Excel MCP Server

by yzfly

Overview Schema Related Servers Score Discussions

Python

Hybrid

Server Quality Checklist

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Latest release: v1.0.0
Disambiguation4/5
Most tools have distinct purposes like reading, writing, filtering, and analyzing Excel data, but analyze_excel and data_summary overlap significantly as both perform data analysis/summarization. The descriptions help differentiate them slightly, but an agent might struggle to choose between them for basic statistical tasks.
Naming Consistency5/5
All tools follow a consistent verb_noun or verb_excel pattern (e.g., read_excel, update_excel, filter_excel, pivot_table). The naming is predictable and readable throughout, with no mixing of conventions like camelCase or snake_case deviations.
Tool Count5/5
With 8 tools, this server is well-scoped for Excel operations, covering core tasks like reading, writing, updating, filtering, analyzing, summarizing, charting, and pivoting. Each tool earns its place without feeling excessive or insufficient for the domain.
Completeness4/5
The toolset provides strong coverage for Excel data manipulation and analysis, including CRUD-like operations (read, write, update) and advanced features (pivot tables, charts). A minor gap exists in lacking tools for specific Excel formatting or cell-level edits, but agents can work around this with the available tools.
Average 3.1/5 across 8 of 8 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- 0 of 1 community issues answered or closed in the last 6 months
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This repository is licensed under MIT License.
This repository includes a README.md file.
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How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It states the tool performs statistical analysis and returns JSON, but lacks critical details like what statistical methods are used, whether it modifies the Excel file, error handling for invalid data, or performance considerations. This is inadequate for a tool with 3 parameters and no annotation coverage.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with a clear purpose statement followed by 'Args' and 'Returns' sections, making it easy to parse. It's concise with no redundant information, though the lack of usage guidelines slightly reduces efficiency. Every sentence adds value, earning a high score for structure.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (3 parameters, no annotations, no output schema), the description is incomplete. It doesn't explain the statistical analysis output in detail (e.g., what metrics are included), error conditions, or how it interacts with sibling tools. For a data analysis tool with multiple parameters, this leaves significant gaps for an AI agent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description adds some semantic context beyond the input schema, which has 0% description coverage. It explains that 'columns' is a comma-separated list and defaults to analyzing all numeric columns if None, and that 'sheet_name' is for Excel files. However, it doesn't clarify the format for 'file_path' (e.g., local vs. remote) or provide examples, leaving gaps in parameter understanding.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with a specific verb ('perform statistical analysis') and resource ('on Excel data'), making it immediately understandable. However, it doesn't differentiate this tool from potential sibling tools like 'data_summary' or 'read_excel' that might also analyze Excel data, preventing a perfect score.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives like 'data_summary', 'filter_excel', or 'pivot_table' from the sibling list. It mentions parameters but doesn't explain the tool's specific use cases, prerequisites, or exclusions, leaving the agent with minimal contextual direction.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions generating a 'comprehensive summary' but doesn't specify what that entails (e.g., statistical summaries, data types, missing values, or format details). It also lacks information on permissions, file size limits, error handling, or performance characteristics, which are critical for a tool that reads and processes Excel files.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately sized and front-loaded, with the main purpose stated first. The Args and Returns sections are structured clearly, though the 'Returns' section is somewhat vague ('Comprehensive data summary as string'). There's minimal waste, but it could be more precise in defining the output format.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of processing Excel data, no annotations, no output schema, and low schema coverage, the description is incomplete. It doesn't explain what a 'comprehensive summary' includes, how errors are handled, or any limitations (e.g., file size, supported Excel versions). For a tool with 2 parameters and no structured safety hints, this leaves significant gaps for an AI agent to use it correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate. It adds basic meaning by explaining that 'file_path' is the 'Path to the Excel file' and 'sheet_name' is the 'Name of the sheet to summarize (for Excel files)', which clarifies their roles beyond the schema's titles. However, it doesn't provide details on accepted file formats, path constraints, or sheet name handling when null, leaving gaps in parameter understanding.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Generate a comprehensive summary of the data in an Excel file.' This specifies the verb ('generate'), resource ('data in an Excel file'), and output type ('comprehensive summary'). However, it doesn't explicitly differentiate from sibling tools like 'analyze_excel' or 'read_excel', which might have overlapping functionality.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. With sibling tools like 'analyze_excel', 'filter_excel', 'pivot_table', and 'read_excel' available, there's no indication of what makes this tool unique or when it should be preferred over other data processing tools. Usage is implied only by the general purpose statement.