Schema | VegaMCP

VegaMCP

Describes the environment variables required to run the server.

Name	Required	Description	Default
`SENTRY_ORG`	No	Sentry organization slug
`SEARXNG_URL`	No	Self-hosted search fallback
`GITHUB_TOKEN`	No	GitHub API (60→5000 req/hr)
`KIMI_API_KEY`	No	Kimi K2.5 for coding
`SENTRY_PROJECT`	No	Sentry project name
`TAVILY_API_KEY`	No	AI-powered web search
`DEEPSEEK_API_KEY`	No	Direct DeepSeek API (R1 + Chat)
`SENTRY_AUTH_TOKEN`	No	Error tracking
`OPENROUTER_API_KEY`	No	Supports ALL models via OpenRouter
`VEGAMCP_TOOL_PROFILE`	No	full \| minimal \| research \| coding \| ops	full
`TOKEN_DAILY_BUDGET_USD`	No	Daily budget in USD	5.00
`TOKEN_HOURLY_BUDGET_USD`	No	Hourly budget in USD	1.00

Features and capabilities supported by this server

Functions exposed to the LLM to take actions

Name	Description
omni_assistantC	Core AI intelligence: reasoning, budget, auto-update, prompts, web search (speed/balanced/quality modes + domain filter), code analysis, docs, sequential thinking, expert toolkits, knowledge engine, memory graph, semantic memory (vector search), context7 library docs.
omni_swarmC	Agent orchestration: swarm ops, agent intel/ops, A2A protocol, agent graphs, agentic sampling, multimodal embeddings, session manager, MCP relay.
omni_automationC	Action & control: browser, workflow, filesystem, shell, git, MCP tasks, API requests, sandbox, watchers, webhooks, schedule, notify, the_claw, claw_command_center, vps_control, image_generation.
omni_systemsD	Infrastructure: database, postgres_client, health check, analytics, skills, seed data, sentry, OAuth, gateway, tool search, MCP apps, vault, dynamic indexing.
omni_researchD	Research & Science: graph RAG, agentic RAG, self evolution, hypothesis gen, quality gate, synthesis engine, LLM router, memory bridge, tool discovery, sentinel, LLM output evaluation.
omni_testingC	QA & Security: mobile, web, API, DB, server, desktop, accessibility, security, visual, advanced testing, security scanner, stress test, zero trust.

Interactive templates invoked by user choice

Name	Description
`investigate_error`	Investigate a production error by chaining: Sentry lookup → source correlation → fix.
`architecture_review`	Review the project architecture by querying the memory graph and analyzing patterns.
`research_pipeline`	Run a multi-agent research pipeline: research → analyze → review → summarize.
`swarm_status_report`	Generate a comprehensive status report of the agent swarm, including agent health, task metrics, and system performance.
`code_review_pipeline`	Execute a full code review pipeline: analyze code → critique → suggest improvements → summarize.
`content_pipeline`	Generate polished content: research → draft → critique → revise → publish.
`project_planning`	Create a project plan: decompose tasks → estimate → identify dependencies → generate roadmap.

Contextual data attached and managed by the client

Name	Description
`All Memory Entities`	Browse all entities in the persistent knowledge graph with observation counts.
`Project Architecture Entities`	Entities in the project-arch domain.
`Coding Style Entities`	Entities in the coding-style domain.
`Bug History Entities`	Entities in the bug-history domain.
`General Entities`	Entities in the general domain.
`All Memory Relations`	Browse all relationships between entities in the knowledge graph.
`Swarm Status`	Live status of all agents, active tasks, and coordinators.
`Active Tasks`	Currently running and queued swarm tasks.
`Metrics Dashboard`	Aggregated performance metrics across all agents.
`Event Triggers`	All registered event triggers and their fire history.

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