Insight Digger MCP
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| MCP_CLIENT_URL | No | URL of the MCP Client Flask API service. By default, the system will use Sandsiv's hosted MCP service. Only provide this variable if you're deploying your own version of the data-narrator-mcp service. | |
| INSIGHT_DIGGER_API_URL | No | Backend API URL (configured in MCP server layer). |
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 |
|---|---|
| setup_authenticationA | Setup authentication credentials for Sandsiv+ Insight Digger. This must be called FIRST before any analysis tools become available. Requires your API URL and JWT token. About This System:
When to Use This System:
Capabilities:
Workflow: 7-step process: Authentication → Source Discovery → Structure Analysis → Strategy Planning → Configuration → Dashboard Creation → Insight Generation |
| validate_settingsA | 🔒 [Requires Authentication] Validate API settings by testing the connection to the external data API. 🔄 Auto-Cached: apiUrl and jwtToken are automatically provided from your authentication session - you typically don't need to provide these parameters. Args: apiUrl (str), jwtToken (str). Returns: {{'status': 'success'|'error', 'error': str (if status == 'error')}}. ⚠️ Please authenticate first by calling the |
| list_sourcesA | 🔒 [Requires Authentication] List available data sources. This is typically the FIRST interactive step in data analysis. Ask user to provide a search term to filter the sources. 🔄 Auto-Cached: apiUrl and jwtToken are automatically provided from your authentication session. Only provide 'search', 'page', and 'limit' parameters as needed. Ask the user for a source name to search first. Returns: {{'count': int, 'data': [{'id': str, 'title': str, 'type': str, 'updated': str, 'numberOfColumns': int}]}}. ⚠️ Please authenticate first by calling the |
| analyze_source_structureA | 🔒 [Requires Authentication] Fetches and analyzes the structure of a given data source. This is a key step after selecting a source. 🔄 Auto-Cached: apiUrl and jwtToken are automatically provided from authentication. Only provide the 'sourceId' parameter (from the previous list_sources step). Returns detailed column analysis to help formulate analytical questions. ⚠️ Please authenticate first by calling the |
| generate_strategyA | 🔒 [Requires Authentication] Generate analysis strategy for a question and column analysis. This is a granular step in the analysis workflow. 🔄 Auto-Cached: 'columnAnalysis' is automatically provided from the previous analyze_source_structure step. Only provide the 'question' parameter from the user. ⚠️ Please authenticate first by calling the |
| create_configurationA | 🔒 [Requires Authentication] Create dashboard configuration from question, column analysis, and strategy. This is a granular step in the analysis workflow. 🔄 Auto-Cached: 'question', 'columnAnalysis', and 'strategy' are automatically provided from previous steps. You typically don't need to provide any parameters for this tool. ⚠️ Please authenticate first by calling the |
| generate_configA | 🔒 [Requires Authentication] Generate dashboard config (markdown) from question and source structure. 🔄 Auto-Cached: 'sourceStructure' is automatically provided from analyze_source_structure step. 'apiUrl' and 'jwtToken' are provided from authentication. Only provide the 'question' parameter from the user. Returns: dict (markdown configuration). ⚠️ Please authenticate first by calling the |
| create_dashboardA | 🔒 [Requires Authentication] Create a dashboard from config, source structure, and API settings. This is a granular step in the analysis workflow. 🔄 Auto-Cached: 'markdownConfig' is provided from create_configuration step, 'sourceStructure' from analyze_source_structure, and 'apiUrl'/'jwtToken' from authentication. You typically don't need to provide any parameters for this tool. ⚠️ Please authenticate first by calling the |
| get_charts_dataA | 🔒 [Requires Authentication] Fetch data for multiple charts. This step is granular. 🔄 Auto-Cached: 'chartConfigs' is automatically provided from the create_dashboard step, and 'apiUrl'/'jwtToken' from authentication. You typically don't need to provide any parameters for this tool. Returns a summary of fetched charts (chart names). The full data is cached for the next step. ⚠️ Please authenticate first by calling the |
| analyze_chartsA | 🔒 [Requires Authentication] Analyzes data from all charts and returns detailed insights. This is the final analytical step. 🔄 Auto-Cached: 'chartData' is automatically provided from get_charts_data step, 'question' from the workflow, and 'apiUrl'/'jwtToken' from authentication. You typically don't need to provide any parameters for this tool. After receiving the insights, you MUST synthesize them into a final report for the user. Your report should:
⚠️ Please authenticate first by calling the |
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 10 tools
Most tools have distinct purposes aligned with a clear 7-step workflow, but there is some overlap between create_configuration and generate_config that could cause confusion. Both tools generate dashboard configurations, with create_configuration being described as a 'granular step' and generate_config as generating 'markdown configuration', making their boundaries unclear. Other tools like analyze_charts, analyze_source_structure, and list_sources are well-differentiated.
The naming follows a mostly consistent verb_noun pattern (e.g., analyze_charts, list_sources, generate_strategy), but there are inconsistencies like setup_authentication (verb_noun) vs. validate_settings (verb_noun, but 'settings' is plural and less specific) and create_configuration vs. generate_config (different verbs for similar actions). While readable, these deviations reduce overall consistency.
With 10 tools, the count is well-scoped for the server's purpose of transforming business questions into dashboards and insights. Each tool corresponds to a step in the described 7-step workflow, such as authentication, source discovery, and insight generation, ensuring that every tool earns its place without being excessive or insufficient.
The tool set provides complete coverage for the data analysis workflow, from authentication (setup_authentication) to insight generation (analyze_charts). It includes all necessary steps like source listing, structure analysis, strategy planning, configuration, dashboard creation, and data fetching, with no obvious gaps. The tools support the full lifecycle, enabling agents to execute end-to-end analyses without dead ends.