Ai Model Experiments vs AI Model Lifecycle Index
A side-by-side look at the Ai Model Experiments and AI Model Lifecycle Index MCP servers: transport, auth, tools, quality signals, and the config to install either one in Claude, Cursor, Windsurf or VS Code.
Verdict: choose Ai Model Experiments for conversational ai work as a local process; choose AI Model Lifecycle Index for conversational ai over a hosted endpoint. You can also run both side by side in one mcpServers block.
Which MCP server should you choose?
Choose Ai Model Experiments when
- You need dedicated capabilities in the Conversational AI domain.
- You prefer a local stdio subprocess transport.
- You want tools like
experiment_models, experiment_estimate, experiment_create.
Choose AI Model Lifecycle Index when
- You need dedicated capabilities in the Conversational AI domain.
- You prefer a remote HTTP/SSE transport.
- You want tools like
model_lifecycle, is_model_live, upcoming_deprecations.
Feature & specification comparison
Rows where the two servers differ are marked.
| Spec | ||
|---|---|---|
| Category | Conversational AI | Conversational AI |
| Quality signal | 61/100 · Good | 53/100 · Good |
| Install command | Confirmed | Confirmed |
| Transport | Local subprocess (stdio) | Remote HTTP/SSE |
| Auth | Not declared | Not declared |
| Pricing | Not declared | Not declared |
| Required env vars | None listed | None listed |
| Verified owner | Community listing | Community listing |
| GitHub stars | 0 | 0 |
| Directory activity | 1 views 0 installs 0 upvotes | 1 views 0 installs 0 upvotes |
| Tools | 8 | 4 |
| Full listing | View Ai Model Experiments | View AI Model Lifecycle Index |
Tools & capabilities
Ai Model Experiments tools 8
experiment_modelsList AI models available for experiments (about 300 across Anthropic, OpenAI, Google, Meta, Mistral, DeepSeek, Qwen and more), with context window and OUR per-token prices (provider cost × 1.5 — what experiments actually bill). Filter by name/vendor search, minimum context, or max price. Use the returned model ids in experiment_create. Example: experiment_models({ search: "claude", min_context: 100000 })experiment_estimateDry-run cost estimate for an experiment BEFORE creating it — cell count and estimated billed cost range (at our 1.5× pricing) for prompts × models × reps. Free to call, no side effects, does not need credit balance. Same spec shape as experiment_create. Example: experiment_estimate({ prompts: ["Summarize: ..."], models: ["anthropic/claude-sonnet-4.5", "openai/gpt-5"], reps: 2 })experiment_createModel Lab: run one prompt across many AI models at once and compare their outputs, cost, and latency side by side. Create and start an experiment: run each prompt against each model (× reps), collecting output, tokens, latency, and billed cost per cell. PREPAID: requires Pipeworx credit balance ≥ max_spend_usd (top up via experiment_topup); bills actual provider cost × 1.5 with a $0.10 minimum per experiment. ASYNC: returns experiment_id immediately — execution starts within ~1 minute; poll experiment_status until complete, then call experiment_results. Do NOT wait synchronously. Set summary:false to skip the AI-written model-comparison summary. Example: experiment_create({ name: "tone test", prompts: ["Rewrite formally: ..."], models: ["anthropic/claude-haiku-4.5", "openai/gpt-5-mini"], reps: 2, max_spend_usd: 2 })experiment_statusProgress of an experiment: cell counts by state (pending/running/ok/error/skipped), spend so far vs cap, and whether it is complete. Poll this after experiment_create (every few seconds). Example: experiment_status({ experiment_id: "..." })experiment_resultsResults of an experiment: per-model aggregates (mean latency, tokens, total billed cost, error rate), per-cell outputs, and the AI-written summary comparing how the models differed (if enabled). Use include_outputs:false for aggregates only. Example: experiment_results({ experiment_id: "..." })experiment_listList your experiments, newest first, with status and spend. Example: experiment_list({ limit: 10 })experiment_cancelCancel a running experiment: pending cells are skipped (not billed); in-flight cells finish and bill. Example: experiment_cancel({ experiment_id: "..." })experiment_topupHow to add prepaid credits for experiments (and your current balance). Payment is x402 — USDC on Base, paid in-band by any wallet-equipped agent: POST https://gateway.pipeworx.io/credits/topup?amount_usd=10 responds HTTP 402 with payment requirements; retry with PAYMENT-SIGNATURE to settle and the credits land instantly. Example: experiment_topup({})
AI Model Lifecycle Index tools 4
model_lifecycleFREE. Lifecycle of one AI model: status (active/preview/deprecated/retired), release/deprecation/shutdown dates, days until shutdown, recommended migration target, and the official source URL.is_model_liveFREE. Fast check of whether an AI model is still callable, plus its sunset date and days remaining. Use before depending on a model at runtime.upcoming_deprecationsFREE. AI models being shut down within the next N days across OpenAI, Anthropic, Google, and Amazon Bedrock, with migration targets. Default 90 days.get_model_lifecycle_allFull cross-vendor lifecycle export (all tracked models). Free during the public preview; metered via x402 when live.
Ready-to-paste client configs
Paste either block (or both) into your client config, e.g. claude_desktop_config.json or ~/.cursor/mcp.json. We only show a config when the install command is confirmed.
Ai Model Experiments
{
"mcpServers": {
"ai-model-experiments": {
"command": "npx",
"args": [
"-y",
"@pipeworx/mcp-ai-model-experiments"
]
}
}
}
AI Model Lifecycle Index
{
"mcpServers": {
"ai-model-lifecycle-index": {
"url": "https://lifecycle.oscar-lopez.com/mcp"
}
}
}
Frequently asked questions
Ai Model Experiments is categorized under Conversational AI and uses a local stdio subprocess. AI Model Lifecycle Index belongs to Conversational AI and uses a remote HTTP/SSE transport. Pick Ai Model Experiments for conversational ai work and AI Model Lifecycle Index for conversational ai.
Copy the mcpServers block from the config section on this page into your client's config (for example claude_desktop_config.json or ~/.cursor/mcp.json), then fully restart the client.
Ai Model Experiments has no declared pricing — check the project's own site. AI Model Lifecycle Index has no declared pricing — check the project's own site. If a server needs environment variables such as API keys, define them under the env key in your client's MCP config.
Yes. MCP clients support multiple servers: add ai-model-experiments and ai-model-lifecycle-index as separate keys in the same mcpServers object, and your assistant will call tools from either one.
On AllMCPs, Ai Model Experiments has a quality signal of 61/100 (Good) with 1 views, 0 installs and 0 GitHub stars. AI Model Lifecycle Index has 53/100 (Good) with 1 views, 0 installs and 0 GitHub stars.