{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"npm_npm-gramatrmcp","slug":"npm-gramatrmcp","name":"@gramatr/mcp","type":"mcp","url":"https://www.npmjs.com/package/@gramatr/mcp","page_url":"https://unfragile.ai/npm-gramatrmcp","categories":["mcp-servers","app-builders","testing-quality"],"tags":["mcp","model-context-protocol","intelligence-layer","decision-routing","context-engineering","session-continuity","agent-memory","knowledge-graph","semantic-search","claude","chatgpt","codex","cursor","gemini","gramatr"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"npm_npm-gramatrmcp__cap_0","uri":"capability://planning.reasoning.request.pre.classification.and.intent.routing","name":"request pre-classification and intent routing","description":"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.","intents":["Route different types of user requests to specialized handlers without forcing the LLM to decide routing logic","Classify request intent early to inject pre-computed context and reduce LLM latency","Determine which knowledge domains or memory systems are relevant before the main inference pass"],"best_for":["AI agent builders implementing multi-step workflows with heterogeneous request types","Teams building Claude/ChatGPT integrations that need request-level filtering or preprocessing"],"limitations":["Classification accuracy depends on training data and semantic model quality — no guarantees on edge cases or novel request types","Pre-classification adds latency overhead (~50-150ms) before main LLM inference","Requires explicit intent taxonomy definition — no automatic discovery of new intent types"],"requires":["MCP-compatible client (Claude, ChatGPT, Gemini, Cursor, or Codex)","Node.js 16+ for running the MCP server","Semantic embedding model or classifier (local or API-based)"],"input_types":["text (user message/prompt)","structured metadata (session context, user profile)"],"output_types":["intent classification (enum or string)","confidence score (0-1)","routed context object"],"categories":["planning-reasoning","request-classification"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm_npm-gramatrmcp__cap_1","uri":"capability://memory.knowledge.contextual.memory.injection.with.semantic.relevance","name":"contextual memory injection with semantic relevance","description":"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.","intents":["Automatically surface relevant past conversations or decisions without the user having to explicitly reference them","Inject domain-specific knowledge or behavioral guidelines that are semantically related to the current request","Maintain conversation coherence across multiple turns by providing the LLM with filtered historical context"],"best_for":["Multi-turn agent systems where context continuity is critical (e.g., customer support, code review agents)","Knowledge-intensive applications where relevant facts must be injected without explicit user queries","Teams building stateful agents across multiple LLM providers"],"limitations":["Requires pre-computed embeddings or knowledge graph — cold-start problem for new users/domains","Semantic relevance is probabilistic — may inject irrelevant context if embedding model is weak","Context injection increases token count and latency; no automatic pruning of low-relevance memories","No built-in conflict resolution if injected context contradicts current request"],"requires":["Vector database or knowledge graph backend (e.g., Pinecone, Weaviate, Neo4j, or local embeddings)","Embedding model (OpenAI, Anthropic, or local)","Session/user identifier to scope memory retrieval","MCP server implementation with memory backend integration"],"input_types":["text (current user request)","user/session ID","optional: explicit memory filters or tags"],"output_types":["augmented prompt with injected context","relevance scores for injected memories","memory source attribution"],"categories":["memory-knowledge","context-engineering"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm_npm-gramatrmcp__cap_10","uri":"capability://automation.workflow.request.deduplication.and.caching.with.semantic.matching","name":"request deduplication and caching with semantic matching","description":"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.","intents":["Avoid redundant LLM calls for semantically similar requests by detecting and caching duplicates","Reduce token consumption and latency for repeated queries by serving cached responses","Improve response consistency by returning the same response for semantically equivalent requests"],"best_for":["High-volume applications with repeated or similar queries (customer support, FAQ systems)","Cost-sensitive applications where token savings are critical","Systems requiring consistent responses for semantically equivalent requests"],"limitations":["Semantic deduplication is probabilistic — may miss duplicates or incorrectly match dissimilar requests","Cache staleness is not automatically managed — requires explicit TTL or invalidation logic","Caching adds latency (~20-50ms per request) for embedding computation and cache lookup","No built-in cache invalidation strategy — requires external cache management"],"requires":["Cache backend (Redis, Memcached, or in-memory store)","Embedding model for semantic matching","Similarity threshold configuration (for deduplication)","MCP server implementation with caching middleware"],"input_types":["text (user request)","optional: cache key or explicit deduplication hints"],"output_types":["cached response (if duplicate found)","cache hit/miss indicator","similarity score (for debugging)"],"categories":["automation-workflow","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm_npm-gramatrmcp__cap_11","uri":"capability://safety.moderation.audit.logging.and.compliance.tracking","name":"audit logging and compliance tracking","description":"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.","intents":["Maintain