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. While 'update' implies a mutation, it doesn't specify whether this overwrites existing data, appends to it, or modifies in place. It also lacks details on permissions, error handling, or what 'Confirmation message' entails, leaving significant gaps in understanding the tool's behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured and front-loaded with the core purpose, followed by parameter details. It uses bullet points for clarity and avoids unnecessary fluff. However, the 'Returns' section is vague ('Confirmation message'), which slightly reduces efficiency, but overall it's concise and organized.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (4 parameters, mutation operation) and lack of annotations or output schema, the description is incomplete. It doesn't explain the update behavior (e.g., overwrite vs. append), error conditions, or what the return value contains. For a tool with no structured support, this leaves too many unknowns for effective use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description lists all four parameters with brief explanations, but the input schema has 0% description coverage, meaning parameters are undocumented in the schema. The description adds basic semantics (e.g., 'Path to the Excel file to update'), but it doesn't fully compensate for the schema gap—details like data format specifics or sheet name constraints are missing, keeping it at a baseline level.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Update an existing Excel file with new data.' It specifies the verb ('update') and resource ('Excel file'), making it easy to understand what the tool does. However, it doesn't explicitly differentiate from sibling tools like 'write_excel' or 'filter_excel', which prevents a perfect score.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. With siblings like 'write_excel' and 'filter_excel' available, there's no indication of whether this tool is for appending data, overwriting, or modifying specific cells, nor any prerequisites or exclusions mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions creating and returning an image, implying a read-only operation that generates output, but lacks details on permissions, file access requirements, error handling, or performance aspects like size limits. For a tool with 5 parameters and no annotation coverage, this is a significant gap in transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately sized and front-loaded, starting with a clear purpose statement followed by a structured list of args and returns. Every sentence earns its place by providing essential information without redundancy, making it easy to scan and understand quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity (5 parameters, no annotations, no output schema), the description is moderately complete. It covers the purpose and parameters well but lacks behavioral context and usage guidelines. The absence of an output schema means the description should ideally explain return values more, though it does state 'Chart as image.' Overall, it's adequate but has clear gaps for a tool of this nature.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description adds substantial meaning beyond the input schema, which has 0% description coverage. It explains each parameter's purpose (e.g., 'x_column: Column to use for x-axis'), clarifies that 'sheet_name' is for Excel files, and lists valid 'chart_type' options. This compensates well for the schema's lack of descriptions, though it doesn't cover defaults or null handling explicitly.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Create a chart from Excel data and return as an image.' It specifies the verb ('create'), resource ('chart from Excel data'), and output format ('image'), making it easy to understand what the tool does. However, it doesn't explicitly differentiate from sibling tools like 'analyze_excel' or 'data_summary', which might also involve charting or visualization.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention sibling tools or contexts where other tools might be more appropriate, such as using 'analyze_excel' for statistical insights or 'read_excel' for raw data extraction. Without this, users must infer usage based on the purpose alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool filters data and returns it as a string, but lacks details on error handling (e.g., invalid file paths or query syntax), performance implications (e.g., memory usage with large files), or side effects (e.g., whether the original file is modified). For a tool with no annotations, this is a significant gap in transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured and concise, with zero waste. It starts with a clear purpose statement, followed by bullet-like sections for 'Args' and 'Returns' that efficiently convey key information. Every sentence earns its place, and the formatting enhances readability without unnecessary elaboration.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (3 parameters, no annotations, no output schema), the description is partially complete. It covers the purpose and parameters well but lacks usage guidelines, behavioral details, and output specifics (e.g., format of the returned string). Without an output schema, the description should ideally explain the return value more thoroughly, but it does provide a basic return statement.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description adds meaningful semantics beyond the input schema, which has 0% description coverage. It explains each parameter: 'file_path' as the path to the Excel file, 'query' as a pandas query string with an example, and 'sheet_name' as the name of the sheet to filter. This compensates well for the schema's lack of descriptions, making the parameters clear and actionable for an agent.