OTC Pricing MCP

Official

by seaser0 · Python

MCP server for the Open Telekom Cloud Price Calculator API (eu-de, eu-nl, eu-ch2).

A Model Context Protocol (MCP) server for the Open Telekom Cloud Price Calculator API across eu-de, eu-nl, and eu-ch2 regions. This server implements MCP to provide model context data for OTC pricing workflows, enabling consistent integration with MCP-aware clients.

🛠️ Key Features

Open-source MCP server for OTC pricing models
Region support: eu-de, eu-nl, eu-ch2
Integrates with the OTC Price Calculator API
Includes CI and release infrastructure references in its readme

🚀 Use Cases

Supplying model context to MCP-enabled pricing clients
Enabling automated pricing calculations for OTC in multiple regions
Development and testing of MCP integrations for price calculators

⚡ Developer Benefits

Clear MCP server implementation for OTC pricing
Documentation and badges indicating CI, releases, and licensing
Python 3.12+ compatibility backing the server

⚠️ Limitations

Description and readme excerpt indicate basic MCP server scope; specifics beyond regional coverage and OTC integration are not detailed here.

README.md20 KBView on GitHub

OTC Pricing MCP Server

An open-source Model Context Protocol (MCP) server for the Open Telekom Cloud (OTC) Price Calculator API.

Expose OTC pricing data to Claude and other LLM clients with full observability (structured logging, Prometheus metrics, health checks).

Status: v0.1.3 — STDIO + SSE transports, Kubernetes deployment, full observability

What is MCP?

Model Context Protocol is a standard that enables LLM applications (like Claude) to interact with external tools and data sources. This server supports two transports:

Transport	How it works	Best for
STDIO	Claude launches the server as a subprocess; communication is over stdin/stdout	Local Claude Desktop, CLI tools
SSE	Server-Sent Events over HTTP — Claude connects to a URL	Remote/hosted deployments, web clients

This server gives Claude access to OTC pricing data and the user-manual / API-reference documentation through 9 specialized tools, on whichever transport you prefer.

What Can You Do With This?

Example Use Cases:

Ask Claude: "Find the cheapest ECS instance with 4 CPUs and 8GB RAM in eu-de"
Claude calls find_compute_flavor tool → gets pricing data → answers you
Ask: "Compare PAYG vs 12-month reserved pricing for S3 storage"
Claude calls compare_billing_models tool → does the analysis → shows savings

Quick Start

1. Install

Requirements: Python 3.12+

bash

# Clone the repository
git clone https://github.com/seaser0/otc-pricing-mcp.git
cd otc-pricing-mcp

# Install dependencies
uv sync

# Run the server
python -m otc_pricing_mcp

What You'll See:

code

{"event": "mcp_server_starting", "transports": ["stdio", "sse"], "port": 8080, ...}
{"event": "mcp_server_ready", "status": "accepting_connections", ...}

The server now listens for MCP connections on both stdin/stdout and http://localhost:8080/sse.

2. Connect Your MCP Client

Option A — STDIO (local, Claude Desktop)

json

{
  "mcpServers": {
    "otc-pricing": {
      "command": "python",
      "args": ["-m", "otc_pricing_mcp"],
      "env": {
        "LOG_LEVEL": "INFO",
        "METRICS_PORT": "8080"
      }
    }
  }
}

Option B — SSE (remote, Kubernetes)

Point any MCP client that supports SSE transport at the hosted endpoint:

json

{
  "mcpServers": {
    "otc-pricing": {
      "url": "https://mcp-otc-pricing.example.com/sse"
    }
  }
}

Or test locally while running the server:

bash

# In a second terminal:
curl -N http://localhost:8080/sse
# event: endpoint
# data: /messages/?session_id=<uuid>

3. Start Using Tools

Once connected, Claude can call any of the 7 available tools. See the Tools Reference section below.

Tools Reference

The server exposes 7 MCP tools for different pricing queries:

1. `list_services`

Purpose: Get all available OTC services

Input: None

Output: List of service names and metadata

Example Claude usage:

code

"What OTC services are available for pricing?"

2. `list_regions`

Purpose: Get available OTC regions

Input: None

Output: List of region codes (eu-de, eu-nl, eu-ch2, etc.)

Example Claude usage:

code

"What regions does OTC support?"

3. `get_service_schema`

Purpose: Get filterable/returnable columns for a service

Input:

service (string): Service name (e.g., "ecs", "evs", "obs", "s3", "rds")

Output: Schema with filterable and returnable column names

Example Claude usage:

code

"What columns can I filter on for ECS pricing?"

