Excel MCP Server
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| GEMINI_API_KEY | No | Your Gemini API key for AI-powered features | |
| OPENAI_API_KEY | No | Your OpenAI API key for AI-powered features | |
| DEEPSEEK_API_KEY | No | Your DeepSeek API key for AI-powered features | |
| ANTHROPIC_API_KEY | No | Your Anthropic API key for AI-powered features |
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 |
|---|---|
| read_fileB | Read an entire CSV or Excel file with optional chunking for large files |
| get_cellC | Get the value of a specific cell using A1 notation |
| get_rangeC | Get values from a range of cells |
| get_headersB | Get the column headers (first row) of a file |
| searchC | Search for cells containing a specific value |
| filter_rowsC | Filter rows based on column values |
| aggregateC | Perform aggregation operations on a column |
| read_file_chunkedB | Read large files in manageable chunks to avoid token limits |
| get_file_infoC | Analyze file size and get chunking recommendations |
| statistical_analysisC | Perform comprehensive statistical analysis on a column |
| correlation_analysisC | Calculate correlation between two numeric columns |
| data_profileC | Generate comprehensive data profiling report for all columns |
| pivot_tableC | Create pivot table with grouping and aggregation |
| dcf_analysisC | Perform Discounted Cash Flow (DCF) valuation analysis for investment evaluation |
| budget_variance_analysisC | Analyze budget vs actual performance with variance calculations |
| ratio_analysisC | Perform comprehensive financial ratio analysis with industry benchmarks |
| scenario_modelingC | Perform what-if scenario analysis with multiple assumptions |
| trend_analysisC | Analyze time series trends, growth rates, seasonality, and forecasting for sales and performance data |
| write_fileC | Write data to a new CSV or Excel file (supports multiple sheets for Excel) |
| add_sheetC | Add a new sheet to an existing Excel file |
| write_multi_sheetC | Create a complex Excel file with multiple sheets, formulas, and inter-sheet references |
| export_analysisC | Export analysis results (pivot tables, statistics, etc.) to a new file |
| format_cellsC | Apply formatting to Excel cells (fonts, colors, borders, alignment) |
| auto_fit_columnsB | Automatically adjust column widths to fit content in Excel files |
| evaluate_formulaC | Evaluate an Excel formula with given context |
| parse_natural_languageC | Convert natural language to Excel formula or command |
| explain_formulaB | Explain what an Excel formula does in plain English |
| ai_provider_statusB | Check status of available AI providers |
| smart_data_analysisC | AI-powered analysis suggestions for your data |
| validate_data_consistencyC | Cross-validate data integrity across related files |
| bulk_aggregate_multi_filesB | Aggregate same column across multiple files in parallel |
| bulk_filter_multi_filesC | Filter data across multiple files with optional export |
| find_duplicatesC | Find and manage duplicate rows in Excel/CSV files with multiple strategies |
| data_cleanerC | Batch data cleaning operations with intelligent detection of common data quality issues |
| vlookup_helperB | Intelligent VLOOKUP setup and execution with error handling and fuzzy matching |
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 35 tools
The tools cover distinct Excel/data analysis functions, but there is significant overlap in some areas. For example, 'read_file' and 'read_file_chunked' serve similar purposes with minor differences, and 'bulk_aggregate_multi_files' and 'aggregate' could be confused for related operations. Descriptions help clarify, but the high count increases potential for misselection.
Naming conventions are mixed, with some tools using verb_noun patterns (e.g., 'add_sheet', 'filter_rows') and others using noun_verb or descriptive phrases (e.g., 'data_cleaner', 'dcf_analysis'). While readable, the lack of a uniform pattern across all 35 tools reduces predictability and consistency.
With 35 tools, the server feels overloaded for an Excel-focused domain. Many tools could be consolidated or parameterized (e.g., 'read_file' and 'read_file_chunked'), leading to a heavy and potentially confusing interface. This exceeds the typical well-scoped range of 3-15 tools, indicating poor scoping.
The tool set provides extensive coverage for Excel operations, including data I/O, analysis, formatting, and advanced functions like AI integration and financial modeling. Minor gaps exist, such as limited editing capabilities for existing files, but agents can work around these given the broad functionality available.