MCP Data Analyzer
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
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 |
|---|---|
| load_fileA | Load Data File Tool Purpose: Load a local CSV or XLSX file into a DataFrame. Usage Notes: • If a df_name is not provided, the tool will automatically assign names sequentially as df_1, df_2, and so on. • For XLSX files, you can specify the sheet_name. If not provided, the first sheet will be loaded. |
| run_scriptB | Python Script Execution Tool Purpose: Execute Python scripts for specific data analytics tasks. Allowed Actions 1. Print Results: Output will be displayed as the script’s stdout. 2. [Optional] Save DataFrames: Store DataFrames in memory for future use by specifying a save_to_memory name. 3. Create Charts: You can use matplotlib.pyplot or plotly.graph_objects to create and save charts to an absolute path. Prohibited Actions 1. Overwriting Original DataFrames: Do not modify existing DataFrames to preserve their integrity for future tasks. |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
| explore-data | A prompt to explore a dataset as a data scientist |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
| Data Exploration Notes | Notes generated by the data exploration server |
TDQS
Scored across 2 tools
The two tools have completely distinct purposes: load_file is for loading data files into DataFrames, while run_script is for executing Python scripts for analytics tasks. There is no overlap in functionality or ambiguity about when to use each tool.
Both tools use snake_case naming, which is consistent. However, load_file follows a verb_noun pattern while run_script uses verb_noun, but the noun 'script' is less specific than 'file', creating a minor deviation in clarity. Overall, the naming is mostly predictable and readable.
With only 2 tools, the server feels severely under-scoped for a 'Data Analyzer' purpose. Key operations like data transformation, filtering, aggregation, or visualization-specific tools are missing, making it inadequate for comprehensive data analysis workflows.
The tool set is significantly incomplete for data analysis. While loading and script execution are covered, there are major gaps: no tools for data cleaning, transformation, statistical analysis, or dedicated visualization. This will likely cause agent failures when trying to perform common analytics tasks beyond basic loading and scripting.