Provides access to OpenAI models (including GPT) via the RouterBase API gateway, enabling chat completions and model discovery.
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., "@RouterBase MCP list available models"
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.
An MCP server for routerbase, the OpenAI-compatible API gateway for GPT, Claude, Gemini, and 200+ AI models.
This package exposes RouterBase model discovery, model details, pricing lookup, quickstart guidance, and chat completions as Model Context Protocol tools.
routerbase_get_started returns integration snippets and the canonical routerbase link.
routerbase_list_models searches and filters the RouterBase model catalog.
routerbase_get_model fetches details for one model id.
routerbase_get_pricing fetches pricing for one model or all active models.
routerbase_chat_completion calls the OpenAI-compatible /v1/chat/completions endpoint.
routerbase://overview is a resource with RouterBase integration metadata.
Related MCP server: Polybrain MCP Server
npm install -g routerbase-mcpOr run it without installing:
npx routerbase-mcpUntil the npm package is live, run it directly from GitHub:
npx -y github:zenlee123/routerbase-mcpCreate a RouterBase API key at routerbase and set:
export ROUTERBASE_API_KEY=sk-rb-your-keyOptional overrides:
export ROUTERBASE_API_BASE_URL=https://routerbase.com/api/v1
export ROUTERBASE_OPENAI_BASE_URL=https://routerbase.com/v1{
"mcpServers": {
"routerbase": {
"command": "npx",
"args": ["-y", "routerbase-mcp"],
"env": {
"ROUTERBASE_API_KEY": "sk-rb-your-key"
}
}
}
}Use the same stdio command:
{
"command": "npx",
"args": ["-y", "routerbase-mcp"],
"env": {
"ROUTERBASE_API_KEY": "sk-rb-your-key"
}
}npm install
npm run build
node dist/index.jsThis repository includes:
server.json for the official MCP Registry.
metadata/directory-listing.json for MCP directories.
SUBMISSION_KIT.md with listing copy for MCP.so, PulseMCP, Glama, Smithery, and GitHub lists.
The preferred listing sentence is:
RouterBase MCP lets AI agents discover models, pricing, and chat completions through routerbase.
MIT
Call RouterBase's OpenAI-compatible chat completions API through the configured RouterBase key.
| Name | Required | Description | Default |
|---|---|---|---|
| model | Yes | RouterBase chat model id. | |
| api_key | No | Optional RouterBase API key. Prefer ROUTERBASE_API_KEY. | |
| messages | Yes | OpenAI-compatible chat messages. | |
| max_tokens | No | ||
| temperature | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description must cover behavioral traits. It only states it calls the API, omitting details like side effects (mutation), rate limits, authentication specifics, or error handling.
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?
One sentence is too brief for a tool with 5 params and no annotations. It lacks important details, making it underspecified rather than effectively concise.
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?
Given no output schema, no annotations, and multiple parameters, the description fails to explain return values, behavior, or usage context, leaving significant gaps.
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?
Schema covers 60% of parameters with descriptions. Tool description adds no value beyond schema; undocumented parameters (max_tokens, temperature) lack any semantic help in the description.
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 clearly states it calls the OpenAI-compatible chat completions API via RouterBase, using specific verb and resource. It distinguishes from sibling tools which are about model info, pricing, etc.
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?
No explicit guidance on when to use this tool vs alternatives. Usage is implied by the purpose, but the description lacks context about required setup or when NOT to use it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Fetch metadata for a specific RouterBase model id.
| Name | Required | Description | Default |
|---|---|---|---|
| api_key | No | Optional RouterBase API key. Prefer ROUTERBASE_API_KEY. | |
| model_id | Yes | RouterBase model id. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description only says 'Fetch metadata' with no behavioral details such as whether it is read-only, any required permissions, rate limits, or side effects. With no annotations provided, the description carries the full burden and fails to disclose basic traits.
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 a single sentence with no wasted words. It is front-loaded and gets straight to the point.
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?
Given the tool has 2 parameters, no output schema, and no annotations, the description is insufficient. It does not explain what metadata is returned, any usage constraints, or the relationship to other tools. For a simple fetch it is barely adequate.
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?
Schema coverage is 100%, so the baseline is 3. The description adds no extra meaning beyond what the schema already provides; it simply restates the purpose without detailing parameter formats or constraints.
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 clearly states the action ('Fetch metadata') and the resource ('a specific RouterBase model id'). This is a specific verb+resource pair that distinguishes it from sibling tools like routerbase_chat_completion (different verb) and routerbase_list_models (different resource).
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?
