to the correct server
    if response.choices[0].message.tool_calls:
        results = await adapter.process_tool_calls(
            response.choices[0].message.tool_calls
        )
        messages.append(response.choices[0].message)
        messages.extend(results)

        final_response = client.chat.completions.create(
            model="gpt-4o",
            messages=messages,
            tools=tools,
        )
        print(final_response.choices[0].message.content)

asyncio.run(main())

Tool Name Collisions

When multiple servers expose tools with the same name, the adapter auto-prefixes them with the server name to avoid collisions:

# Both servers have a "search" tool
adapter = MCPToolAdapterOpenAI([
    MCPServerConfig(url="https://example.com/mcp/weather", name="weather"),
    MCPServerConfig(url="https://example.com/mcp/calendar", name="calendar"),
])

tools = await adapter.get_tool_definitions()
# "search" becomes "weather__search" and "calendar__search"

You can control this behavior:

# Custom separator
adapter = MCPToolAdapterOpenAI(configs, prefix_separator="-")
# "weather-search", "calendar-search"

# Disable auto-prefixing (will raise on collision)
adapter = MCPToolAdapterOpenAI(configs, auto_prefix_on_collision=False)

# Manual prefix via config (always applied, regardless of collisions)
MCPServerConfig(url="...", tool_prefix="wx")
# "wx__search"

API Reference

MCPToolAdapter

from mcphero import MCPToolAdapter, GenericToolCall

adapter = MCPToolAdapter("https://api.mcphero.app/mcp/your-server-id")

Methods

Method	Returns	Description
discover_tools()	list[MCPToolDefinition]	Discover tools with routing metadata
process_tool_calls(tool_calls, return_errors=True)	list[GenericToolResult]	Execute tool calls and return generic results
call_tool(name, arguments)	JsonRpcResponse	Call a single tool by name
initialize_all()	dict[str, JsonRpcResponse | Exception]	Pre-initialize all server connections

MCPToolAdapterOpenAI

from mcphero import MCPToolAdapterOpenAI, MCPServerConfig

# Single server (URL string)
adapter = MCPToolAdapterOpenAI("https://api.mcphero.app/mcp/your-server-id")

# Single server (config)
adapter = MCPToolAdapterOpenAI(
    MCPServerConfig(
        url="https://api.mcphero.app/mcp/your-server-id",
        headers={"Authorization": "Bearer ..."},
    )
)

# Multiple servers
adapter = MCPToolAdapterOpenAI([
    MCPServerConfig(url="https://server-a.com/mcp", name="a"),
    MCPServerConfig(url="https://server-b.com/mcp", name="b"),
])

Methods

Method	Returns	Description
get_tool_definitions()	list[ChatCompletionToolParam]	Fetch tools from MCP server(s) as OpenAI tool schemas
process_tool_calls(tool_calls, return_errors=True)	list[ChatCompletionToolMessageParam]	Execute tool calls and return results for the conversation
discover_tools()	list[MCPToolDefinition]	Low-level: discover tools with routing metadata
call_tool(name, arguments)	JsonRpcResponse	Low-level: call a single tool by name
initialize_all()	dict[str, JsonRpcResponse | Exception]	Pre-initialize all server connections

MCPToolAdapterGemini

from mcphero import MCPToolAdapterGemini, MCPServerConfig

# Same constructor options as OpenAI adapter
adapter = MCPToolAdapterGemini("https://api.mcphero.app/mcp/your-server-id")

Methods

Method	Returns	Description
get_function_declarations()	list[types.FunctionDeclaration]	Fetch tools as Gemini FunctionDeclaration objects
get_tool()	types.Tool	Fetch tools as a Gemini Tool object
process_function_calls(function_calls, return_errors=True)	list[types.Content]	Execute function calls and return Content objects
process_function_calls_as_parts(function_calls, return_errors=True)	list[types.Part]	Execute function calls and return Part objects
discover_tools()	list[MCPToolDefinition]	Low-level: discover tools with routing metadata
call_tool(name, arguments)	JsonRpcResponse	Low-level: call a single tool by name

Error Handling

All adapters handle errors gracefully. When return_errors=True (default), failed tool calls return error messages that can be sent back to the model:

# Tool call fails -> returns error in result
results = await adapter.process_tool_calls(tool_calls, return_errors=True)
# [{"role": "tool", "tool_call_id": "...", "content": "{\"error\": \"HTTP error...\"}"}]

# Skip failed calls
results = await adapter.process_tool_calls(tool_calls, return_errors=False)

Links

• Website
• Documentation
• GitHub
• Issues

License

MIT

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