OpenRouter Zero MCP
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| OPENROUTER_API_KEY | Yes | Your OpenRouter API key (e.g. sk-or-v1-your-key-here). All API calls are made directly to https://openrouter.ai/api/v1 with your personal key. |
Instructions
Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.
This server publishes no instructions, or was last inspected before Glama recorded them.
Capabilities
Features and capabilities supported by this server
Protocol revision2025-11-25
| Capability | Details |
|---|---|
| tools | {} |
| prompts | {} |
| resources | {} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| openrouter_chat_completionC | Execute chat inference on any model available on OpenRouter (e.g. google/gemini-2.5-flash, anthropic/claude-3.7-sonnet, deepseek/deepseek-v4-flash-0731:US, meta-llama/llama-3.3-70b-instruct). |
| openrouter_get_creditsB | Retrieve account balance, credits usage, and API key limits from OpenRouter. |
| openrouter_list_modelsC | List available AI models on OpenRouter with filters for name, slug, context window size, and pricing. |
| openrouter_manage_keysB | Manage OpenRouter API keys (list, create, delete) via the Management API. Requires OPENROUTER_MANAGEMENT_KEY. |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
No prompts | |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
No resources | |
TDQS
Scored across 4 tools
Each tool targets a clearly distinct area: chat inference, account credits, model listing, and API key management. There is no meaningful overlap or ambiguity in intent.
All tools share the openrouter_ prefix and snake_case format, which is highly consistent. The only minor deviation is openrouter_chat_completion, which is noun-based rather than verb_noun like the other three.
Four well-scoped tools cleanly cover the OpenRouter integration surface without redundancy. Each tool earns its place for a focused MCP server.
Core workflows are covered: inference, model discovery, credits, and key lifecycle. Minor gaps exist around richer generation options (e.g. embeddings, streaming controls, per-model usage), but agents can work around them.