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VegaMCP

by Pastarafian
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VegaMCP is a production-grade MCP (Model Context Protocol) server providing an autonomous AI agent swarm, persistent semantic memory, browser automation, multi-model reasoning, security gateway, agent graphs, zero-trust identity, A2A protocol, Docker sandbox v5.0, AI-first testing suite (mobile, web, API, desktop, accessibility, security, visual), and 78+ tools — all accessible via any MCP-compatible client.

Version 7.2 (Sovereign Intelligence) introduces the production Claw Command Center, ultra-fast SQLite chat syncing, local vector Semantic Memory, LLM Output Evaluation, and 6 Unified Omni-Clusters.

📖 Complete Features

Read FEATURES.md for a comprehensive list of all 17 unified V7 capability clusters including Docker Sandbox v5.0.

Related MCP server: Gravitas-Core-MCP

Quick Start

Prerequisites

  • Node.js 20+

  • npm 9+

Installation

# Clone the repository
git clone https://github.com/Pastarafian/VegaMCP.git
cd VegaMCP

# Install dependencies
npm install

# Copy environment config
cp .env.example .env
# Edit .env with your API keys

# Build
npm run build

Connect to VS Code (Gemini / Copilot)

Create .vscode/mcp.json in your workspace:

{
  "servers": {
    "REDACTED": {
      "type": "stdio",
      "command": "node",
      "args": ["/path/to/VegaMCP/build/index.js"],
      "cwd": "/path/to/VegaMCP"
    }
  }
}

Note: API keys can be set in the env block of mcp.json or in the .env file (dotenv is loaded automatically).

Configuration

Copy .env.example to .env and configure:

# At least one reasoning model key required
OPENROUTER_API_KEY=          # Supports ALL models via OpenRouter
DEEPSEEK_API_KEY=            # Direct DeepSeek API (R1 + Chat)
KIMI_API_KEY=                # Kimi K2.5 for coding

# Optional integrations
GITHUB_TOKEN=                # GitHub API (60→5000 req/hr)
TAVILY_API_KEY=              # AI-powered web search
SEARXNG_URL=                 # Self-hosted search fallback
SENTRY_AUTH_TOKEN=           # Error tracking
SENTRY_ORG=
SENTRY_PROJECT=

# Budget controls
TOKEN_DAILY_BUDGET_USD=5.00
TOKEN_HOURLY_BUDGET_USD=1.00

# Tool profiles
VEGAMCP_TOOL_PROFILE=full    # full | minimal | research | coding | ops

Project Structure

VegaMCP/
├── src/
│   ├── index.ts                     # Server entry point + hub router
│   ├── mcp-extensions.ts            # Sampling, logging, progress, roots
│   ├── mcp-protocol/               # v6.0 / v7.0 protocol modules
│   ├── db/                          # SQLite + vector store
│   ├── swarm/                       # Agent swarm (10 agents)
│   ├── tools/                       # All tool implementations
│   ├── resources/                   # MCP resource providers
│   ├── prompts/                     # MCP prompt templates
│   └── security/                    # Rate limiter, validator, guard
├── .env.example                     # Environment template
├── package.json
└── tsconfig.json

License

MIT

Available Tools

6 tools
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.

ParametersJSON Schema
NameRequiredDescriptionDefault
tool_targetYesThe precise sub-tool to execute within this omni-cluster.
payloadYesThe arguments payload exactly matching the target tool's native inputSchema requirements.

TDQS

C2.8/5.0
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description must disclose behavioral traits. It fails to mention that the tool is a router, authentication needs, rate limits, or error behavior. Only a list of capabilities is given, which is insufficient.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is one long sentence listing many items without structure or prioritization. It could be more organized into categories or bullet points for readability.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a complex router with 15 sub-tools, the description lacks details about each sub-tool's purpose and behavior. No output schema is provided, and the description does not fully equip an AI agent to use the tool correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% and describes both parameters. The description adds no additional meaning beyond the schema; it merely lists sub-tool names. Baseline score of 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description lists many capabilities, indicating this is a multi-purpose router tool. It distinguishes from siblings by naming specific sub-tools, but the list is broad and lacks a focused statement of the tool's primary function.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

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 versus alternatives. Sibling tools are named but not differentiated, and the description does not provide context for selecting among the many sub-tools.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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.

