AdKit MCP Server

# macOS/Linux
curl -LsSf https://astral.sh/uv/install.sh | sh

# Windows (PowerShell)
powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex"

# Or with pip
pip install uv

# Using Docker
docker run -p 6333:6333 -p 6334:6334 qdrant/qdrant

# Or download binary from https://github.com/qdrant/qdrant/releases

# Install all dependencies and create virtual environment
uv sync

# Copy example env file (defaults work for local Qdrant)
cp .env.example .env

docker run -d --name qdrant \
  -p 6333:6333 -p 6334:6334 \
  qdrant/qdrant

docker ps --filter name=qdrant

uv sync

cp .env.example .env

uv run ad-index create 

uv run ad-index seed

src/ad_injector/
  models/           # Ad, Targeting, Policy; MCP request/response DTOs
  services/         # MatchService, PolicyEngine, TargetingEngine, IndexService
  adapters/         # QdrantVectorStore, FastEmbedProvider
  mcp/              # server, tools, auth, observability
  config/           # RuntimeSettings, env vars
  ops/              # smoke_check, migrations

# Data Plane MCP server (LLM-facing, read-only): ads_match, ads_explain, ads_health, ads_capabilities
uv run ad-mcp-data
# or: uv run ad-data-plane

# Control Plane MCP server (admin): collection.*, ads.upsert_batch, ads.delete, ads.bulk_disable, ads.get
uv run ad-mcp-control

# CLI (Control Plane): create collection, seed ads, info, delete
uv run ad-index create          # Create the collection
uv run ad-index seed            # Add sample ads for testing
uv run ad-index info            # Show collection info
uv run ad-index delete          # Delete the collection

uv run python -m ad_injector.main_runtime   # Data Plane MCP
uv run python -m ad_injector.main_control   # Control Plane MCP
uv run python -m ad_injector.cli create     # Control Plane CLI
uv run python -m ad_injector.cli seed

uv run pytest tests/ -v

uv run python -c "
from ad_injector.mcp.server import create_server
from ad_injector.mcp.tools import DATA_PLANE_ALLOWED_TOOLS
s = create_server('data')
tools = set(s._tool_manager._tools.keys())
print(f'Server: {s.name}')
print(f'Tools:  {tools}')
assert tools == DATA_PLANE_ALLOWED_TOOLS, f'FAIL: expected {DATA_PLANE_ALLOWED_TOOLS}'
print('PASS: Data Plane allowlist registered')
"

uv run python -c "
from ad_injector.mcp.server import create_server
s = create_server('admin')
tools = set(s._tool_manager._tools.keys())
print(f'Server: {s.name}')
print(f'Tools:  {tools}')
assert 'ads_match' not in tools, 'FAIL: ads_match on admin plane'
assert 'collection_ensure' in tools
print('PASS: admin tools registered, no ads_match')
"

uv run python -c "
from ad_injector.models import MatchRequest, MatchConstraints, PlacementContext, MatchResponse, AdCandidate

# Valid request
req = MatchRequest(
    context_text='I want to learn Python',
    top_k=5,
    placement=PlacementContext(placement='sidebar', surface='chat'),
    constraints=MatchConstraints(topics=['python'], locale='en-US', sensitive_ok=False),
)
print(f'MatchRequest OK: context_text={req.context_text!r}, top_k={req.top_k}')
print(f'  constraints.topics={req.constraints.topics}, locale={req.constraints.locale}')

# Valid response
resp = MatchResponse(
    candidates=[AdCandidate(ad_id='ad-001', advertiser_id='adv-1', title='Learn Python',
        body='Courses', cta_text='Go', landing_url='https://example.com', score=0.95, match_id='m-1')],
    request_id='req-xyz', placement='sidebar',
)
print(f'MatchResponse OK: {len(resp.candidates)} candidate(s)')

# Invalid request (empty context) fails
try:
    MatchRequest(context_text='', top_k=5)
    print('FAIL: empty context_text should be rejected')
except Exception:
    print('PASS: empty context_text rejected')
"

# Defaults
uv run python -c "
from ad_injector.config import get_settings
s = get_settings()
print(f'host={s.qdrant_host} port={s.qdrant_port} model={s.embedding_model_id}')
"

# Invalid port fails fast
QDRANT_PORT=99999 uv run python -c "from ad_injector.config.runtime import RuntimeSettings; RuntimeSettings()" 2>&1 | head -3

uv run python -c "
import sys
from ad_injector.main_runtime import main
mods = [m for m in sys.modules if m.startswith('ad_injector')]
assert 'ad_injector.cli' not in mods, 'FAIL: cli imported'
print('PASS: main_runtime has clean import graph (no admin modules)')
"

uv add <package-name>           # Add a dependency
uv add --dev <package-name>     # Add a dev dependency

from ad_injector.models import Ad, AdTargeting, AdPolicy
from ad_injector.wiring import build_index_service, build_match_service
from ad_injector.models.mcp_requests import MatchRequest

# Create the collection (once) and seed ads via IndexService
index_svc = build_index_service()
index_svc.ensure_collection()
ad = Ad(
    ad_id="ad-001",
    advertiser_id="adv-123",
    title="Learn Python Today",
    body="Master Python programming with our interactive courses.",
    cta_text="Start Learning",
    landing_url="https://example.com/python",
    targeting=AdTargeting(
        topics=["programming", "python", "education"],
        locale=["en-US"],
        verticals=["education", "technology"],
    ),
    policy=AdPolicy(sensitive=False, age_restricted=False),
)
index_svc.upsert_ads([ad])

# Match ads via MatchService (Data Plane logic)
match_svc = build_match_service()
response, audit_trace = match_svc.match(
    MatchRequest(context_text="python tutorial", top_k=5)
)
for c in response.candidates:
    print(f"{c.ad_id}: {c.title} (score={c.score}, match_id={c.match_id})")

{
  "candidates": [
    {
      "ad_id": "ad-001",
      "advertiser_id": "adv-123",
      "title": "Learn Python Today",
      "body": "Master Python programming...",
      "cta_text": "Start Learning",
      "landing_url": "https://example.com/python",
      "score": 0.95,
      "match_id": "m-abc123"
    }
  ],
  "request_id": "req-xyz-456",
  "placement": "sidebar"
}