2.2.47 — 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):

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 → npm: `@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.

npx skills add theQuert/apertis-skills

Compatible with Claude Code, Cursor, GitHub Copilot, Codex, Gemini CLI, and 45+ other AI coding tools.

| 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

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.

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:

{
  "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

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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
• Updated: Timeout documentation — X-Timeout header, 408 status semantics

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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.