Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@model-version-mcp show me recent OpenAI models with pricing"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
MCP server that provides up-to-date AI model names and pricing from models.dev.
Solves the problem of AI models hallucinating outdated model names by providing a live-fetched, filtered list of recently released models.
get_ai_modelsReturns AI models released within the last 4 months with pricing data.
Parameters:
provider (optional): Filter by provider name, e.g. "anthropic", "openai", "google"
Returns: JSON with model id, name, provider, release_date, cost ($/1M tokens), context window, and capabilities.
Related MCP server: token-scout
git clone <repo>
cd model-version-mcp
npm install
npm run build
claude mcp add --transport stdio model-version-mcp node dist/index.jsAdd to ~/Library/Application Support/Claude/claude_desktop_config.json:
{
"mcpServers": {
"model-version-mcp": {
"command": "node",
"args": ["/absolute/path/to/model-version-mcp/dist/index.js"]
}
}
}models.dev — community-maintained, no API key required. Cache refreshes every 60 minutes.
Returns current AI models (released within last 4 months) with pricing. Use this to get accurate, up-to-date model IDs instead of guessing.
| Name | Required | Description | Default |
|---|---|---|---|
| provider | No | Filter by provider, e.g. "anthropic", "openai", "google". Omit for all providers. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden of behavioral disclosure. It communicates that results are time-filtered to the last 4 months, that pricing is included, and that the data is current. However, it does not disclose output format, pagination, error behavior, or whether provider filtering affects the recency semantics, leaving some behavioral uncertainty.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences with no filler, front-loading the key facts: what is returned, the recency window, and that pricing is included. The second sentence gives a concrete usage rationale. Every word contributes to an agent's ability to decide whether this tool is appropriate.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple tool with one optional parameter and no output schema, the description provides the essential context: it returns current model IDs and pricing, with a 4-month recency limit. It could be more complete by describing the return shape, but the core invocation decision is well supported. The absence of an output schema is partially mitigated by the explicit mention of model IDs and pricing in the description.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema already provides 100% of the parameter documentation: the optional provider filter with examples and the instruction to omit for all providers. The description reinforces the tool's purpose but does not add meaningful detail about the provider parameter beyond the schema. The baseline score of 3 applies since the schema fully covers parameter semantics.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb and resource: "Returns current AI models" with a clear recency filter (released within last 4 months) and includes pricing. It also states the intended use case, obtaining accurate, up-to-date model IDs rather than guessing. Despite having no siblings to differentiate from, the description is unusually precise about what the tool offers.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly tells the agent when to use the tool: "Use this to get accurate, up-to-date model IDs instead of guessing." This provides clear contextual guidance, though it does not enumerate exclusions or alternative tools because none are listed. The guidance is sufficient for a simple lookup tool.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
v0.1.0get_ai_modelsWith only a single tool, there is no possibility of confusing it with another tool. The purpose of get_ai_models is clearly isolated and unambiguous.
The sole tool name follows a consistent verb_noun pattern (get_ai_models), which is clear and descriptive. There are no mixed conventions since only one name exists.
A single tool feels thin for an MCP server, even if the scope is narrowly focused on retrieving current AI models. While it can serve as a simple read-only utility, users might expect additional related operations given the server name.
The tool provides a core way to fetch recent AI models with pricing, but it lacks support for retrieving specific model details or older models outside the 4-month window. These gaps could force agents to work around the limitation or fail when needing historical model info.
Unclaimed servers have limited discoverability.
If you are the server author, to access and configure the admin panel.
Source-backed AI model pricing, rankings, history, and benchmark data.
Live LLM API pricing: token prices, comparisons, cheapest-model lookups. No key required.
Live LLM API price + status radar across 11 providers, with public per-model price HISTORY.
Check if an AI model is deprecated, retiring, or silently changed price or context window.
We provide all the information about MCP servers via our MCP API.
curl -X GET 'https://glama.ai/api/mcp/v1/servers/JonasKeil/model-version-mcp'
If you have feedback or need assistance with the MCP directory API, please join our Discord server
