Nexs MCP
JSON →NEXS MCP Server is a high-performance implementation of the Model Context Protocol, designed to manage AI elements with enterprise-grade architecture. Built with the official MCP Go SDK v1.1.0, it provides a robust foundation for AI system management.
Install
npm install -g @fsvxavier/nexs-mcp-server Tools · 56
- create_persona Creates a new AI persona with specified attributes and characteristics
- get_persona Retrieves a specific persona by ID
- update_persona Updates an existing persona's attributes
- delete_persona Deletes a persona by ID
- list_personas Lists all available personas
- create_skill Creates a new skill definition
- get_skill Retrieves a specific skill by ID
- update_skill Updates an existing skill
- delete_skill Deletes a skill by ID
- list_skills Lists all available skills
- create_template Creates a new template
- get_template Retrieves a specific template by ID
- update_template Updates an existing template
- delete_template Deletes a template by ID
- list_templates Lists all available templates
- create_agent Creates a new agent
- get_agent Retrieves a specific agent by ID
- update_agent Updates an existing agent
- delete_agent Deletes an agent by ID
- list_agents Lists all available agents
- create_memory Creates a new memory entry
- get_memory Retrieves a specific memory by ID
- update_memory Updates an existing memory
- delete_memory Deletes a memory by ID
- list_memories Lists all available memories
- create_ensemble Creates a new ensemble of agents
- get_ensemble Retrieves a specific ensemble by ID
- update_ensemble Updates an existing ensemble
- delete_ensemble Deletes an ensemble by ID
- list_ensembles Lists all available ensembles
- compress_response Compresses a response using gzip or zlib
- stream_response Streams a response in chunks
- deduplicate_content Removes duplicate content semantically
- summarize_content Summarizes content using TF-IDF
- manage_context_window Manages context window size
- cache_adaptively Caches responses adaptively
- batch_process Processes items in batch
- compress_prompt Compresses a prompt for efficiency
- store_working_memory Stores a working memory entry
- retrieve_working_memory Retrieves a working memory entry
- update_working_memory Updates a working memory entry
- delete_working_memory Deletes a working memory entry
- list_working_memories Lists all working memory entries
- search_working_memory Searches working memory
- consolidate_working_memory Consolidates working memory entries
- prune_working_memory Prunes old working memory entries
- export_working_memory Exports working memory to file
- import_working_memory Imports working memory from file
- clear_working_memory Clears all working memory
- score_quality Scores content quality using ONNX models
- assess_relevance Assesses relevance of content
- evaluate_coherence Evaluates coherence of content
- extract_entities Extracts entities from text using BERT-based NER
- detect_relationships Detects relationships between entities
- analyze_sentiment Analyzes sentiment of text
- model_topics Models topics from text with coherence scoring
Links
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