mcpschema 0.1.0

Convert MCP tool schemas to OpenAI, Anthropic, Gemini, and other LLM provider formats — zero dependencies

mcpschema

Convert MCP tool schemas to OpenAI, Anthropic, Gemini, and other LLM provider formats — zero dependencies.

mcpschema translates mcp.types.Tool inputSchema dicts into the tool-call shapes used by OpenAI, Anthropic, Gemini, Ollama, DeepSeek, and Mistral — so you can route an MCP server's tools to any LLM provider without reimplementing the conversion per-provider. It runs as a library and a CLI.

Quick Start

Install from source:

pip install git+https://github.com/prasad-a-abhishek/mcpschema.git

Convert a single MCP tool to any provider format:

from mcpschema import to_openai_tool, to_anthropic_tool, to_gemini_tool, to_ollama_tool

mcp_tool = {
    "name": "git_log",
    "description": "Get recent commits",
    "inputSchema": {
        "type": "object",
        "properties": {"n": {"type": "integer", "default": 10, "description": "Number of commits"}},
        "required": ["n"],
    },
}

openai = to_openai_tool(mcp_tool)       # {"type": "function", "function": {...}}
anthropic = to_anthropic_tool(mcp_tool) # {"name", "description", "input_schema"}
gemini = to_gemini_tool(mcp_tool)       # {"name", "description", "parameters": {OBJECT,...}}
ollama = to_ollama_tool(mcp_tool)       # + "system_prompt_suffix" for local-model prompting

Convert a whole server's tools in one call:

from mcpschema import convert_all

tools = server.list_tools()  # any iterable of mcp.types.Tool
openai_batch = convert_all(tools, "openai")

⚡ Performance & Benchmarks

mcpschema is a thin, pure-Python library — every adapter is a single function. We benchmark against a hand-written equivalent (the de-facto "no library" path that every developer reaches for when they hit issue #235 of the MCP Python SDK).

The honest summary: mcpschema wins on batch conversion (saving per-tool function-call overhead) and ties the hand-written baseline on single-tool conversions. Full results in benchmarks/BENCHMARK.md include every workload — including the ones where the hand-written baseline is faster. Reproduce locally:

python benchmarks/run_benchmark.py

Why mcpschema? (Problem & Trade-Off Statement)

The MCP Python SDK ships no adapter module — developers integrating MCP with non-MCP LLM providers each hand-roll their own inputSchema → provider-format conversion. Three GitHub issues document the gap:

• modelcontextprotocol/python-sdk#235 — "Official Adapter Functions for LLM Providers in MCP Python SDK" (18 reactions, open since 2024)
• modelcontextprotocol/python-sdk#226 — schema-conversion complexity (16 reactions)
• anthropics/claude-code#6915 — multi-agent routing (89 reactions)

What mcpschema is: a focused, zero-dependency library that does ONE thing (schema conversion) and does it well. It is NOT an LLM client, NOT a MCP server, and NOT a replacement for the SDK.

What mcpschema is NOT:

• Not an LLM client. It only converts schemas; it never calls a provider.
• Not coupled to the MCP SDK. It accepts any object with name, description, inputSchema (duck-typed), so the SDK stays out of your dependency tree.
• Not a streaming/conversation manager. See the out-of-scope list below.

Trade-offs you accept by using mcpschema:

• Defaults are stripped on conversion (per the OpenAI/Anthropic/Gemini API specs). If you need to preserve them, copy them out before converting.
• anyOf/oneOf/allOf are not in any provider's tool-call schema; we drop them rather than silently mis-modeling them.
• New provider API changes require a small library update (each adapter is ~20 LOC).

Key Features & Complete API / CLI Reference

Library API

Function	Returns	Provider
to_openai_tool(tool)	{"type":"function","function":{...}}	OpenAI / GPT-4o / GPT-5
to_anthropic_tool(tool)	{"name","description","input_schema"}	Anthropic Claude
to_gemini_tool(tool)	{"name","description","parameters":{OBJECT,...}}	Google Gemini
to_ollama_tool(tool)	OpenAI shape + system_prompt_suffix	Ollama (local models)
to_deepseek_tool(tool)	OpenAI shape	DeepSeek
to_mistral_tool(tool)	OpenAI shape	Mistral
convert(tool, "provider")	dispatches to the right adapter	any
convert_all(tools, "provider")	list	any
tool_from_dict(raw)	MCPTool	helper
PROVIDERS	dict mapping provider names to callables	—

CLI

$ mcpschema --help
usage: mcpschema [-h] [--version] {providers,convert} ...

Convert MCP tool schemas to OpenAI, Anthropic, Gemini, Ollama, DeepSeek,
and Mistral tool-call formats.

$ mcpschema providers
mcpschema 0.1.0 supports 6 providers:
  - anthropic
  - deepseek
  - gemini
  - mistral
  - ollama
  - openai

$ mcpschema convert --provider openai --input '[{"name":"git_log","description":"...","inputSchema":{...}}]'
[
  {
    "type": "function",
    "function": {
      "name": "git_log",
      "description": "...",
      "parameters": {
        "type": "object",
        "properties": {"n": {"type": "integer"}},
        "required": ["n"]
      }
    }
  }
]

$ echo '[{...}]' | mcpschema convert --provider anthropic --input -

Flags:

• --provider (required): target provider name (case-insensitive)
• --input (required): JSON payload (single dict, list, or - for stdin)
• --compact: emit single-line JSON instead of pretty-printed

Out of scope

• LLM provider authentication or API calls
• MCP server implementation
• Provider-specific parameter constraints (e.g. OpenAI's max_items limits)
• Streaming response parsing
• Multi-turn conversation management
• Tool result conversion (MCP → provider response format)

Limitations

• Defaults are stripped — none of OpenAI/Anthropic/Gemini/Ollama's tool-call schemas accept default values.
• anyOf/oneOf/allOf are dropped — these JSON-Schema keywords are not in any provider's tool format.
• Deeply nested objects (>3 levels) may need flattening for some providers — we preserve nesting as deep as it goes.
• Schema validation is not strict — we coerce loose schemas (missing type, missing properties) to valid provider shapes rather than raising.

Tests

pytest tests/ -q

137 tests across 9 files. Coverage spans all 22 spec acceptance criteria plus angular sweep (empty/None inputs, unicode, large schemas, malformed schemas, regression guards, batch conversion, JSON-RPC round-trip).

License

MIT — see LICENSE.