Orchestrator MCP Server
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| ORCHESTRATOR_URL | No | Override default orchestrator URL | https://web-production-bdfb4.up.railway.app |
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
Server capabilities have not been inspected yet.
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| thinkA | Deep reasoning using cognitive orchestrator. Auto-routes to best AI capabilities. Use for complex analysis, multi-step reasoning, research questions. |
| consensusB | Get multiple AI perspectives (Claude, GPT, Gemini) and synthesize consensus. Use for important decisions. |
| fast_aiB | Ultra-fast AI response via Groq (~100ms). Use for quick questions. |
| web_searchA | Real-time web search via Perplexity. Use for current events, recent info, live data. |
| scrape_urlC | Scrape and extract content from any URL via Firecrawl. |
| run_pythonB | Execute Python code in secure E2B sandbox. Use for data analysis, calculations, testing. |
| run_javascriptC | Execute JavaScript code in secure E2B sandbox. |
| mem0_addC | Store information in Mem0 long-term memory. |
| mem0_searchC | Search Mem0 long-term memory. |
| neo4j_queryC | Query Neo4j knowledge graph for entity relationships. |
| railway_statusB | Get deployment status of all Railway services. |
| railway_set_variableC | Set environment variable on a Railway service. |
| railway_redeployC | Trigger redeployment of a Railway service. |
| github_reposC | List GitHub repositories. |
| github_fileC | Read a file from a GitHub repository. |
| healthA | Check health status of cloud orchestrator and all connected services. |
| wolfram_queryB | Query Wolfram Alpha for math, science, calculations, conversions, real-time data. Use for any computational question. |
| wolfram_calculateC | Calculate a math expression using Wolfram Alpha. |
| wolfram_convertC | Convert between units using Wolfram Alpha. |
| openrouter_chatB | Query ANY AI model via OpenRouter. Access Claude, GPT, Llama, Mistral, and 100+ models through single API. |
| openrouter_modelsB | List all available AI models on OpenRouter. |
| sentry_statsB | Get error tracking statistics from Sentry. |
| sentry_errorsC | Get recent errors captured by Sentry. |
| api_keys_summaryB | Get summary of all API keys status from the configuration sheet. Shows active, missing, and high-impact services. |
| api_keys_missingB | List all services that are still missing API keys. |
| api_keys_readyA | List services that have API keys filled in and are ready to inject to Railway. |
| api_keys_injectC | Inject a specific service API key from the sheet to Railway. |
| api_keys_inject_allC | Inject ALL ready API keys from the sheet to Railway services. |
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 28 tools
The tool set has clear clusters for distinct domains like API key management, AI services, and Railway operations, but there is significant overlap within some clusters. For example, wolfram_calculate, wolfram_convert, and wolfram_query have overlapping purposes that could cause confusion, and fast_ai, openrouter_chat, and think all involve AI interactions with unclear boundaries. Descriptions help differentiate some tools, but misselection is possible due to this overlap.
Most tools follow a consistent snake_case naming pattern with clear verb_noun structures, such as api_keys_inject, railway_redeploy, and mem0_search. However, there are minor deviations like consensus, health, and think, which use single words without a noun, slightly breaking the pattern. Overall, the naming is mostly predictable and readable across the set.
With 28 tools, the count is borderline high for a single server, as it covers multiple broad domains like cloud orchestration, AI services, and data processing. This could feel heavy and overwhelming for agents, but it is reasonable given the server's apparent scope as an 'Orchestrator' integrating various services. A more focused scoping might reduce the count for better usability.
The tool surface covers a wide range of operations across its domains, such as CRUD-like actions for API keys and Railway services, and diverse AI and data processing capabilities. Minor gaps exist, like no explicit update or delete tools for some resources (e.g., GitHub files or Mem0 entries), but agents can likely work around these with available tools. The set supports core workflows without major dead ends.