Volume Wall Detector MCP
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| PORT | No | Server port when not using stdio transport | 8080 |
| TIMEZONE | No | The timezone setting for the server | GMT+7 |
| PAGE_SIZE | No | Number of items per page | 50 |
| MONGO_HOST | No | MongoDB server hostname | localhost |
| MONGO_PORT | No | MongoDB server port | 27017 |
| MONGO_USER | No | MongoDB username | admin |
| API_BASE_URL | Yes | Your stock market API URL | |
| DAYS_TO_FETCH | No | Number of days of data to fetch | 1 |
| MONGO_DATABASE | No | MongoDB database name | volume_wall_detector |
| MONGO_PASSWORD | No | MongoDB password | password |
| TRANSPORT_TYPE | No | Transport type for MCP communication | stdio |
| TRADES_TO_FETCH | No | Number of trades to fetch | 10000 |
| MONGO_AUTH_SOURCE | No | MongoDB authentication source | admin |
| MONGO_AUTH_MECHANISM | No | MongoDB authentication mechanism | SCRAM-SHA-1 |
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
Server capabilities have not been inspected yet.
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| fetch-order-bookC | Fetch current order book data for a symbol |
| fetch-tradesC | Fetch recent trades for a symbol |
| analyze-stockC | Analyze stock data including volume and value analysis |
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 3 tools
Each tool has a clearly distinct purpose: analyze-stock focuses on data analysis, fetch-order-book retrieves order book data, and fetch-trades gets trade history. There is no overlap in functionality, making it easy for an agent to select the right tool without confusion.
The naming is mostly consistent with a verb-noun pattern (analyze-stock, fetch-order-book, fetch-trades), using kebab-case throughout. The minor deviation is that analyze-stock uses 'analyze' while the others use 'fetch', but this is reasonable given the different actions.
With only 3 tools, the set feels thin for a server named 'Volume Wall Detector MCP', which suggests a focus on volume analysis in trading. While the tools cover basic data fetching and analysis, more tools might be expected for comprehensive volume detection or trading operations.
The tools provide core data retrieval (order book, trades) and analysis, but there are notable gaps for a volume-focused detector, such as tools for real-time volume alerts, historical volume trends, or integration with trading actions. The surface is functional but incomplete for advanced volume analysis workflows.