audit trails of all LLM requests and responses for compliance and debugging","Track which context was injected and why for transparency and explainability","Enable compliance with regulatory requirements by recording user attribution and data handling"],"best_for":["Regulated industries (healthcare, finance, legal) requiring audit trails and compliance tracking","Enterprise applications requiring transparency and explainability of LLM decisions","Teams debugging complex multi-step workflows or investigating issues"],"limitations":["Audit logging adds latency (~10-50ms per request) for log writing and persistence","Logging large amounts of data (full requests/responses) can consume significant storage","No built-in log retention or archival policy — requires external log management","Sensitive data in logs requires careful handling and encryption"],"requires":["Logging backend (database, log aggregation service, or file system)","Log schema and retention policy configuration","Optional: encryption for sensitive data in logs","MCP server implementation with audit logging middleware"],"input_types":["request metadata","response metadata","middleware decisions (routing, context injection, validation results)"],"output_types":["audit log entries","compliance reports","debugging traces"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm_npm-gramatrmcp__cap_2","uri":"capability://memory.knowledge.session.continuity.and.state.management.across.llm.providers","name":"session continuity and state management across llm providers","description":"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.","intents":["Switch between different LLM providers mid-conversation without losing context or conversation history","Maintain user preferences and behavioral state across multiple AI tools and interfaces","Orchestrate multi-model workflows where different steps use different LLM providers but share session context"],"best_for":["Enterprise teams using multiple LLM providers and needing unified session management","Agent builders implementing provider-agnostic workflows","Applications requiring fallback or load-balancing across multiple LLM APIs"],"limitations":["Session state is provider-agnostic but not provider-optimized — may lose provider-specific context (e.g., Claude's thinking tokens, GPT-4's vision context)","Requires external session store with consistent read/write semantics — no built-in persistence","No automatic conflict resolution if session state diverges across providers","Latency overhead for session lookups and state synchronization (~50-200ms per request)"],"requires":["Session storage backend (Redis, PostgreSQL, DynamoDB, or in-memory store)","MCP server implementation with session middleware","Session ID generation and tracking mechanism","Support for at least one MCP-compatible LLM client"],"input_types":["session ID","user request","optional: provider hint or provider list for multi-model routing"],"output_types":["session state object","conversation history (filtered or full)","user context and preferences"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm_npm-gramatrmcp__cap_3","uri":"capability://safety.moderation.data.quality.enforcement.and.validation","name":"data quality enforcement and validation","description":"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.","intents":["Prevent malformed or invalid requests from wasting LLM tokens and causing errors","Enforce domain-specific data constraints (e.g., email format, numeric ranges, enum values) before processing","Validate LLM outputs against expected schemas or quality criteria before returning to user"],"best_for":["Production agent systems where data quality is critical (e.g., financial, healthcare, compliance-heavy domains)","Teams building structured data extraction or ETL pipelines with LLMs","Applications with strict input/output contracts or regulatory requirements"],"limitations":["Validation rules must be explicitly defined — no automatic schema inference","Overly strict validation may reject valid edge cases; requires tuning and maintenance","Validation adds latency (~10-50ms per request depending on rule complexity)","No built-in recovery or auto-correction — invalid data is rejected or requires manual intervention"],"requires":["Schema definition language (JSON Schema, Zod, or custom validator)","Validation rule configuration (per request type or domain)","MCP server implementation with validation middleware"],"input_types":["text (user request)","structured data (JSON, form data)","optional: schema or validation rules"],"output_types":["validation result (pass/fail)","error messages or constraint violations","transformed/sanitized data (if applicable)"],"categories":["safety-moderation","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm_npm-gramatrmcp__cap_4","uri":"capability://planning.reasoning.behavioral.context.and.instruction.injection","name":"behavioral context and instruction injection","description":"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.","intents":["Adapt LLM behavior (tone, expertise, constraints) based on user role or request context without explicit prompting","Inject domain-specific instructions or guidelines that are relevant to the current task","Enforce behavioral constraints or safety guidelines that vary by use case or user"],"best_for":["Multi-tenant or multi-use-case applications where different users need different LLM behaviors","Teams building role-based or persona-based AI agents","Applications requiring dynamic safety constraints or compliance rules"],"limitations":["Behavioral context is injected as text — increases token count and latency","No guarantee that LLM will follow injected instructions if they conflict with training or user requests","Requires careful design of behavioral libraries to avoid contradictions or confusion","No built-in monitoring of whether injected behaviors are actually followed"],"requires":["Behavioral