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Advanced multi-modal AI assistant with unified superintelligence, inspired by JARVIS. Exposes capabilities via MCP (Model Context Protocol) with support for text, voice, vision, face recognition, emotion detection, and domain expertise across health, career, finance, relationships, and more.
This MCP server exhibits severe definition quality issues across nearly all dimensions. Tool descriptions are either absent, trivial, or marketing-focused rather than specification-focused. Most parameters lack type information or meaningful descriptions. Output schemas are not documented. The server mixes MCP protocol tools (initialize, tools/list, resources/list) with application-specific tools (process-text, recognize-face, analyze-perception) without clear separation. Error handling is minimal. The codebase shows partial MCP integration but lacks rigor in tool definition. Of 17 tools, only 8 have any input schema visible; most are incomplete. The tools that do have schemas (tools/call, prompts/get, resources/read) have adequate but minimal descriptions. Application tools like 'process-text', 'analyze-perception', and 'predict-needs' have vague descriptions that do not specify what data is returned, when to use them, or error conditions. No tool declares output schemas. No tool includes parameter validation guidance or error recovery paths. This server would not pass code review for production use.
Advanced perception analysis: micro-expressions, body language, physiology. Get true emotional state beyond what the user says
Handle completion request
Return JARVIS capabilities and features including voice interaction, face recognition, NLP, task execution, learning, emotion recognition, and more
Retrieve JARVIS memory and context including conversation history, learned patterns, and user preferences
Comprehensive system health check
Handle MCP initialize request - returns protocol version, server capabilities, and server info
Allow JARVIS to learn new patterns and preferences
No output schemas documented for any tool. LLMs cannot plan multi-step sequences or extract expected fields without knowing what each tool returns.
Vague application-domain descriptions. 'Process text input through unified superintelligence - routes through all analytical subsystems' is marketing language, not a specification. Does it call external APIs? What does it return? When would an agent choose this over prompt completion?
Parameters lack descriptions for required inputs. 'recognize-face' accepts 'file' (described as 'Image file content') but does not specify format (base64? binary? URL?), size limits, or acceptable image types (JPEG? PNG? BMP?).
Inferred effective spec: <=2025-11-25.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-22 | F | 40 | <=2025-11-25 | v2 |
| 2026-03-09 | F | 24 | 2024-11-05+ | v1 |
Pre-cognitive prediction of user needs based on patterns, context, and subtle behavioral cues
Process text input through unified superintelligence - routes through all analytical subsystems for comprehensive understanding
Process voice input (speech-to-text + understanding) and return text response with optional audio
Get a prompt template by name with provided arguments
List available prompt templates
Upload image for face recognition - returns identified user and emotional context
List available resources that MCP clients can access
Read a specific resource by URI
Execute a tool by name with provided arguments
List available tools with descriptions and input schemas
Ambiguous parameter names. 'file' in recognize-face and process-voice is undefined, is it a file path, base64 content, or multipart payload? MCP HTTP does not typically handle file uploads; this interface is non-standard.
No error handling guidance. Tools like 'process-text' and 'learn' offer no hints on failure modes: What if speech recognition fails? What if the face is not recognized? What if learning data is malformed?
tools/call accepts 'arguments' as an unconstrained object. No schema validates argument structure against the named tool. An LLM could pass arbitrary fields without detection.
Optional parameters not clearly marked. 'process-text' has user_id as required, but context, observations, conversation_history, and credentials are implied optional without explicit minProperties: 0 or description of fallback behavior.
Sensitive data exposure risk. 'credentials' parameter in process-text invites agents to pass API keys, tokens, or passwords as tool input. This is a security anti-pattern, credentials should be server-side injected.
Incomplete parameter descriptions. 'conversation_history' and 'observations' are typed as arrays but have no description of element structure. Is it an array of strings? Objects with role/content fields? Undefined structures force LLMs to guess.
No idempotency guidance. The 'learn' tool modifies state but offers no idempotency key or deduplication mechanism. If an agent retries after a timeout, will it create duplicate learning records?