No guidance is provided on when to use this tool versus the siblings. For example, it does not explain when to fetch details for a single model versus listing all models with routerbase_list_models. No preconditions or alternatives are mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Fetch RouterBase pricing for one model or all active models.
| Name | Required | Description | Default |
|---|---|---|---|
| api_key | No | Optional RouterBase API key. Prefer ROUTERBASE_API_KEY. | |
| model_id | No | Optional RouterBase model id. Omit to list all pricing. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. It discloses that the operation is fetching (read-only), but does not mention authentication requirements, rate limits, or behavior when api_key is missing. Minimal disclosure beyond the action itself.
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?
One concise sentence with no fluff. Front-loaded with verb and resource, perfectly sized for a simple pricing tool.
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?
Simple tool with no output schema. Description covers what it does but omits return format or pagination. Given simplicity, it's adequate but could benefit from stating the output structure.
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?
Schema coverage is 100% with clear parameter descriptions. The description adds marginal value ('Omit to list all pricing' is already in schema). Baseline 3 is appropriate since schema already explains parameters well.
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 clearly states the verb 'Fetch', resource 'RouterBase pricing', and specifies the scope: one model or all active models. It distinguishes from sibling tools like routerbase_chat_completion and routerbase_list_models by focusing on pricing data.
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?
No explicit guidance on when to use this tool vs alternatives. The description implies usage by omitting model_id for all pricing, but lacks context like prerequisites or comparisons to sibling tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Return a concise RouterBase integration guide with the canonical routerbase backlink.
| Name | Required | Description | Default |
|---|---|---|---|
| language | No | Preferred quickstart language. | python |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, and the description does not disclose behavioral traits such as side effects, permissions, or data source. It only states it returns a guide, which is insufficient for full transparency.
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 single and concise, with no wasted words. However, it could be slightly more detailed without losing conciseness.
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?
With no output schema, the description should clarify the return format or content. The mention of a 'canonical routerbase backlink' is vague, and the guide's structure or depth is not explained.
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 fully describes the 'language' parameter with enum and default. The description adds no additional meaning or context about this parameter, failing to compensate for the already high schema coverage.
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 clearly states the tool returns a RouterBase integration guide, which distinguishes it from siblings like chat completion or model listing. It is specific with verb 'Return' and resource 'integration guide'.
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?
No guidance on when to use this tool versus its siblings. The description does not mention use cases or prerequisites, leaving the agent to infer from the tool name.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List RouterBase models, optionally filtered by modality, provider, or search query.
| Name | Required | Description | Default |
|---|---|---|---|
| page | No | 1-indexed page number. | |
| task | No | Filter by modality. Multiple values are ORed. | |
| search | No | Full-text search query. | |
| api_key | No | Optional RouterBase API key. Prefer ROUTERBASE_API_KEY. | |
| per_page | No | Results per page. | |
| provider | No | Filter by provider names such as OpenAI, Anthropic, Google, or xAI. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, and the description is minimal. It does not disclose behavioral traits such as pagination behavior, authentication needs (though api_key param exists), rate limits, or what the output contains. The description adds little beyond the obvious.
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 a single, front-loaded sentence with no wasted words. It captures the core action and variability efficiently.
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?
The description is adequate for a simple list tool, but given 6 parameters, no output schema, and no annotations, it could provide more context about output format, pagination details, or defaults. It is minimally complete.
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?
Schema description coverage is 100%, so parameters are well-documented in the schema. The description adds no new semantic meaning beyond reiterating that filters exist. Baseline 3 is appropriate.
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 clearly states the action 'List RouterBase models' and specifies optional filters (modality, provider, search), distinguishing it from siblings like routerbase_get_model (single model) and routerbase_chat_completion (chat).
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 implies use for listing with optional filters but does not explicitly state when to use it over alternatives or provide any when-not-to-use guidance. Sibling tools exist, but no differentiation criteria are given.
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.
v0.1.0routerbase_chat_completionrouterbase_get_modelrouterbase_get_pricingrouterbase_get_startedrouterbase_list_modelsEach tool serves a distinct purpose: chat completion, model metadata, pricing, integration guide, and model listing. No overlap or ambiguity.
All tools follow a consistent 'routerbase_verb_noun' pattern, e.g., routerbase_list_models, routerbase_chat_completion. Predictable and uniform.
With 5 tools, the server is well-scoped for its purpose as an API wrapper. It covers essential operations without being overly minimal or bloated.
The tool set covers the full lifecycle for interacting with RouterBase: listing models, getting details, pricing, making API calls, and onboarding. No obvious gaps.
Unclaimed servers have limited discoverability.
If you are the server author, to access and configure the admin panel.
AI model routing on your own vendor keys: pick the best model per prompt, or route and run it.
AI service marketplace — agents discover, call, and pay for API services automatically.
AI routing, memory, guardrails, and governance. Routes across Claude, GPT, Gemini.
run any ai model. compose agents, stack knowledge, connect tools. one api, pay per run.
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