AI Model Lifecycle Index vs Modelmeter
A side-by-side look at the AI Model Lifecycle Index and Modelmeter 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 Lifecycle Index for conversational ai work over a hosted endpoint; choose Modelmeter 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 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.
Choose Modelmeter when
- You need dedicated capabilities in the Conversational AI domain.
- You prefer a remote HTTP/SSE transport.
- You want tools like
estimate_cost, get_model, list_models.
Feature & specification comparison
Rows where the two servers differ are marked.
| Spec | ||
|---|---|---|
| Category | Conversational AI | Conversational AI |
| Quality signal | 53/100 · Good | 56/100 · Good |
| Install command | Confirmed | Confirmed |
| Transport | Remote HTTP/SSE | 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 | 1 |
| Directory activity | 1 views 0 installs 0 upvotes | 1 views 0 installs 0 upvotes |
| Tools | 4 | 7 |
| Full listing | View AI Model Lifecycle Index | View Modelmeter |
Tools & capabilities
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.
Modelmeter tools 7
estimate_costEstimate the USD cost of an LLM API call for a given model and token counts. Returns input/output/total cost, plus reseller markup vs. the upstream model when applicable.get_modelUnified card for one model in a single call: normalized pricing, capabilities (context window, vision, reasoning, tags), availability, reseller markup vs. upstream, and a price-history summary (launch vs. current price, percent change, last change date, all-time low/high).list_modelsList available models with their current pricing. Optionally filter by provider.get_price_historyHistorical pricing time-series for a model or provider. Each point is a dated snapshot of the per-Mtok rates.list_eventsThe changelog of record for the model layer: deprecations, price changes, launches, and the market events around them. Filter by severity to find what demands action: 'breaking' (model going away, ID changing), 'action_required' (price/rate-limit/context changes), 'informational' (releases, funding).check_model_dependenciesIs my stack okay? Check a list of model ids against the record: scheduled retirements with days-remaining countdowns and migration targets, past retirements, and other breaking/action-required changes. Id matching tolerates dots vs dashes, dated snapshot suffixes, and bare or provider-prefixed forms. Call this at startup or in CI with the models you depend on.list_deprecationsPer-model retirement rows from the record: which model dies when, with runway and migration target. Default shows scheduled (upcoming) retirements sorted by shutdown date.
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 Lifecycle Index
{
"mcpServers": {
"ai-model-lifecycle-index": {
"url": "https://lifecycle.oscar-lopez.com/mcp"
}
}
}
Modelmeter
{
"mcpServers": {
"modelmeter": {
"url": "https://modelmeter.xyz/mcp"
}
}
}
Frequently asked questions
AI Model Lifecycle Index is categorized under Conversational AI and uses a remote HTTP/SSE transport. Modelmeter belongs to Conversational AI and uses a remote HTTP/SSE transport. Pick AI Model Lifecycle Index for conversational ai work and Modelmeter 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 Lifecycle Index has no declared pricing — check the project's own site. Modelmeter 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-lifecycle-index and modelmeter as separate keys in the same mcpServers object, and your assistant will call tools from either one.
On AllMCPs, AI Model Lifecycle Index has a quality signal of 53/100 (Good) with 1 views, 0 installs and 0 GitHub stars. Modelmeter has 56/100 (Good) with 1 views, 0 installs and 1 GitHub stars.