InferIndex vs Tokencast
A side-by-side look at the InferIndex and Tokencast MCP servers: transport, auth, tools, quality signals, and the config to install either one in Claude, Cursor, Windsurf or VS Code.
Verdict: choose InferIndex for conversational ai work over a hosted endpoint; choose Tokencast for conversational ai as a local process. You can also run both side by side in one mcpServers block.
Which MCP server should you choose?
Choose InferIndex when
- You need dedicated capabilities in the Conversational AI domain.
- You prefer a remote HTTP/SSE transport.
- You want tools like
search_models, cheapest, compare_providers.
Choose Tokencast when
- You need dedicated capabilities in the Conversational AI domain.
- You prefer a local stdio subprocess transport.
- You want tools like
estimate_cost, get_calibration_status, get_cost_history.
Feature & specification comparison
Rows where the two servers differ are marked.
| Spec | ||
|---|---|---|
| Category | Conversational AI | Conversational AI |
| Quality signal | 57/100 · Good | 48/100 · Fair |
| Install command | Confirmed | Confirmed |
| Transport | Remote HTTP/SSE | Local subprocess (stdio) |
| 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 | 2 |
| Directory activity | 1 views 0 installs 0 upvotes | 1 views 0 installs 0 upvotes |
| Tools | 8 | 5 |
| Full listing | View InferIndex | View Tokencast |
Tools & capabilities
InferIndex tools 8
search_modelsFind the exact id of an LLM tracked by InferIndex from a name or partial name (e.g. 'deepseek', 'qwen3 max', 'claude opus'). Returns matching model ids and names, best match first.cheapestCheapest current API offers for one model across direct providers and aggregators, in USD per 1M tokens (input, output, blended 3:1). Stale prices, and flex/batch tiers, are excluded by default. Optional filters (context, tools, JSON, vision, region, no training on prompts, open sign-up) and usage (tokens per request, requests per day) to get an estimated cost per request and per month. Returns the winner in detail and one short line per following offer. A condition that is absent was not published by the provider, it never means "no"; a flag not listed in signals is false.compare_providersCurrent offers for one model, one line per provider and source (direct or via an aggregator), cheapest first (10 by default), with price, quantization, signals and training on prompts when published; the cheapest offer comes in detail. Pass detail: "full" for context, every published condition (data regions, sign-up) and reliability from official status pages on each offer.price_historyPrice history of one model: every offer tracked by InferIndex (daily or weekly min/max/last price in USD, or raw price changes), plus the official price of the model's lab over time. Give either days, or from/to (YYYY-MM-DD), or at (a date) for the prices in effect that day.estimate_costEstimated cost of a workload on one model at each provider: cost per request, and per month if requests_per_day is given, taking the provider's tiered pricing and prompt-cache price into account. Offers sorted by estimated cost, cheapest first: the cheapest in detail, then one short line per offer with its estimated cost.list_gpusGPU types whose rental price InferIndex tracks (H100, A100, L40S…), with the lowest price per GPU per hour in USD for each tier (guaranteed, community, spot) and the number of GPUs of that configuration. Prices are per GPU per hour, as published by each provider, and dated. Tiers are different products and are never compared with each other.gpu_rentalsRental offers for one GPU type: provider, price in USD per GPU per hour, number of GPUs in the published configuration, billing and region when published. One block per tier (guaranteed, community, spot), each with its own cheapest offer, also given per configuration (cheapest_by_gpu_count: the price per GPU of a single GPU and of a multi-GPU node are not comparable); tiers are never compared with each other. guaranteed: capacity the provider presents as not interrupted; community: third-party hosts; spot: interruptible capacity. Prices are as published by the provider, and dated. Get the GPU id from list_gpus.self_host_or_apiIs it cheaper to host an open-weights model yourself on rented GPUs, or to use the cheapest API offer? Returns a one-sentence verdict (headline) and the numbers behind it: the break-even volume in million tokens per day, the throughput the whole configuration must deliver for self-hosting to cost less (required_tokens_per_second: compare it with what your setup does), the break-even utilization when a throughput is known, the GPU configuration and its hourly price, and what the verdict rests on (confidence). Without a volume you still get the verdict and the break-even volume. It is an estimate: the throughput is a published measurement (measured), an estimate scaled from one (derived; estimated for a wide range), or a published minimum (lower_bound: a floor, valid for requests of up to 2,048 tokens in total, that can show self-hosting wins, never that the API is cheaper); or you give your own tokens_per_second. When no published figure decides, the headline gives the break-even volume and the required throughput, to compare with yours, rather than a yes or no, and may point out a dearer setup that published figures do decide (settled_alternative: for comparison, not a recommendation). headline_kind and headline_values give the headline as fields. It declines to give a number (status refused, with a reason) when it cannot do so reliably, for example a closed model, weights that do not fit the requested GPU, or a GPU nobody rents. Hardware rental only: the hourly price the provider publishes for the machine. It does not count the engineering to deploy and run the model, monitoring, redundancy, start-up time, or storage and network costs billed separately.
Tokencast tools 5
estimate_costEstimate API cost for a planned task before running itget_calibration_statusCheck whether your estimates are well-calibratedget_cost_historyBrowse past estimates vs actualsreport_sessionReport actual cost at session end to improve calibrationreport_step_costRecord the cost of a single pipeline step during a session
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.
InferIndex
{
"mcpServers": {
"inferindex": {
"url": "https://mcp.inferindex.dev/mcp"
}
}
}
Tokencast
{
"mcpServers": {
"tokencast": {
"command": "uvx",
"args": [
"tokencast"
]
}
}
}
Frequently asked questions
InferIndex is categorized under Conversational AI and uses a remote HTTP/SSE transport. Tokencast belongs to Conversational AI and uses a local stdio subprocess. Pick InferIndex for conversational ai work and Tokencast 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.
InferIndex has no declared pricing — check the project's own site. Tokencast 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 inferindex and tokencast as separate keys in the same mcpServers object, and your assistant will call tools from either one.
On AllMCPs, InferIndex has a quality signal of 57/100 (Good) with 1 views, 0 installs and 0 GitHub stars. Tokencast has 48/100 (Fair) with 1 views, 0 installs and 2 GitHub stars.