context library or configuration (role definitions, instruction templates)","User/session profile with role or context attributes","MCP server implementation with context injection middleware"],"input_types":["user request","user role or profile","session context","optional: explicit behavior hints"],"output_types":["augmented prompt with injected behavioral context","selected role or persona","applicable constraints or guidelines"],"categories":["planning-reasoning","context-engineering"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm_npm-gramatrmcp__cap_5","uri":"capability://search.retrieval.semantic.search.and.relevance.ranking.across.knowledge.domains","name":"semantic search and relevance ranking across knowledge domains","description":"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.","intents":["Find relevant documents, past conversations, or knowledge base entries based on semantic similarity to the current request","Rank search results by relevance to improve context injection quality","Search across multiple knowledge domains (internal docs, external APIs, conversation history) with a unified interface"],"best_for":["Knowledge-intensive applications (customer support, technical documentation, research)","Teams building RAG (Retrieval-Augmented Generation) systems with multiple knowledge sources","Applications requiring cross-domain semantic search without keyword matching"],"limitations":["Search quality depends on embedding model quality and knowledge base coverage — no guarantees on completeness","Semantic search is probabilistic — may miss relevant results if embedding space is poorly aligned","Requires pre-computed embeddings for all knowledge — cold-start problem for new domains","Relevance ranking is heuristic-based — no learning from user feedback without explicit feedback loops"],"requires":["Vector database or search backend (Pinecone, Weaviate, Elasticsearch, or local embeddings)","Embedding model (OpenAI, Anthropic, or local)","Knowledge base or document corpus with embeddings pre-computed","MCP server implementation with search middleware"],"input_types":["text (search query or current request)","optional: search filters or domain hints","optional: ranking criteria or weights"],"output_types":["ranked list of relevant results","relevance scores (0-1)","source attribution and metadata"],"categories":["search-retrieval","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm_npm-gramatrmcp__cap_6","uri":"capability://automation.workflow.multi.provider.llm.orchestration.and.fallback.routing","name":"multi-provider llm orchestration and fallback routing","description":"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.","intents":["Automatically select the best LLM provider for a given request based on cost, latency, or capability requirements","Implement fallback routing to ensure reliability if a provider is unavailable or rate-limited","Load-balance requests across multiple providers to optimize cost and latency"],"best_for":["Enterprise applications requiring high availability and cost optimization across multiple LLM providers","Teams building provider-agnostic agent systems with fallback requirements","Applications with heterogeneous request types that benefit from different providers (e.g., code generation vs. creative writing)"],"limitations":["Routing decisions add latency (~50-200ms) before request is sent to provider","Fallback routing may result in inconsistent responses if different providers produce different outputs","Cost optimization requires accurate pricing models and usage tracking — no built-in cost prediction","Provider-specific optimizations require custom adapters for each provider"],"requires":["API keys for multiple LLM providers (OpenAI, Anthropic, Google, etc.)","Routing policy configuration (cost thresholds, latency targets, provider preferences)","MCP server implementation with multi-provider routing middleware","Optional: usage tracking and cost monitoring backend"],"input_types":["user request","optional: routing hints (preferred provider, cost budget, latency target)","optional: request characteristics (complexity, domain, token estimate)"],"output_types":["selected provider","routing decision rationale","LLM response","optional: cost and latency metrics"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm_npm-gramatrmcp__cap_7","uri":"capability://data.processing.analysis.request.response.transformation.and.normalization","name":"request-response transformation and normalization","description":"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.","intents":["Convert requests between different message formats (OpenAI, Anthropic, Google) without application-level logic","Normalize LLM responses to a consistent format regardless of provider","Apply formatting rules or transformations to requests and responses based on provider or use case"],"best_for":["Multi-provider applications requiring format compatibility without provider-specific code","Teams building provider-agnostic agent frameworks","Applications with strict output format requirements (JSON, markdown, structured data)"],"limitations":["Transformation logic may lose provider-specific features or optimizations (e.g., Claude's thinking tokens, GPT-4's vision context)","Normalization adds latency (~10-50ms per request) for format conversion","Custom transformations require explicit configuration — no automatic format inference","Bidirectional transformation may not be lossless for all provider features"],"requires":["Format definitions for each supported provider (OpenAI, Anthropic, Google, etc.)","Transformation rules or mappings (canonical format to provider-specific format)","MCP server implementation with transformation middleware"],"input_types":["request in any supported format (OpenAI, Anthropic, Google, etc.)","response from any LLM provider"],"output_types":["normalized request in canonical format","normalized response in canonical