Kimss Python SDK & MCP server

Lightweight client for the Kimss API — call agents, run model completions, upload files, and manage vector stores from Python. Optional Model Context Protocol (MCP) server for Cursor, Windsurf, and other MCP-capable IDEs.

AI assistants: read docs/llm-context.md or the repo root .llms.txt for dense integration context.

Cursor Marketplace plugin

This repository includes a Cursor plugin layout for Cursor Marketplace submission alongside the PyPI package:

Legacy Open Plugins metadata remains under .plugin/plugin.json and mcpb/manifest.json. The root .mcp.json matches mcp.json for environments that read dot-prefixed MCP config.

Related MCP server: agentforge

Cursor & Windsurf (MCP) — zero local venv with uvx

Install the MCP extra on the fly and expose tools to your IDE:

{
  "mcpServers": {
    "kimss": {
      "command": "uvx",
      "args": ["--from", "kimss[mcp]", "kimss-mcp-server"],
      "env": {
        "KIMSS_API_KEY": "your_key_here",
        "KIMSS_BASE_URL": "https://api.kimss.ai",
        "KIMSS_WORKSPACE_ID": ""
      }
    }
  }
}

• Set KIMSS_API_KEY to a long-lived key from Developer Settings → API Keys (never commit it).

• Optional KIMSS_WORKSPACE_ID stamps X-Workspace-ID / tenant_id for workspace-scoped calls.

• MCP tools are non-streaming in v1 (kimss_chat, kimss_create_agent, kimss_run_agent, kimss_complete, kimss_upload_file, kimss_create_vector_store, kimss_add_function_to_agent).

Alternatively, after pip install 'kimss[mcp]', use "command": "kimss-mcp-server" on your PATH with the same env.

Windsurf Integration

To use Kimss natively inside Codeium Windsurf as an MCP toolset, add the configuration to your local Windsurf settings:

1. Open your global Windsurf MCP configuration file:

   • macOS/Linux: ~/.codeium/windsurf/mcp_config.json

   • Windows: %USERPROFILE%\.codeium\windsurf\mcp_config.json

2. Append the kimss config block to the mcpServers object:

{
  "mcpServers": {
    "kimss": {
      "command": "uvx",
      "args": ["--from", "kimss[mcp]", "kimss-mcp-server"],
      "env": {
        "KIMSS_API_KEY": "your_api_key_here",
        "KIMSS_BASE_URL": "https://api.kimss.ai"
      }
    }
  }
}

3. Reload Windsurf. The kimss tools appear under the MCP toolset once the server starts.

Note: use uvx --from kimss[mcp] kimss-mcp-server (not --with). --from tells uvx to install the kimss package and run its kimss-mcp-server console script; --with would make uvx look for a (nonexistent) PyPI package literally named kimss-mcp-server.

Install (library)

pip install kimss

Optional PII redaction (Microsoft Presidio + spaCy; e.g. python -m spacy download en_core_web_lg):

pip install 'kimss[privacy]'

Other extras:

pip install 'kimss[mcp]'   # MCP server (stdio)
pip install 'kimss[types]' # Pydantic (reserved for future typed models)
pip install 'kimss[dev]'    # pytest, responses, ruff

Editable from a checkout of this repository root:

pip install -e ".[dev,mcp]"

Authentication

Use a long-lived API key (not a browser session token). Create keys in your Kimss app under Developer Settings → API Keys. The key is scoped to your tenant and user.