4. `query_pricing`

Purpose: Query pricing data with flexible filtering

Input:

services (array): List of service names (e.g., ["ecs", "evs"])
region (string, optional): Filter by region (e.g., "eu-de")
max_results (integer, optional): Max results to return (default: 5000)

Output: Pricing rows matching the filter

Example Claude usage:

code

"Show me ECS and EVS pricing in the eu-de region"

5. `find_compute_flavor`

Purpose: Find compute (ECS) instances by vCPU/RAM/OS

Input:

v_cpu (integer): Number of virtual CPUs
ram_gb (number): RAM in GiB
os (string, optional): Operating system (Linux, Windows, etc.)
region (string, optional): Region (default: eu-de)

Output: Matching ECS instance types with pricing

Example Claude usage:

code

"Find a Linux ECS instance with 4 CPUs and 16GB RAM in eu-nl"

6. `estimate_monthly_cost`

Purpose: Calculate monthly cost for multiple resources

Input:

items (array): Resources with:
- id (string): Product ID (e.g., "OTC_S3M1_LI")
- quantity (number, optional): How many units (default: 1)
- hours_per_month (number, optional): Usage hours (default: 730)

Output: Itemized costs with monthly total

Example Claude usage:

code

"Calculate monthly cost for 100GB S3 storage and an ECS instance"

7. `compare_billing_models`

Purpose: Compare PAYG vs Reserved Instance pricing

Input:

product_id (string): Product ID (e.g., "OTC_S3M1_LI")
quantity (number, optional): Quantity (default: 1)
hours_per_month (number, optional): Usage hours (default: 730)

Output: Cost comparison for PAYG, 12mo, 24mo, 36mo reserved

Example Claude usage:

code

"Compare PAYG vs 12/24/36 month reserved pricing for ECS"

8. `search_otc_docs`

Purpose: Full-text search across the indexed OTC user manual and API reference

Input:

query (string): Search terms (BM25-ranked, AND of tokens)
scope (string, optional): public | swiss | both (default: both)
service (string, optional): Restrict to one service repo (e.g. elastic-cloud-server)
top_k (integer, optional): 1-50, default 5

Output: Ranked list of {url, title, h2, h3, snippet, service, cloud, upstream_commit} hits. The index ships with the package and is rebuilt weekly from the upstream opentelekomcloud-docs/<service> Sphinx/RST repos (Apache-2.0); the runtime never touches the Anubis-gated docs.otc.t-systems.com HTML.

Example Claude usage:

code

"Find the OTC docs page that explains S3-flavor ECS specifications"

9. `get_otc_doc_section`

Purpose: Fetch the body of one indexed documentation page (or one of its sections) as Markdown

Input:

url (string): Canonical URL as returned by search_otc_docs (with or without #anchor)
section (string, optional): H2/H3 heading filter (case-insensitive substring)

Output: {url, title, sections: [{h2, h3, anchor, body}, ...], matched, ...}

Example Claude usage:

code

"Show me the EVS Disk Types and Performance section"

Configuration

Environment Variables

Control the server behavior with environment variables:

Variable	Default	Description
`LOG_LEVEL`	`INFO`	Logging level: `DEBUG`, `INFO`, `WARNING`, `ERROR`
`METRICS_PORT`	`8080`	Port for metrics/health endpoints
`METRICS_HOST`	`0.0.0.0`	Bind address for the HTTP server (set to `127.0.0.1` for non-container runs)
`OTC_PRICING_API_BASE`	`https://calculator.otc-service.com/en/open-telekom-price-api/`	OTC API endpoint
`OTC_DOCS_DB`	(auto)	Override path to the docs FTS5 index (default: bundled `data/otc_docs.sqlite3`)

Example:

bash

LOG_LEVEL=DEBUG METRICS_PORT=9090 python -m otc_pricing_mcp

Observability: Metrics & Logs

This server is built with production-grade observability so you can debug issues and monitor performance.

Structured Logging (JSON)

Every action is logged as JSON, making logs machine-readable for aggregation and analysis.