ParametersJSON Schema
NameRequiredDescriptionDefault
tool_targetYesThe precise sub-tool to execute within this omni-cluster.
payloadYesThe arguments payload exactly matching the target tool's native inputSchema requirements.

TDQS

C2.5/5.0
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are present, and the description does not disclose behavioral traits such as how the tool routes to sub-tools, side effects, authentication needs, or limits. The payload schema implies flexibility, but the behavior is not explained.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is very brief (a list after a colon), but it is not well-structured. It is concise but lacks sentence-level clarity and could be reorganized for better readability.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the complexity of 15 sub-tools, the description is severely incomplete. No output schema, no explanation of payload structure per sub-tool, and no usage examples. The tool is underdescribed for effective use.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so baseline is 3. The description of 'payload' adds some value by stating it must match the target tool's native inputSchema, but the tool description itself adds little extra meaning beyond listing enum values.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose3/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description lists many sub-tools and says 'Action & control,' but it does not clearly state that the tool is a dispatcher or orchestrator. While the list gives an idea of capabilities, the purpose is vague and lacks a specific verb+resource.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

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 its siblings (omni_assistant, omni_research, etc.). There is no mention of contexts or exclusions.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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.

ParametersJSON Schema
NameRequiredDescriptionDefault
tool_targetYesThe precise sub-tool to execute within this omni-cluster.
payloadYesThe arguments payload exactly matching the target tool's native inputSchema requirements.

TDQS

D1.5/5.0
Behavior1/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are present, so the description must fully disclose behavioral traits. It does not mention destructiveness, authentication, state changes, or any side effects.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness2/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Description is a single long list, not concise or front-loaded with a clear function. It wastes space on enumeration rather than providing compact, structured information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness1/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given no output schema and no annotations, the description is severely incomplete. It fails to explain routing logic, return values, or usage nuances for a complex multi-sub-tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% but description adds no meaning beyond the enum and payload description. It merely repeats the list of sub-tools without explaining their purpose or payload requirements.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose2/5

Does the description clearly state what the tool does and how it differs from similar tools?

Description lists sub-tools but fails to state the overall function of omni_research. It does not use a specific verb+resource and does not distinguish from sibling tools like omni_assistant or omni_automation.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines1/5

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 alternatives. No when-to-use or when-not-to-use information provided.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

omni_swarmC

Agent orchestration: swarm ops, agent intel/ops, A2A protocol, agent graphs, agentic sampling, multimodal embeddings, session manager, MCP relay.

ParametersJSON Schema
NameRequiredDescriptionDefault
tool_targetYesThe precise sub-tool to execute within this omni-cluster.
payloadYesThe arguments payload exactly matching the target tool's native inputSchema requirements.

TDQS

C2.2/5.0
Behavior1/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, and the description gives no information about side effects, authorization needs, rate limits, or whether operations are destructive. For a meta-tool that dispatches to various sub-tools, this is a critical gap.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness2/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single run-on sentence with a colon and a long list, making it hard to parse. It could be much more concise by stating it is a dispatcher and referencing the sub-tool-specific inputs.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description does not explain that payload must match the target tool's schema, nor does it mention the need for deep knowledge of each sub-tool's inputs. No output schema is provided, and the description adds no context about return values or behavior.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so baseline is 3. The description adds no additional meaning beyond the schema; the list of enum values is redundant with the tool_target parameter.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose3/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states 'Agent orchestration' which conveys a general purpose, but the rest is a comma-separated list of sub-tools, essentially repeating the enum values without a clear verb or overall action. It does not distinguish itself from sibling tools like omni_assistant or omni_automation.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

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 versus alternatives, no prerequisites or conditions mentioned. The description does not help an agent decide between omni_swarm and its siblings.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

omni_systemsD

Infrastructure: database, postgres_client, health check, analytics, skills, seed data, sentry, OAuth, gateway, tool search, MCP apps, vault, dynamic indexing.

