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Multi-Agent Business Intelligence Architecture for E-commerce using Chain of Thought reasoning. Provides specialized agents for data intelligence, revenue/margin analysis, financial modeling, competitor intelligence, customer insights, operations/inventory, scenario/risk analysis, execution, and global constraint identification.
ProfitPilot MCP Server exhibits significant quality gaps across naming, descriptions, schemas, and error handling. While tool names follow basic conventions (profitpilot_*), descriptions lack specificity about when to use each tool and what constitutes success. Parameter descriptions are sparse or generic. Input schemas are present but lack granularity, notably, the 'role' enum and 'sessionId' handling show good structure, but there is no documented output schema for any tool, making it impossible for LLMs to plan downstream operations. Error handling is absent: no guidance on recovery, no categorization of error types, and no examples of failure modes. The stateful session management (activeSessions Map) couples tool behavior to server state, violating modern MCP statelessness principles. Tools 1 - 4 and 5 - 8 are near-duplicates with no justification for the split, suggesting poor composition. Security: sessionId is client-generated without validation; no rate limiting or permission gates. This server would not pass production code review.
Ejecuta el pipeline completo de ProfitPilot usando Chain of Thought. Este servidor NO usa LLMs externos. Genera prompts estructurados que el cliente MCP debe ejecutar con su propio modelo. El flujo es: 1. Cliente llama a profitpilot_analyze con el problema 2. Servidor genera un prompt de razonamiento 3. Cliente ejecuta el prompt con su modelo 4. Cliente llama a profitpilot_continue con la respuesta 5. Se repite hasta completar el análisis ProfitPilot combina agentes especializados en: - Data Intelligence (análisis de métricas y anomalías) - Revenue & Margin (LTV, CLV, márgenes por canal) - Financial Modeling (cash flow, working capital, payback) - Competitor Intelligence (precios, tendencias del mercado) - Customer Insight (comportamiento, churn prediction) - Operations & Inventory (forecasting, stock levels) - Scenario & Risk Engine (simulación de escenarios) - Execution Agent (acciones automáticas) - Global Constraint Strategist (cuellos de botella globales)
Continúa una sesión de razonamiento con la respuesta del cliente. Debe llamarse después de ejecutar el prompt generado por profitpilot_analyze o profitpilot_continue.
Continúa una sesión de razonamiento con la respuesta del cliente. Debe llamarse después de ejecutar el prompt generado por profitpilot_cot_start o profitpilot_cot_continue.
Reinicia una sesión de razonamiento.
No documented output schemas for any tool. LLMs cannot determine what fields to extract from responses, what data is available for downstream planning, or how to chain tool calls. The code references 'stepResult.step' and 'stepResult.prompt' but these structures are not documented for agents.
Duplicate tool pairs (analyze/continue/status/reset vs cot_start/cot_continue/cot_status/cot_reset) with no clear distinction. LLMs will waste reasoning cycles choosing between nearly identical tools. Tool descriptions do not explain when to use analyze vs cot_start.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-22 | F | 44 | 2026-07-28+ | v2 |
| 2026-03-09 | F | 39 | - | v1 |
Inicia una sesión de razonamiento guiado con Chain of Thought. Este servidor NO usa LLMs externos. En su lugar, genera prompts estructurados que el cliente MCP debe ejecutar con su propio modelo. El flujo es: 1. Cliente llama a profitpilot_cot_start con el problema 2. Servidor genera un prompt de razonamiento 3. Cliente ejecuta el prompt con su modelo 4. Cliente llama a profitpilot_cot_continue con la respuesta 5. Servidor genera el siguiente prompt 6. Se repite hasta completar el análisis Compatible con cualquier cliente MCP: Claude Desktop, Cursor, etc.
Obtiene el estado actual de una sesión de razonamiento.
Reinicia una sesión de razonamiento.
Obtiene el estado actual de una sesión de razonamiento.
No error handling or recovery guidance. Tools lack error classification (retryable vs user-fixable vs fatal). Examples: What happens if sessionId is invalid? What if response is empty? What if the analysis times out? No guidance for LLM recovery.
Descriptions are under 100 chars for 6 of 8 tools (status, reset, cot_status, cot_reset), critically insufficient for LLM tool selection. Baseline for A+ tools is 194 chars average.
Stateful session management (activeSessions Map keyed by sessionId) violates MCP statelessness principle. Each request should be self-contained. Server state can be lost on restart; agents have no way to recover in-flight analyses.
sessionId is client-generated with no validation (sessionId || `session_${Date.now()}`). An LLM could pass a malicious string or an already-active sessionId, causing state collisions. No rate limiting or permission checks.
Parameter 'role' enum and 'verbose' boolean lack inline description repeating the constraint. E.g., 'role: enum [ceo, cmo, ...]' should be mirrored in the description: 'Business role: one of ceo, cmo, ecommerce, operations, growth, cfo.'
No documentation of what happens at session boundaries: How many turns is a session? What is the max response length? When does a session expire? How does an LLM know when analysis is complete vs when it needs to call continue again?