Start the server with DEBUG logging:

bash

LOG_LEVEL=DEBUG python -m otc_pricing_mcp 2>&1

You'll see JSON logs like:

json

{"timestamp": "2026-05-06T18:00:00.123456Z", "event": "tool_invocation_start", "tool": "query_pricing", "request_id": "550e8400-e29b-41d4-a716-446655440000", "arguments": {"services": ["ecs"]}}

{"timestamp": "2026-05-06T18:00:00.234567Z", "event": "upstream_request_start", "service": "ecs", "request_id": "550e8400-e29b-41d4-a716-446655440000"}

{"timestamp": "2026-05-06T18:00:00.345678Z", "event": "upstream_request_success", "service": "ecs", "request_id": "550e8400-e29b-41d4-a716-446655440000", "status_code": 200, "duration_seconds": 0.111, "attempt": 1, "items_returned": 42}

{"timestamp": "2026-05-06T18:00:00.456789Z", "event": "tool_invocation_success", "tool": "query_pricing", "request_id": "550e8400-e29b-41d4-a716-446655440000", "duration_seconds": 0.333}

Key fields in every log:

timestamp: When the event happened (ISO 8601)
event: What happened (tool_invocation_start, upstream_request_success, etc.)
request_id: Unique ID for this request (same across all related logs)
Custom fields depending on the event

Logs are printed to stderr, so redirect to a file or log aggregator:

bash

python -m otc_pricing_mcp 2>/var/log/otc-pricing-mcp.log

Pipe to jq for pretty printing:

bash

python -m otc_pricing_mcp 2>&1 | jq .

HTTP Endpoints (port 8080)

The uvicorn server exposes all endpoints on port 8080:

Path	Method	Description
`/sse`	GET	MCP SSE transport — connect your MCP client here
`/messages/`	POST	MCP SSE message handler (used internally by the client)
`/healthz`	GET	Liveness probe — always 200 if the process is up
`/readyz`	GET	Readiness probe — 200 when OTC API is reachable, 503 otherwise
`/metrics`	GET	Prometheus metrics in text exposition format

Health Checks:

bash

# Liveness check (always 200 if process is up)
curl http://localhost:8080/healthz
# {"status": "ok", "service": "otc-pricing-mcp"}

# Readiness check (verifies OTC API is reachable)
curl http://localhost:8080/readyz
# {"status": "ready", "upstream": "ok", "api_response_time": 0.042}

Prometheus Metrics:

bash

curl http://localhost:8080/metrics

Returns Prometheus format metrics:

code

# HELP otc_pricing_mcp_requests_total Total MCP tool requests (success and failure)
# TYPE otc_pricing_mcp_requests_total counter
otc_pricing_mcp_requests_total{status="success",tool="query_pricing"} 5.0
otc_pricing_mcp_requests_total{status="error",tool="query_pricing"} 1.0

# HELP otc_pricing_mcp_request_duration_seconds MCP tool request duration in seconds
# TYPE otc_pricing_mcp_request_duration_seconds histogram
otc_pricing_mcp_request_duration_seconds_bucket{le="0.005",tool="query_pricing"} 0.0
otc_pricing_mcp_request_duration_seconds_bucket{le="0.01",tool="query_pricing"} 1.0
...

# HELP otc_pricing_mcp_upstream_requests_total Total upstream OTC API requests (success and failure)
# TYPE otc_pricing_mcp_upstream_requests_total counter
otc_pricing_mcp_upstream_requests_total{service="ecs",status="success"} 10.0
otc_pricing_mcp_upstream_requests_total{service="ecs",status="error"} 2.0
...

Available Metrics:

otc_pricing_mcp_requests_total{tool, status}: Count of tool invocations
otc_pricing_mcp_request_duration_seconds{tool}: Tool execution time
otc_pricing_mcp_upstream_requests_total{service, status}: Count of API calls
otc_pricing_mcp_upstream_duration_seconds{service}: API call latency

Using Prometheus:

Add to your prometheus.yml:

yaml

scrape_configs:
  - job_name: 'otc-pricing-mcp'
    static_configs:
      - targets: ['localhost:8080']

Then query in Prometheus:

code

rate(otc_pricing_mcp_requests_total[5m])  # Requests per second
histogram_quantile(0.95, otc_pricing_mcp_request_duration_seconds_bucket)  # p95 latency

Debugging Guide

Problem: Slow API Calls

Check the logs:

bash

LOG_LEVEL=DEBUG python -m otc_pricing_mcp 2>&1 | jq 'select(.event == "upstream_request_success") | {service, duration_seconds}'