ParametersJSON Schema
NameRequiredDescriptionDefault
tool_targetYesThe precise sub-tool to execute within this omni-cluster.
payloadYesThe arguments payload exactly matching the target tool's native inputSchema requirements.

TDQS

D1.9/5.0
Behavior1/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations provided; description fails to disclose behavioral traits such as side effects, required permissions, or error behavior. The tool dispatches to various infrastructure actions, but the description does not indicate what happens upon invocation.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Description is brief but unstructured—a single line listing many items. It is not front-loaded with a clear purpose, and the list format reduces readability. While concise, it sacrifices clarity.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness1/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With no output schema and no annotations, the description is severely incomplete for a complex routing tool. It lacks return value details, error handling, and guidance on constructing the payload for each sub-tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Input schema covers 100% of parameters with descriptions (tool_target enum and payload object). However, the description adds no meaning beyond the schema; it does not explain the sub-tools or payload structure further. Baseline 3 per guidelines.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose2/5

Does the description clearly state what the tool does and how it differs from similar tools?

Description lists sub-tools like 'database, health check, analytics' but lacks a unified verb and resource. It does not distinguish its purpose from sibling tools; it reads as a catch-all infrastructure router.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No explicit guidance on when to use omni_systems versus siblings (omni_assistant, omni_automation, etc.). The description provides no context for appropriate usage or exclusions.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

omni_testingC

QA & Security: mobile, web, API, DB, server, desktop, accessibility, security, visual, advanced testing, security scanner, stress test, zero trust.

ParametersJSON Schema
NameRequiredDescriptionDefault
tool_targetYesThe precise sub-tool to execute within this omni-cluster.
payloadYesThe arguments payload exactly matching the target tool's native inputSchema requirements.

TDQS

C2.7/5.0
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries full burden. It does not disclose any behavioral traits such as authentication needs, rate limits, destructive potential, or output behavior. The description only lists testing types without mentioning side effects or prerequisites.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single sentence listing domains, which is concise but lacks structure. It front-loads 'QA & Security:' but then becomes a comma-separated list. It could be better organized with bullet points or clearer grammar.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a tool with multiple sub-tools and a nested payload parameter, the description is insufficient. It does not explain how to construct the payload, what each sub-tool does, or any expected output. The absence of an output schema further reduces completeness. Given the complexity, more context is needed.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema already describes both parameters (tool_target and payload) clearly with 100% coverage. The description adds no additional meaning beyond the schema, which is adequate but not enhanced. Baseline 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose3/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description lists a broad range of testing categories (mobile, web, API, etc.) but lacks a specific verb and resource. It states it's for 'QA & Security' testing, which distinguishes it from sibling tools like omni_research or omni_automation, but the purpose is vague as it does not explain what action the tool performs (e.g., execute tests, scan, etc.).

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies this tool is used for testing and security tasks, and sibling names (omni_assistant, omni_automation, etc.) provide some context. However, there is no explicit guidance on when to use this tool versus alternatives, nor any exclusions or conditions for sub-tools.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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TDQS

C2.2/5.0
Disambiguation1/5

Tools have extremely broad and overlapping descriptions (e.g., omni_assistant and omni_research both cover RAG and knowledge; omni_automation and omni_swarm both involve orchestration). An agent would struggle to distinguish which tool to use for a given task.

Naming Consistency4/5

All tools follow a consistent 'omni_<noun>' pattern. However, the nouns (assistant, automation, research, swarm, systems, testing) are vague and do not clearly indicate the tool's unique scope.

Tool Count2/5

With only 6 tools covering an enormous range of capabilities (from AI assistants to infrastructure to testing), the set is under-scoped. Each tool effectively contains multiple distinct functionalities that should probably be separate tools.

Completeness3/5

The tool set covers many domains (AI, automation, research, orchestration, infrastructure, testing), but there are noticeable gaps such as dedicated monitoring, deep analytics, or security scanning beyond basic mentions. The overlap also indicates incomplete separation of concerns.

Maintenance

ActivityInactive
ResponsivenessSyncing

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