Neuro MCP V2
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| TAVILY_API_KEY | Yes | Your Tavily API key for web search capabilities |
Instructions
Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.
This server publishes no instructions, or was last inspected before Glama recorded them.
Capabilities
Features and capabilities supported by this server
Protocol revision2025-11-25
| Capability | Details |
|---|---|
| tools | {
"listChanged": true
} |
| logging | {} |
| prompts | {
"listChanged": false
} |
| resources | {
"subscribe": false,
"listChanged": false
} |
| extensions | {
"io.modelcontextprotocol/ui": {}
} |
| experimental | {} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| read_workspace_fileA | Read the full text contents from a specific file strictly inside the sandboxed workspace folder. Use this to get context from previous documents or code files. |
| write_workspace_fileA | Write or overwrite text content to a specific file inside the sandboxed workspace folder. Automatically creates directories if they do not exist. Use this to save notes, code, or outputs. |
| store_persistent_memoryA | Store long-term semantic context or user preferences permanently in the vector database. Use this when the user explicitly asks you to remember something or when key insights are uncovered. |
| recall_persistent_memoryA | Query the persistent vector database to recall past context, preferences, or project details. Use this to check if you have existing knowledge on a topic the user mentions. |
| tavily_web_searchB | Search the live web using the Tavily API for highly optimized real-time technical answers, documentation, news, or updates. |
| analyze_text_emotionA | Locally run a deep learning classification pipeline to detect semantic emotional markers (Joy, Sadness, Anger, Fear, Surprise, Disgust, Neutral) inside a block of text. Use this to adapt your tone or better understand user sentiment. |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
No prompts | |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
No resources | |
TDQS
Scored across 6 tools
Each tool has a clearly distinct purpose: emotion analysis, file reading, memory recall, memory store, web search, and file writing. No overlap in functionality.
All tools follow a verb_noun pattern (e.g., analyze_text_emotion, read_workspace_file). The only minor deviation is 'tavily_web_search' which includes a brand name, but it still fits the pattern.
Six tools is well within the ideal 3-15 range. The tool count matches the server's purpose of providing a capable assistant with memory, file operations, web search, and emotion analysis.
Core operations are covered: file read/write, memory recall/store, web search, and text analysis. Minor gaps include lack of file deletion or memory deletion, but the set enables key workflows.