Check metrics:

bash

curl http://localhost:8080/metrics | grep upstream_duration_seconds

Problem: Tool Fails

Look for error logs:

bash

LOG_LEVEL=DEBUG python -m otc_pricing_mcp 2>&1 | jq 'select(.event == "tool_invocation_error")'

Example error log:

json

{
  "event": "tool_invocation_error",
  "tool": "query_pricing",
  "request_id": "550e8400-e29b-41d4-a716-446655440000",
  "error": "list index out of range",
  "error_type": "IndexError",
  "duration_seconds": 0.001,
  "exc_info": true
}

Problem: OTC API Unreachable

Check readiness endpoint:

bash

curl -v http://localhost:8080/readyz
# HTTP/1.1 503 Service Unavailable
# {"status": "not_ready", "upstream": "unreachable", "error": "..."}

Check metrics:

bash

curl http://localhost:8080/metrics | grep upstream_requests_total
# Will show increased error counts

Problem: Need Full Request Trace

Use request_id to trace a request:

bash

# Get the request_id from any log
LOG_LEVEL=DEBUG python -m otc_pricing_mcp 2>&1 | jq 'select(.request_id == "550e8400-e29b-41d4-a716-446655440000")'

This shows all logs for that request in order:

tool_invocation_start
upstream_request_start
upstream_request_success (with items_returned)
tool_invocation_success

Running Locally (Development)

Setup

bash

# Clone repo
git clone https://github.com/seaser0/otc-pricing-mcp.git
cd otc-pricing-mcp

# Install with dev dependencies
uv sync

# Run tests
uv run pytest tests/ -v

# Check code quality
uv run ruff check src/
uv run mypy src/ --strict

Run in Development Mode

bash

# With debug logging
LOG_LEVEL=DEBUG python -m otc_pricing_mcp

# In another terminal, test the endpoints
curl http://localhost:8080/healthz | jq .
curl http://localhost:8080/metrics

Running in Production (Docker)

Build Image

bash

docker build -t otc-pricing-mcp:latest .

Run Container

bash

docker run \
  --name otc-pricing-mcp \
  -e LOG_LEVEL=INFO \
  -e METRICS_PORT=8080 \
  -p 8080:8080 \
  otc-pricing-mcp:latest

Kubernetes Deployment

See deploy/kubernetes/ for the full manifest set (Deployment, Service, Ingress, NetworkPolicy, ServiceMonitor, PodDisruptionBudget).

When self-hosting on Kubernetes, connect remote clients to your ingress hostname:

code

https://mcp-otc-pricing.example.com/sse

Key features:

Non-root user, read-only root filesystem
Resource limits (100m–500m CPU, 128Mi–512Mi RAM)
Liveness probe: GET /healthz on port 8080
Readiness probe: GET /readyz on port 8080
NetworkPolicy: ingress from nginx controller only, egress to DNS + OTC API
ServiceMonitor for Prometheus scraping
Managed by ArgoCD with selfHeal: true and prune: true

Architecture

Request Flow

code

Claude Client
    │
    ├─ STDIO transport (local)      ──┐
    │  stdin/stdout                   │
    │                                 ▼
    └─ SSE transport (remote)      MCP Server (server.py)
       GET  /sse                     - List tools
       POST /messages/               - Route tool calls
                                     - Log invocations
                                     - Record metrics
                                         │
                                         ▼
                                   HTTP Client (client.py)
                                     - Build request
                                     - Retry logic
                                     - Parse response
                                         │
                                         ▼
                                   OTC Price Calculator API

Both transports share the same MCP Server instance and run concurrently in the same asyncio event loop.

Component Overview

Component	Purpose
`__main__.py`	Entry point — runs STDIO + uvicorn SSE concurrently
`server.py`	MCP server, routes tool calls, logs invocations
`client.py`	HTTP client for OTC API, retry logic, API logging
`tools/`	Tool implementations (discovery, pricing, estimation)
`observability/http_server.py`	Starlette app — SSE transport + health/metrics routes
`observability/`	Logging, Prometheus metrics, request context
`models.py`	Data models (validated with Pydantic)
`normalize.py`	Price parsing and formatting

Enhancement Ideas (Future Development)

Stories 0–9 are complete. The following are post-v1.0 enhancements:

Enhancement Ideas

Caching

Cache pricing data for N seconds to reduce API load
Redis or in-memory cache option
Cache invalidation strategy

Advanced Querying

More filtering options (e.g., price range, commitment period)
Sorting by price, CPU, RAM
Aggregations (min/max/avg pricing per service)

