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Data File Analysis MCP Server

by llm-guy

summarize_parquet_file

Get the row and column counts of any Parquet file by providing its filename to quickly understand the dataset's dimensions and scope before further analysis.

Instructions

Summarize a Parquet file by reporting its number of rows and columns. Args: filename: Name of the Parquet file in the /data directory (e.g., 'sample.parquet') Returns: A string describing the file's dimensions.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
filenameYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
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. The description mentions the return value (a string describing dimensions) but does not disclose the actual output format, whether it reads the entire file into memory, error behavior for missing files, or whether it handles nested/partitioned Parquet files. For a read-only tool, the absence of annotations and lack of behavioral detail is a gap.

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 compact and uses the Args/Returns structure for clarity. It's front-loaded with the core purpose in the first sentence. Every sentence earns its place, though the Args/Returns formatting is slightly verbose for a single-parameter tool.

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 simple 1-parameter, read-only tool with an output schema present, the description is reasonably complete. It states inputs and return type. However, the return format is vague ('a string describing the file's dimensions') and the /data directory constraint could use more precision (absolute vs relative paths, access permissions). Given the low complexity, this is adequate but not thorough.

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?

With a single parameter and 0% schema description coverage, the description compensates somewhat by specifying the filename format with an example ('sample.parquet') and the /data directory location. However, it doesn't clarify whether the path extension matters, whether it accepts paths with subdirectories under /data, or what happens with invalid filenames. The description adds moderate value beyond 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 verb+resource: 'Summarize a Parquet file by reporting its number of rows and columns.' This is specific about what it does and distinguishes it from the sibling summarize_csv_file by explicitly naming the Parquet format. However, it doesn't explicitly contrast itself with the sibling tool, so it doesn't fully maximize sibling differentiation.

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 by stating the tool works on files in the /data directory, but gives no explicit guidance on when to choose this over summarize_csv_file or any exclusions. The file-format naming (Parquet vs CSV) makes the usage distinction implicit but not explicitly stated.

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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