Feature Added

A Model Context Protocol server that lets any MCP-compatible AI assistant call Apertis directly. No setup, no wrapper code — just install once. **Install (Claude Code):** ```bash claude mcp add apertis -- npx -y @apertis/mcp-server ``` **Nine tools shipped:** | Tool | What it does | |------|--------------| | `list_models` | List models with optional free/paid + capability filters | | `get_model_info` | Detailed info for a specific model (pricing, context, provider) | | `compare_models` | Side-by-side comparison of 2–5 models | | `check_quota` | Account balance, subscription status, remaining quota | | `get_usage_stats` | Usage by model and period (today / week / month) | | `list_api_keys` | List your keys (masked) with status and quota | | `create_api_key` | Create a new key with an optional quota limit | | `suggest_model` | Freeform keyword search over the full catalog | | `recommend_model` | Curated Apertis pick for a task type with live pricing *(new in v0.3.0)* | → Guide: [docs.apertis.ai/api/sdks/mcp-server](https://docs.apertis.ai/api/sdks/mcp-server) → npm: [`@apertis/mcp-server`](https://www.npmjs.com/package/@apertis/mcp-server) ### Agent Skills — one-command install for 45+ AI tools Three curated skills that teach your AI assistant how to use Apertis correctly. Install once, works everywhere. ```bash npx skills add theQuert/apertis-skills ``` Compatible with Claude Code, Cursor, GitHub Copilot, Codex, Gemini CLI, and [45+ other AI coding tools](https://agentskills.io). | Skill | What your agent learns | |-------|------------------------| | `apertis-api` | Auth, endpoints, `:web` suffix, MCP reference — the complete API surface | | `apertis-model-picker` | Opinionated model picks by task type with reasoning | | `apertis-migrate` | One-line swap from OpenAI SDK to Apertis | → Source: [github.com/theQuert/apertis-skills](https://github.com/theQuert/apertis-skills) ### `GET /v1/recommend` — dynamic model selection endpoint Ask Apertis what to use for a task and get back the curated pick with live pricing. Recommendations update as models are added, retired, or re-priced — your code stays the same. ```bash curl "https://api.apertis.ai/v1/recommend?task=coding&budget=medium" \ -H "Authorization: Bearer $APERTIS_API_KEY" ``` **Task types:** `coding`, `long-context`, `fast-chat`, `reasoning`, `vision` **Budget tiers:** `low`, `medium` (default), `high` **Response shape:** ```json { "model": "claude-sonnet-4-6", "pricing": { "input_per_1m": 2.40, "output_per_1m": 12.00 }, "why": "Best coding ability per dollar. 200K context.", "alternatives": [ { "model": "deepseek-v3", "note": "3x cheaper, good for simpler coding" }, { "model": "claude-opus-4-6", "note": "most capable, higher cost" } ] } ``` Use the returned `model` ID directly in your next `/v1/chat/completions` call. → Reference: [docs.apertis.ai/api/utilities/recommend](https://docs.apertis.ai/api/utilities/recommend) --- ## Docs - New: `@apertis/mcp-server` SDK guide with `recommend_model` walkthrough - New: `GET /v1/recommend` endpoint reference with Python example - Updated: Cursor integration guide with new screenshots and `apertis/` prefix convention - Updated: Ideas page now publicly browsable at [docs.apertis.ai/help/ideas](https://docs.apertis.ai/help/ideas) - Updated: Timeout documentation — `X-Timeout` header, `408` status semantics --- ## Why this release We kept seeing two questions in support: 1. *“Which model should I use?”* 2. *“How do I wire Apertis into my agent/IDE?”* `recommend_model` + the skills + the MCP server answer both — without asking you to paste the same instructions into every new session. Your agent now picks the right model and knows how to call us, natively.