AI and MCP

Phase H adds id_effect_ai — vendor-neutral LLM traits over Phase A HttpClient, with feature-gated vendors.

Language model trait

#![allow(unused)]
fn main() {
use id_effect_ai::{ChatRequest, LanguageModel, complete};
}

LanguageModel is a capability (LanguageModelService). Install MockLanguageModel in tests; wire OpenAI or Anthropic at the edge with provide_openai_language_model / provide_anthropic_language_model.

OpenAI and ChatGPT share one adapter (feature = "openai") — model IDs like gpt-4o select the model.

Streaming

complete_stream returns Stream<CompletionChunk, AiError, R> — token deltas map to chunks for sinks and UI bridges.

Anthropic Claude

feature = "anthropic" uses the Messages API (/v1/messages) with SSE streaming. System prompts map to the top-level system field.

Cursor Cloud Agents

feature = "cursor" exposes CursorAgentsClient — a separate capability from LanguageModel:

  • list_models() — GET /v1/models
  • create_agent(prompt) — POST /v1/agents
  • send_followup(agent_id, prompt) — POST /v1/agents/{id}/runs
  • wait_until_idle(agent_id, run_id) — poll until terminal status

Cursor uses HTTP Basic auth (CURSOR_API_KEY).

Configuration and secrets

Load keys via AiConfig::from_env(). API keys are Secret<String> from id_effect_config — never log .expose() output.

Transient HTTP failures (429/502/503/504) retry via Schedule in retry_transient_ai_http.

MCP server template

cargo run -p id_effect_ai --example mcp_server_template scaffolds a minimal MCP JSON-RPC loop over stdio using the same Effect + capability patterns.