Excel MCP Server

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read_file_chunked

Process large Excel or CSV files in sections to manage data within token constraints, enabling analysis of extensive datasets without exceeding processing limits.

Instructions

Read large files in manageable chunks to avoid token limits

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "filePath": "<filePath>"
    },
    "name": "read_file_chunked"
  }
}

Input Schema

Name	Required	Description
`filePath`	Yes	Path to the CSV or Excel file
`sheet`	No	Sheet name for Excel files (optional)
`chunkIndex`	No	Chunk index to read (0-based, defaults to 0)
`chunkSize`	No	Number of rows per chunk (optional, auto-calculated if not provided)

Schema Changelog

Changes observed during successful MCP inspections.

First observedv1.0.0Jan 2, 2026

TDQS

B3.2/5.0

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. It mentions chunking to avoid token limits, which is useful behavioral context, but lacks details on file formats (only implied via parameters), error handling, performance traits, or output structure. For a tool with 4 parameters and no annotations, this is insufficient.

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 a single, efficient sentence that front-loads the core purpose. Every word earns its place, with no redundancy or unnecessary elaboration, making it highly concise and well-structured.

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 4 parameters, no annotations, and no output schema, the description is incomplete. It doesn't cover behavioral aspects like file format support (beyond CSV/Excel implied in schema), chunking mechanics, or return values. For a tool with moderate complexity, 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?

Schema description coverage is 100%, so the schema fully documents all parameters. The description adds no parameter-specific information beyond what's in the schema (e.g., it doesn't explain chunkIndex or chunkSize behavior further). Baseline 3 is appropriate as the schema does the heavy lifting.

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: 'Read large files in manageable chunks to avoid token limits.' It specifies the verb ('read'), resource ('large files'), and key constraint ('chunked'). However, it doesn't explicitly differentiate from sibling 'read_file' (which likely reads entire files), though the chunking aspect implies a distinction.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage context ('large files... to avoid token limits'), suggesting this tool is for handling files too large for standard processing. It doesn't explicitly state when to use it versus alternatives like 'read_file' or provide exclusions (e.g., for small files). The guidance is present but not comprehensive.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.