MongTap
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| NODE_ENV | No | Node.js environment setting | production |
| LOG_LEVEL | No | Logging level (error, warn, info, debug, trace) | info |
| MONGTAP_PORT | No | Default port for MCP server | 3000 |
| MONGTAP_STORAGE | No | Path for model storage | ./welldb-models |
| MONGTAP_MAX_SERVERS | No | Maximum concurrent MongoDB servers | 10 |
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 |
|---|---|
| generateDataModelC | Create a statistical model from sample documents or a text description for data generation |
| startMongoServerC | Start a local MongoDB-compatible server that generates data from statistical models |
| stopMongoServerB | Stop a running MongoDB-compatible server instance by port number |
| listActiveServersB | Get a list of all currently running MongoDB-compatible server instances |
| queryModelC | Generate documents from a statistical model with optional query filters and generation control ($seed for reproducibility, $entropy for randomness) |
| trainModelC | Update an existing statistical model with additional sample documents to improve generation quality |
| listModelsB | Get a list of all available statistical models stored locally |
| getModelInfoC | Retrieve detailed schema and statistics for a specific statistical model |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
| create_ecommerce_db | Create a complete e-commerce database with products, customers, and orders |
| create_user_profile | Generate a user profile model with common fields |
| analyze_model | Analyze an existing model and provide insights |
| generation_control | Learn how to control document generation with $seed and $entropy parameters |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
| Available Models | List of all trained DataFlood models |
| Server Status | Status of all MongoDB servers |
| Model Schema | Get the JSON schema for a specific model |
| Model Sample Data | Get sample data from a model |
| Generation Control Documentation | Documentation for $seed and $entropy query parameters |
TDQS
Scored across 8 tools
Each tool has a clearly distinct purpose with no overlap: generateDataModel creates models, getModelInfo retrieves model details, listActiveServers lists running servers, listModels lists available models, queryModel generates documents, startMongoServer starts servers, stopMongoServer stops servers, and trainModel updates models. The descriptions clearly differentiate between model management, server management, and data generation operations.
The naming is mostly consistent with a verb_noun pattern (e.g., generateDataModel, getModelInfo, listActiveServers), but there are minor deviations: 'queryModel' uses a verb that doesn't perfectly match the others (though it's still clear), and the capitalization is consistent (camelCase). The pattern is predictable enough for agents to understand the tool purposes.
With 8 tools, this is well-scoped for the server's purpose of managing statistical models and MongoDB-compatible servers for data generation. Each tool earns its place by covering distinct aspects like model creation, querying, server control, and listing operations, without being overly sparse or bloated.
The tool set provides complete coverage for the domain of statistical model-based data generation and server management. It includes CRUD-like operations for models (generate, get, list, train), server lifecycle control (start, stop, list), and data generation (queryModel), with no obvious gaps that would cause agent failures in this context.