Cost Analysis Tools

Historical pricing trends
Cost anomaly detection
Recommendation engine (right-sizing)

Multi-Cloud Support

AWS pricing API integration
Azure pricing API integration
Cost comparison across clouds

User Preferences

Save favorite services/regions
Custom pricing alerts
Budget tracking per project

Better Error Recovery

Exponential backoff with jitter (vs fixed exponential)
Circuit breaker pattern
Fallback to cached data on API failure

Performance Optimizations

Query result pagination
Database caching layer
Streaming responses for large datasets

Observability Enhancements

Distributed tracing (OpenTelemetry)
Custom business metrics (cost calculated, queries per service)
Log aggregation integration (Loki, ELK)
Alert rules (Prometheus Alertmanager)

Testing Improvements

Load testing (k6, Locust)
Chaos testing (failure scenarios)
Contract testing with OTC API

API Stability

API versioning (v1, v2)
Deprecation policies
Backward compatibility guarantees

Contributing

We welcome contributions! See CONTRIBUTING.md for:

Development setup
Code style (ruff, mypy --strict)
Testing requirements (53+ tests with coverage)
Security scanning (bandit, cyclonedx-bom)
Commit message conventions

Quick PR Checklist:

Tests pass: uv run pytest tests/
Linting passes: uv run ruff check src/
Type checking passes: uv run mypy src/ --strict
Security scan passes: uv run bandit -r src/
Meaningful commit message

License

Apache License 2.0 — see LICENSE file.

Copyright: seaser0 (s34s3r@gmail.com)

Getting Help

Questions or Issues?

Check the Debugging Guide above
Open a GitHub Issue: https://github.com/seaser0/otc-pricing-mcp/issues
Check logs with: LOG_LEVEL=DEBUG python -m otc_pricing_mcp 2>&1 | jq .

Want to Report a Security Issue? See SECURITY.md for responsible disclosure.

Project Status

Story	Feature	Status
0	Project setup, API client, data models	✅ Done
1	Catalog discovery tools	✅ Done
2	Pricing query tools	✅ Done
3	Multi-service fan-out	✅ Done
4	Comprehensive testing	✅ Done
5	Security & container hardening	✅ Done
6	CI/CD pipeline (GHCR image, PyPI, SBOM, GitHub Release)	✅ Done
7	Observability (structured logging, Prometheus metrics, health probes)	✅ Done
8	ArgoCD deployment (Kubernetes, SSE transport, remote endpoint)	✅ Done
9	Open source documentation (README, server.json, community docs)	✅ Done

Architecture Decisions

See docs/ directory for detailed documentation:

docs/ci-cd.md — CI/CD workflow details
docs/deployment.md — Deployment guide
docs/security.md — Security features and considerations

Built with ❤️ by seaser0

Last updated: 2026-05-07

Install

Add to VS Code Add to Cursor

claude_desktop_config.json

json

{
  "mcpServers": {
    "otc-pricing-mcp": {
      "command": "uvx",
      "args": [
        "otc-pricing-mcp"
      ]
    }
  }
}

🛠️ Key Features

🚀 Use Cases

⚡ Developer Benefits

⚠️ Limitations

OTC Pricing MCP Server

What is MCP?

What Can You Do With This?

Quick Start

1. Install

2. Connect Your MCP Client

3. Start Using Tools

Tools Reference

1. list_services

2. list_regions

3. get_service_schema

4. query_pricing

5. find_compute_flavor

6. estimate_monthly_cost

7. compare_billing_models

8. search_otc_docs

9. get_otc_doc_section

Configuration

Environment Variables

Observability: Metrics & Logs

Structured Logging (JSON)

HTTP Endpoints (port 8080)

Debugging Guide

Problem: Slow API Calls

Problem: Tool Fails

Problem: OTC API Unreachable

Problem: Need Full Request Trace

Running Locally (Development)

Setup

Run in Development Mode

Running in Production (Docker)

Build Image

Run Container

Kubernetes Deployment

Architecture

Request Flow

Component Overview

Enhancement Ideas (Future Development)

Enhancement Ideas

Contributing

License

Getting Help

Project Status

Architecture Decisions

Install

Related servers

1. `list_services`

2. `list_regions`

3. `get_service_schema`

4. `query_pricing`

5. `find_compute_flavor`

6. `estimate_monthly_cost`

7. `compare_billing_models`

8. `search_otc_docs`

9. `get_otc_doc_section`