Headless workers can also authenticate with Microsoft Entra ID by passing an Azure credential plus a Kimss API token scope:

from azure.identity import DefaultAzureCredential
from kimss import KimssClient

client = KimssClient(
    base_url="https://api.kimss.ai",
    credential=DefaultAzureCredential(),
    token_scope="api://<kimss-api-app-id>/.default",
    workspace_id="<your-workspace-slug>",
)

Usage

Use the canonical Kimss API host. Production is https://api.kimss.ai and staging is https://stg.kimss.ai; do not include a trailing slash.

from kimss import KimssClient, Agent

client = KimssClient(
    api_key="kimss_xxxxxxxxxxxxxxxxxxxxxxxx",  # from Developer Settings
    base_url="https://api.kimss.ai",  # no trailing slash
)

# Get an agent and send a message
agent = client.get_agent("asst_xxxx")
result = agent.query("Hello")
# result is the API "res" payload (messages, usage, etc.). Prefer conversation_id in SDK 2+.
result2 = agent.query("What did I just say?", conversation_id=result.get("thread_id"))

# One-off chat without an Agent handle (same wire field as above)
result3 = client.chat("asst_xxxx", "Hi", conversation_id=result.get("thread_id"))

# Or v1 orchestration (preferred): non-stream returns AgentRunResult (.text, .usage, .conversation_id)
result_v1 = client.agents.run("asst_xxxx", "Hello", stream=False)
print(result_v1.text, result_v1.usage.total_credits, result_v1.conversation_id)

Streaming

client.models.create(..., stream=True) and client.agents.run(..., stream=True) return an SSE iterator of JSON objects. The MCP server does not expose streaming tools in v1.

API

• KimssClient(..., retry=None) – authenticated client. Provide either api_key (uses X-Kimss-Key) or credential + token_scope (uses Authorization: Bearer). workspace_id optionally stamps X-Workspace-ID and tenant_id for isolated worker telemetry. Uses a requests.Session with retry on 5xx (not 429) and Retry-After by default so credit exhaustion and rate limits surface immediately as typed errors (KimssCreditExhausted, KimssRateLimited, KimssSubscriptionRequired).

• client.get_agent(agent_id) – returns an Agent for that assistant.

• agent.query(message, conversation_id=None, chat_type="user_chat") – send a message; returns the res object from POST /assistant_chat/.

• client.chat(assistant_id, message, conversation_id=None, chat_type="user_chat") – one-off chat without an Agent handle.

• client.agents.create / client.agents.run – v1 agent management and orchestration (/v1/agents/create, /v1/agents/run). agents.run accepts positionals (assistant_id, message), keyword aliases agent_id / prompt, optional conversation_id (maps to JSON thread_id), optional tags and routing_preference; stream=False returns AgentRunResult (dict subclass with .text, .usage.total_credits, .conversation_id) when res is a dict.

• client.models.create – /v1/models/completions.

• client.files.upload – /v1/files/upload.

• client.vector_stores.create – /v1/vector_stores/create.

• before_request_hooks – list of callables hook(ctx) where ctx is {"path": str, "json": dict, "headers": dict}; hooks may mutate json / headers before the HTTP POST.

• privacy – shortcut for PresidioRedactor() from kimss.privacy (requires kimss[privacy]).

from kimss import KimssClient, PresidioRedactor

client = KimssClient(
    api_key="kimss_...",
    base_url="https://api.kimss.ai",
    privacy=PresidioRedactor(),
)

API-key requests use the X-Kimss-Key header. Credential requests use Authorization: Bearer <token>. Non-streaming responses are full JSON dicts from the API res envelope where applicable.

Examples

See examples/ — set KIMSS_API_KEY (and KIMSS_ASSISTANT_ID / KIMSS_MODEL where noted).

Usage Hub (execution context)

For agent and model calls, the SDK automatically adds an optional X-Kimss-SDK-Context header (base64url JSON) with:

• host_environment — e.g. Azure WEBSITE_SITE_NAME, GitHub:org/repo, or Local/Dev

• source_location — best-effort path to the caller's Python file (relative to getcwd() when possible)

• resource_type / resource_name — agent or model plus assistant id or model id

Paths are resolved in your process and sent as metadata for the workspace Usage dashboard. Use before_request_hooks to remove that header from ctx["headers"] if your policy forbids file paths.

Contributing & release

See CONTRIBUTING.md for tests, mirror workflow, and PyPI trusted publishing. Operator bookmark (monorepo): 3-step release routine.