format","provider-specific request ready for API call"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm_npm-gramatrmcp__cap_8","uri":"capability://automation.workflow.usage.tracking.and.cost.monitoring.across.providers","name":"usage tracking and cost monitoring across providers","description":"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.","intents":["Monitor and track costs across multiple LLM providers in a unified dashboard or API","Identify cost drivers and optimize spending by analyzing usage patterns","Enforce budget limits or cost thresholds to prevent unexpected spending"],"best_for":["Enterprise teams managing multi-provider LLM spending and requiring cost visibility","Cost-sensitive applications requiring budget enforcement or cost-aware routing","Teams building internal LLM platforms with chargeback or cost allocation requirements"],"limitations":["Cost tracking is approximate — depends on accurate pricing models and token counting","Real-time cost tracking adds latency (~10-30ms per request) for logging and aggregation","No built-in cost prediction or forecasting — requires external analytics","Budget enforcement is reactive — cannot prevent requests that exceed budget, only flag or reject them"],"requires":["Usage tracking backend (database, analytics service, or logging system)","Accurate pricing models for each LLM provider","Token counting logic for each provider (or use provider-reported token counts)","MCP server implementation with usage tracking middleware"],"input_types":["request metadata (provider, model, tokens)","response metadata (tokens, cost)","optional: user or session identifier for cost allocation"],"output_types":["usage metrics (tokens, API calls, cost)","aggregated cost reports (by provider, user, session, time period)","cost alerts or budget warnings"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm_npm-gramatrmcp__cap_9","uri":"capability://text.generation.language.dynamic.prompt.composition.and.template.management","name":"dynamic prompt composition and template management","description":"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.","intents":["Compose prompts dynamically based on request context and user profile without manual engineering","Manage and version prompt templates across different use cases and domains","A/B test different prompt variations to optimize LLM output quality"],"best_for":["Teams managing multiple LLM-powered features with different prompt requirements","Applications requiring prompt versioning and A/B testing capabilities","Non-technical teams that need to modify prompts without code changes"],"limitations":["Prompt composition is template-based — requires explicit template definition and maintenance","Dynamic composition adds latency (~20-50ms per request) for template selection and variable substitution","No automatic optimization of prompts — requires manual tuning or A/B testing","Template variables must be explicitly defined — no automatic variable inference"],"requires":["Prompt template library or configuration (templates with variables and metadata)","Template selection logic (rules or heuristics for choosing appropriate templates)","MCP server implementation with prompt composition middleware","Optional: A/B testing framework or analytics backend"],"input_types":["request context","user profile or role","optional: explicit template hints or preferences"],"output_types":["composed prompt","selected template(s)","variable substitutions applied"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":39,"verified":false,"data_access_risk":"high","permissions":["MCP-compatible client (Claude, ChatGPT, Gemini, Cursor, or Codex)","Node.js 16+ for running the MCP server","Semantic embedding model or classifier (local or API-based)","Vector database or knowledge graph backend (e.g., Pinecone, Weaviate, Neo4j, or local embeddings)","Embedding model (OpenAI, Anthropic, or local)","Session/user identifier to scope memory retrieval","MCP server implementation with memory backend integration","Cache backend (Redis, Memcached, or in-memory store)","Embedding model for semantic matching","Similarity threshold configuration (for deduplication)"],"failure_modes":["Classification accuracy depends on training data and semantic model quality — no guarantees on edge cases or novel request types","Pre-classification adds latency overhead (~50-150ms) before main LLM inference","Requires explicit intent taxonomy definition — no automatic discovery of new intent types","Requires pre-computed embeddings or knowledge graph — cold-start problem for new users/domains","Semantic relevance is probabilistic — may inject irrelevant context if embedding model is weak","Context injection increases token count and latency; no automatic pruning of low-relevance memories","No built-in conflict resolution if injected context contradicts current request","Semantic deduplication is probabilistic — may miss duplicates or incorrectly match dissimilar requests","Cache staleness is not automatically managed — requires explicit TTL or invalidation logic","Caching adds latency (~20-50ms per request) for embedding computation and cache lookup","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.49,"ecosystem":0.7000000000000001,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.15,"match_graph":0.23,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:23.903Z","last_scraped_at":"2026-05-03T14:23:52.403Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=npm-gramatrmcp","compare_url":"https://unfragile.ai/compare?artifact=npm-gramatrmcp"}},"signature":"1RyOl7x7nMPDJrfV8UcC9HwDf/wrZAK0nS+mQV+qIC3JIdTzpldIY8XmVlXvgnZmu+uw3C0i+CtM77p/8VI6Dw==","signedAt":"2026-06-22T19:48:10.308Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/npm-gramatrmcp","artifact":"https://unfragile.ai/npm-gramatrmcp","verify":"https://unfragile.ai/api/v1/verify?slug=npm-gramatrmcp","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}