Trust Engine AI: model retirements and agent cost

trust-engine-mcp

Free MCP server from Trust Engine AI. It answers questions agents otherwise search the web for:

• Is a model we use being retired, and when? Verified dates for Anthropic, OpenAI, Google Gemini API and Vertex AI, AWS Bedrock and Mistral. Each date has its official source URL and the date it was checked.
• Why do our agents cost so much? It reads an OpenAI or Anthropic usage/cost export locally and shows spend by model, cost drivers and the fixes you can make yourself.
• Is a cheaper model good enough? You get our public benchmark numbers, a savings estimate for the model pairs we measured, and a ready request for a free pilot on your own tasks.

Read-only. No key, no account, no telemetry. The server only sends GET requests to agenttrustapi.com.

Honesty notes. The retirement data is copied from vendor pages, so follow source_url before you act on a date. The benchmark is one coding agent on 27 public exercises: it compares the models fairly, but it does not predict results on your tasks. Savings estimates exist only for pairs we measured; for any other pair the tool gives no number. Nothing is sent for you: request_pilot drafts an e-mail that a person sends.

Connect with one command

Needs Python 3.10+ and uv.

# Claude Code
claude mcp add --transport stdio trust-engine -- uvx trust-engine-mcp

# Codex CLI
codex mcp add trust-engine -- uvx trust-engine-mcp

Claude Desktop, Cursor and other stdio clients use this config (claude_desktop_config.json, .cursor/mcp.json):

{
  "mcpServers": {
    "trust-engine": { "command": "uvx", "args": ["trust-engine-mcp"] }
  }
}

Claude Desktop can also install the one-file bundle trust-engine-mcp-<version>.mcpb from the GitHub releases. Without uv, run pip install trust-engine-mcp and use the command trust-engine-mcp.

Try this first

Use the trust-engine tools. We use claude-sonnet-4-5, gpt-4o and gemini-2.5-flash. Which of them are being retired, on which platform, and by when must we re-test? Cite the sources.

Here is our usage export (CSV). Why are our agents so expensive? List the quick fixes we can do ourselves this week.

Is Claude Sonnet good enough instead of Opus for coding work? Show the benchmark numbers and what we would save on a $4,000 monthly bill. Then tell me how to test this on our own tasks.

Tools

Tool	Answers
check_my_models(models)	For each model your agent uses: retirement date per platform, days left, the vendor's replacement, the source, and the next step ("re-test before ...")
model_retirements(provider?, model?, before_date?, include_past?, text_models_only?)	Upcoming retirements and deprecations, filtered, with sources
analyze_usage(csv_text? | rows?)	Spend by model, top-model share, models on the retirement calendar, cost drivers, next steps. Runs offline; nothing is uploaded
benchmark_results()	Public Aider benchmark for Claude Opus, Sonnet and Haiku 5.5: tasks solved, cost per solved task, failed tasks, method and raw data
estimate_switch_savings(monthly_bill_usd, from_model, to_model)	Measured cost ratio and the arithmetic, for measured pairs only
prepare_pilot_tasks(csv_text?, use_case?)	The pilot task format (CSV template), how to pick 20-30 tasks, how to mask personal data, and an offline check of a draft
request_pilot(company?, use_case?, models?, task_count?, monthly_bill_usd?, notes?)	A draft e-mail and mailto: link to [email protected] for a person to send
pricing()	Published prices (US$, valid from 12 Nov 2026), incl. the fixed-price 7-day test ($1,390), 24/7 watch and Bulgarian review

Usage exports analyze_usage reads

Column names are matched loosely; case, spaces and separators are ignored. Only a model column is required.

• model: model, model_name, model_id, snapshot_id, line_item (e.g. "gpt-4o-2024-08-06, input")
• cost: cost, cost_usd, amount, amount_value, spend, total_cost
• requests: requests, num_model_requests, n_requests, request_count
• tokens: input_tokens, prompt_tokens, n_context_tokens_total, uncached_input_tokens, output_tokens, completion_tokens, cache_read_input_tokens, input_cached_tokens, cache_creation_input_tokens
• reliability: errors, error_count, status, status_code, retries
• other: batch, service_tier, date, start_time, bucket_start

Data sources

Data	URL	License
Model retirement calendar	https://agenttrustapi.com/retirements.json (schema, Atom, page)	CC BY 4.0
Aider model-diet benchmark	https://agenttrustapi.com/benchmarks/aider-model-diet.json (report, protocol, raw CSV)	as on the report
Pilot task template	https://agenttrustapi.com/pilot/tasks_template.csv	—

The server caches each file for 6 hours with a 10-second timeout. The package includes a snapshot of both files. If the site is unreachable, the server uses that snapshot and says so in every answer.

Environment

Variable	Default	Meaning
TRUST_ENGINE_OFFLINE	0	1: never fetch; use the bundled snapshot only
TRUST_ENGINE_BASE_URL	https://agenttrustapi.com	data host (for testing against a local copy)

Development

pip install -e ".[dev]"
pytest && ruff check .

The tests use fixtures and a fake site. They make no network calls.

License

Code MIT © 2026 Trust Engine AI. Retirement data CC BY 4.0. Contact: [email protected]

mcp-name: io.github.DinevDecor/trust-engine-mcp