# LLM Model Navigator MCP (`reverberant_equality/mcp-llm-model-navigator`) Actor Compare current AI models, estimate token costs, and get transparent model recommendations for coding, reasoning, chat, vision, and budget-sensitive workloads. - **URL**: https://apify.com/reverberant\_equality/mcp-llm-model-navigator.md - **Developed by:** [Jordan C](https://apify.com/reverberant_equality) (community) - **Categories:** AI - **Stats:** 0 total users, 0 monthly users, 0.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $0.01 / 1,000 results This Actor is paid per event and usage. You are charged both the fixed price for specific events and for Apify platform usage. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event ## What's an Apify Actor? Actors are web data automations that power AI and operations. They run on the Apify platform to scrape websites, process data, connect APIs, and automate workflows. In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours, and optionally produces a well-defined JSON output, datasets with results, or files in key-value store. In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server. Actors are written with capital "A". ## How to integrate an Actor? If asked about integration, you help developers integrate Actors into their projects. You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready. The best way to integrate Actors is as follows. In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js/docs.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python/docs.md): ```bash pip install apify-client ``` In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md): ````bash # MacOS / Linux curl -fsSL https://apify.com/install-cli.sh | bash # Windows irm https://apify.com/install-cli.ps1 | iex ```bash In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/integrations/mcp.md). If your project is in a different language, use the [REST API](https://docs.apify.com/api/v2.md). For usage examples, see the [API](#api) section below. For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt). # README ## LLM Model Navigator MCP An Apify standby MCP server that helps AI builders search the public [OpenRouter model catalog](https://openrouter.ai/api/v1/models), compare model metadata, estimate token costs, and make transparent heuristic choices. It uses the public endpoint through `axios`; **no OpenRouter API key or other secret is required**. The streamable HTTP MCP endpoint is `/mcp`. Standby mode remains enabled, and `GET /` responds to Apify readiness probes. ### Tools #### `search-llm-models` Search current catalog records by optional `query` (model ID, name, or description) and optional `provider` (the prefix before `/` in a model ID). - `query` — optional non-empty string - `provider` — optional non-empty string - `maxResults` — optional integer, default `20`, maximum `50` Returns each match's ID, name, provider, context length, modality, and prompt/completion prices per token when OpenRouter lists them. #### `compare-llm-models` Compare exact OpenRouter IDs in the order requested. - `modelIds` — required array of `2` to `6` non-empty model IDs Returns a structured comparison table with context length, input/output modalities, supported parameters, and listed prompt/completion per-token pricing. Missing IDs are explicitly reported rather than guessed. #### `estimate-llm-cost` Estimate a single request's text-token cost using OpenRouter's current listed pricing. - `modelId` — required exact OpenRouter model ID - `inputTokens` — required non-negative integer - `outputTokens` — required non-negative integer The result provides separate input and output USD estimates plus their total. It assumes the listed `pricing.prompt` and `pricing.completion` per-token values and excludes request, image, web-search, cache, internal-reasoning, tax, and other provider-specific charges. Models without both prices return an actionable error. #### `recommend-llm-model` Return up to five candidates for a workload. - `task` — required: `coding`, `reasoning`, `chat`, `vision`, or `cheap-batch` - `maxInputTokens` — optional minimum context length - `maxCostPerMillionTokens` — optional cap on combined listed prompt + completion USD cost per 1M tokens The recommendation score is deliberately transparent: task name/modality match, advertised context length, and lower listed combined prompt/completion prices. These are **heuristics based on catalog metadata, not benchmark or quality claims**. Validate quality, availability, and terms with your own workload before adopting a model. ### Limits and reliability - Results are derived from the live public OpenRouter catalog, so availability, capabilities, and prices can change. - Search returns at most 50 models; comparison accepts 2–6 IDs; recommendations return at most 5 models. - The service does not persist catalog data and does not use or accept OpenRouter secrets. - If the catalog endpoint is unavailable or malformed, each tool returns a safe, actionable MCP error asking the caller to retry. It does not fabricate results. - Each MCP tool call awaits `Actor.charge({ eventName: 'tool-call' })` for Apify pay-per-event billing. ### Local development ```bash npm install npm run build npm test apify validate-schema npm run start:dev ```` The server listens on port `3000` by default, or `APIFY_CONTAINER_PORT` when set. Connect an MCP client to `http://localhost:3000/mcp`. ### Deployment Deploy with Apify in standby mode and use the actor's `/mcp` endpoint. The actor endpoint itself is authenticated by Apify as appropriate for your deployment; this service makes unauthenticated public requests only to OpenRouter's model-catalog endpoint. # Actor input Schema ## Actor input object example ```json {} ``` # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = {}; // Run the Actor and wait for it to finish const run = await client.actor("reverberant_equality/mcp-llm-model-navigator").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`); const { items } = await client.dataset(run.defaultDatasetId).listItems(); items.forEach((item) => { console.dir(item); }); // 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs ``` ## Python example ```python from apify_client import ApifyClient # Initialize the ApifyClient with your Apify API token # Replace '' with your token. client = ApifyClient("") # Prepare the Actor input run_input = {} # Run the Actor and wait for it to finish run = client.actor("reverberant_equality/mcp-llm-model-navigator").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{}' | apify call reverberant_equality/mcp-llm-model-navigator --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=reverberant_equality/mcp-llm-model-navigator", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "LLM Model Navigator MCP", "description": "Compare current AI models, estimate token costs, and get transparent model recommendations for coding, reasoning, chat, vision, and budget-sensitive workloads.", "version": "1.0", "x-build-id": "4QyCH8dZ7ojjIHFRL" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/reverberant_equality~mcp-llm-model-navigator/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-reverberant_equality-mcp-llm-model-navigator", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/reverberant_equality~mcp-llm-model-navigator/runs": { "post": { "operationId": "runs-sync-reverberant_equality-mcp-llm-model-navigator", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/reverberant_equality~mcp-llm-model-navigator/run-sync": { "post": { "operationId": "run-sync-reverberant_equality-mcp-llm-model-navigator", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } } }, "components": { "schemas": { "inputSchema": { "type": "object", "properties": {} }, "runsResponseSchema": { "type": "object", "properties": { "data": { "type": "object", "properties": { "id": { "type": "string" }, "actId": { "type": "string" }, "userId": { "type": "string" }, "startedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "finishedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "status": { "type": "string", "example": "READY" }, "meta": { "type": "object", "properties": { "origin": { "type": "string", "example": "API" }, "userAgent": { "type": "string" } } }, "stats": { "type": "object", "properties": { "inputBodyLen": { "type": "integer", "example": 2000 }, "rebootCount": { "type": "integer", "example": 0 }, "restartCount": { "type": "integer", "example": 0 }, "resurrectCount": { "type": "integer", "example": 0 }, "computeUnits": { "type": "integer", "example": 0 } } }, "options": { "type": "object", "properties": { "build": { "type": "string", "example": "latest" }, "timeoutSecs": { "type": "integer", "example": 300 }, "memoryMbytes": { "type": "integer", "example": 1024 }, "diskMbytes": { "type": "integer", "example": 2048 } } }, "buildId": { "type": "string" }, "defaultKeyValueStoreId": { "type": "string" }, "defaultDatasetId": { "type": "string" }, "defaultRequestQueueId": { "type": "string" }, "buildNumber": { "type": "string", "example": "1.0.0" }, "containerUrl": { "type": "string" }, "usage": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "integer", "example": 1 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } }, "usageTotalUsd": { "type": "number", "example": 0.00005 }, "usageUsd": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "number", "example": 0.00005 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } } } } } } } } } ```