Anthropic Claude SDK

pip install anthropic requests

import os

import anthropic

NIMA_MCP_URL = "https://mcp.getnima.io/mcp"
MODEL = "claude-opus-5-5"


def main() -> None:
    nima_key = os.environ["NIMA_API_KEY"]
    client = anthropic.Anthropic()

    response = client.beta.messages.create(
        model=MODEL,
        max_tokens=16000,
        messages=[
            {
                "role": "user",
                "content": "List the nima tools you can call, one per line.",
            }
        ],
        mcp_servers=[
            {
                "type": "url",
                "url": NIMA_MCP_URL,
                "name": "nima",
                "authorization_token": nima_key,
            }
        ],
        tools=[{"type": "mcp_toolset", "mcp_server_name": "nima"}],
        betas=["mcp-client-2025-11-20"],
    )

    for block in response.content:
        if block.type == "text":
            print(block.text)


if __name__ == "__main__":
    main()

import json
import os

import anthropic

NIMA_MCP_URL = "https://mcp.getnima.io/mcp"
MODEL = "claude-opus-5-5"

SYSTEM = (
    "Before you propose a change, call ask_knowledge_graph for why the area is "
    "the way it is and who asked for it, then search_code for where it lives. "
    "Quote what they return. search_code reports the commit each index was built "
    "from as lastCommitSha; say so when it may be behind. An empty answer is not "
    "proof that nothing exists."
)

QUESTION = "We want to raise the upload size limit. What should I know first?"


def main() -> None:
    nima_key = os.environ["NIMA_API_KEY"]
    client = anthropic.Anthropic()

    response = client.beta.messages.create(
        model=MODEL,
        max_tokens=16000,
        system=SYSTEM,
        messages=[{"role": "user", "content": QUESTION}],
        mcp_servers=[
            {
                "type": "url",
                "url": NIMA_MCP_URL,
                "name": "nima",
                "authorization_token": nima_key,
            }
        ],
        tools=[
            {
                "type": "mcp_toolset",
                "mcp_server_name": "nima",
                "default_config": {"enabled": False},
                "configs": {
                    "ask_knowledge_graph": {"enabled": True},
                    "search_code": {"enabled": True},
                },
            }
        ],
        betas=["mcp-client-2025-11-20"],
    )

    for block in response.content:
        if block.type == "mcp_tool_use":
            print(f"→ {block.name} {json.dumps(block.input)}")
        elif block.type == "mcp_tool_result":
            if isinstance(block.content, str):
                text = block.content
            else:
                text = "".join(part.text for part in block.content if part.type == "text")
            print(f"← {'error: ' if block.is_error else ''}{text[:300]}")
        elif block.type == "text":
            print(block.text)


if __name__ == "__main__":
    main()

tools=[
    {
        "type": "mcp_toolset",
        "mcp_server_name": "nima",
        "default_config": {"enabled": False},
        "configs": {
            "ask_knowledge_graph": {"enabled": True},
            "search_code": {"enabled": True},
            "create_signal": {"enabled": True},
        },
    }
],

import os

import anthropic
import requests

NIMA_API = "https://api.getnima.io"
MODEL = "claude-opus-5-5"
MAX_TURNS = 10

SYSTEM = (
    "Before you propose a change, call ask_knowledge_graph for why and who, then "
    "search_code for where. Quote what they return. An empty answer is not proof "
    "that nothing exists."
)

TOOLS = [
    {
        "name": "ask_knowledge_graph",
        "description": (
            "Ask nima's knowledge graph a plain-English question about the product: "
            "why a decision was made, who asked for something, what customers said. "
            "Returns markdown with entities, sourced facts, relationships and "
            "unresolved contradictions."
        ),
        "input_schema": {
            "type": "object",
            "properties": {
                "q": {"type": "string", "description": "The question, 2 to 500 characters."},
                "limit": {"type": "integer", "description": "Entities to return, 1 to 20. Default 8."},
            },
            "required": ["q"],
        },
    },
    {
        "name": "search_code",
        "description": (
            "Search the repositories nima has indexed. Returns ranked files with a "
            "summary, symbols and line ranges, plus per repository the commit the "
            "index was built from (lastCommitSha). Never returns source code."
        ),
        "input_schema": {
            "type": "object",
            "properties": {
                "q": {"type": "string", "description": "What to find, at least 2 characters."},
                "repositoryId": {"type": "string", "description": "A repository id from an earlier response."},
                "limit": {"type": "integer", "description": "File hits to return, 1 to 20. Default 8."},
            },
            "required": ["q"],
        },
    },
]

ROUTES = {
    "ask_knowledge_graph": "/v1/knowledge-graph/ask",
    "search_code": "/v1/code-intel/search",
}


def run_tool(name: str, tool_input: dict, nima_key: str) -> tuple[str, bool]:
    path = ROUTES.get(name)
    if path is None:
        return f"Unknown tool: {name}", True
    try:
        response = requests.get(
            f"{NIMA_API}{path}",
            headers={"Authorization": f"Bearer {nima_key}"},
            params=tool_input,
            timeout=60,
        )
    except requests.RequestException as err:
        return f"Could not reach nima: {err}", True
    if response.status_code >= 400:
        return f"nima answered {response.status_code}: {response.text[:500]}", True
    return response.text, False


def main() -> None:
    nima_key = os.environ["NIMA_API_KEY"]
    client = anthropic.Anthropic()
    messages = [
        {"role": "user", "content": "We want to raise the upload size limit. What should I know first?"}
    ]

    for _ in range(MAX_TURNS):
        response = client.messages.create(
            model=MODEL,
            max_tokens=16000,
            system=SYSTEM,
            tools=TOOLS,
            messages=messages,
        )
        messages.append({"role": "assistant", "content": response.content})

        if response.stop_reason != "tool_use":
            break

        results = []
        for block in response.content:
            if block.type != "tool_use":
                continue
            print(f"→ {block.name} {block.input}")
            content, is_error = run_tool(block.name, block.input, nima_key)
            results.append(
                {
                    "type": "tool_result",
                    "tool_use_id": block.id,
                    "content": content,
                    "is_error": is_error,
                }
            )
        messages.append({"role": "user", "content": results})

    if response.stop_reason not in ("end_turn", "tool_use"):
        print(f"Stopped: {response.stop_reason}")
    for block in response.content:
        if block.type == "text":
            print(block.text)


if __name__ == "__main__":
    main()