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Filter Excel data using a pandas query string.' It specifies the verb ('filter'), resource ('Excel data'), and method ('pandas query string'), which is specific and actionable. However, it doesn't explicitly differentiate from sibling tools like 'read_excel' or 'analyze_excel', which might also involve data extraction or processing.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It mentions the tool's function but doesn't specify scenarios, prerequisites, or exclusions, such as when to choose 'filter_excel' over 'read_excel' for data retrieval or 'analyze_excel' for analysis. This lack of context makes it harder for an agent to select the right tool among siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden for behavioral disclosure. It mentions the tool creates a pivot table and returns it as a string, but lacks details on permissions needed, file format requirements, error handling, or performance characteristics. The description is minimal beyond basic functionality.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured and appropriately sized. It begins with a clear purpose statement, followed by a parameter list with brief explanations, and ends with return information. Every sentence serves a clear purpose with zero wasted content.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given 6 parameters with no schema descriptions and no output schema, the description provides parameter semantics but lacks behavioral context. For a data transformation tool with multiple siblings, it should ideally explain more about when to use it, what the output format entails, or any limitations. It's minimally adequate but has clear gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description provides clear semantic explanations for all 6 parameters beyond their titles in the schema (which has 0% description coverage). Each parameter's role in pivot table creation is explained, such as 'index: Column to use as the pivot table index' and 'aggfunc: Aggregation function ('mean', 'sum', 'count', etc.)'. This adds significant value over the bare schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool creates a pivot table from Excel data, specifying the verb 'create' and resource 'pivot table from Excel data'. It distinguishes from siblings like 'analyze_excel' or 'data_summary' by focusing specifically on pivot table creation, though it doesn't explicitly contrast with them.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives like 'analyze_excel' or 'data_summary'. The description only states what the tool does without indicating appropriate contexts, prerequisites, or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It states the tool writes data but doesn't mention file overwriting behavior, permissions needed, error handling, or performance characteristics. The return value is vaguely described as 'Confirmation message' without details.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured with a clear purpose statement followed by organized parameter and return sections. Every sentence earns its place, with no redundant or verbose language. The information is front-loaded and easy to parse.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a write operation with 4 parameters, no annotations, and no output schema, the description provides adequate basic information but lacks depth. It covers what the tool does and parameter meanings but misses behavioral details like file overwriting, error cases, or format specifics that would help an agent use it correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description adds significant value beyond the input schema, which has 0% description coverage. It explains all 4 parameters with brief but meaningful context: 'file_path' as the save location, 'data' as CSV/JSON content, 'sheet_name' for Excel organization, and 'format' specifying input type. This compensates well for the schema's lack of descriptions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'Write' and resource 'data to an Excel file', making the purpose immediately understandable. However, it doesn't explicitly differentiate this tool from sibling tools like 'update_excel' or 'export_chart', which might have overlapping functionality.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives like 'update_excel' or 'export_chart'. It doesn't mention prerequisites, use cases, or exclusions, leaving the agent to guess based on tool names alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden but only states the basic action and return format. It misses critical behavioral details like error handling (e.g., invalid file paths), performance implications (e.g., large file handling), or side effects (e.g., file locking).
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with the core purpose, followed by a well-structured parameter list and return statement. Every sentence earns its place with no redundant information, making it efficient and easy to parse.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no annotations and no output schema, the description adequately covers parameters but lacks details on behavioral traits and output specifics. It's minimally viable for a read operation but could benefit from more context on limitations or usage scenarios.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description fully compensates by clearly explaining all four parameters, including their purposes and constraints (e.g., sheet_name only for .xlsx/.xls, header as 0-indexed). This adds essential meaning beyond the bare schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('Read an Excel file') and the resource ('Excel file'), distinguishing it from siblings like write_excel or update_excel by focusing on data extraction rather than modification or analysis.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives like analyze_excel or filter_excel. The description lacks context about use cases, prerequisites, or comparisons with sibling tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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