@gramatr/mcp

Analyzes incoming user requests before they reach the LLM to classify intent type, extract semantic meaning, and route to appropriate handlers or memory contexts. Uses semantic classification patterns to determine whether a request is a query, command, context-setting, or multi-step task, enabling downstream systems to prepare relevant data and behavioral context before processing.

Retrieves and injects relevant memory, knowledge, and behavioral context into the LLM's input based on semantic similarity to the current request. Uses vector embeddings or knowledge graph traversal to identify related past interactions, domain knowledge, and user preferences, then prepends or augments the prompt with this context to improve response quality and consistency without explicit retrieval calls from the LLM.

Detects and deduplicates semantically similar requests using embedding-based matching, and caches responses to avoid redundant LLM calls. Identifies requests that are semantically equivalent despite different wording, retrieves cached responses for duplicates, and updates cache based on response quality and staleness. Reduces token consumption and latency for repeated or similar queries without requiring exact string matching.

Logs all requests, responses, and decisions made by the middleware for audit, compliance, and debugging purposes. Records request metadata, selected context, routing decisions, cost information, and response data with timestamps and user attribution. Enables compliance with regulatory requirements (HIPAA, GDPR, SOC 2) and provides visibility into system behavior for debugging and optimization.

Maintains consistent session state, conversation history, and user context across multiple LLM providers (Claude, ChatGPT, Gemini, Cursor, Codex) by storing and retrieving session metadata through a unified MCP interface. Tracks conversation turns, user preferences, and behavioral state independently of the underlying LLM provider, enabling seamless switching between models or multi-model orchestration without losing context.

Validates and enforces data quality constraints on requests and responses before they reach the LLM or are returned to the user. Applies schema validation, type checking, format verification, and domain-specific rules to ensure data integrity and consistency. Rejects or transforms invalid data according to configurable policies, preventing malformed inputs from reaching the LLM and ensuring outputs meet quality standards.

Injects dynamic behavioral instructions, system prompts, and role-based context into the LLM's input based on the current request, user profile, and session state. Selects and composes appropriate behavioral guidelines, tone, expertise level, and constraints from a configurable library, enabling the same LLM to adapt its behavior across different use cases without explicit user prompts or model fine-tuning.

Performs semantic search across multiple knowledge domains (documents, past conversations, knowledge graphs, external APIs) to find relevant information for the current request. Uses embedding-based similarity matching and optional relevance ranking to surface the most contextually appropriate results, enabling the LLM to access domain-specific knowledge without explicit user queries or keyword matching.

Routes requests across multiple LLM providers (Claude, ChatGPT, Gemini, Codex, Cursor) based on request characteristics, provider availability, cost, or performance criteria. Implements fallback logic to automatically retry failed requests with alternative providers, load-balancing strategies to distribute requests across providers, and provider-specific optimizations to maximize quality and minimize latency.

Transforms and normalizes requests and responses to ensure compatibility across different LLM providers and client interfaces. Converts between different message formats, handles provider-specific response structures, applies formatting rules, and normalizes output to a canonical format. Enables seamless switching between providers without requiring application-level format conversion or provider-specific handling.

Tracks token usage, API calls, and costs across multiple LLM providers in real-time. Aggregates usage metrics by provider, user, session, or request type, and provides visibility into spending patterns and cost drivers. Enables cost-aware routing decisions and budget enforcement without requiring manual tracking or post-hoc analysis.

Manages a library of prompt templates and dynamically composes prompts based on request context, user profile, and behavioral requirements. Selects appropriate templates, fills in variables, and combines multiple templates to create context-aware prompts without requiring manual prompt engineering for each request. Enables version control and A/B testing of prompts across different use cases.