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kshitiz305

analytics-mcp-server

by kshitiz305

Import CSV Into Table

analytics_import_csv
Destructive

Import a CSV file into a SQLite table with automatic column type inference. Choose to fail, replace, or append existing data.

Instructions

Load a CSV file into a SQLite table (validated via pandas).

This is the only WRITE tool. Column types are inferred by pandas. The destination table name must be a valid SQL identifier.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
tableYesDestination table name (letters, digits, underscores).
csv_pathYesPath to a readable .csv file on disk.
if_existsNo``fail`` (default — error if the table exists), ``replace`` (drop and recreate) or ``append`` (add rows).fail

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Adds value beyond annotations by specifying pandas column type inference and requiring valid SQL identifiers. Annotations already mark destructiveHint true, but the description enriches understanding without contradiction.

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?

Three sentences, front-loaded with core action, each sentence adding critical information without waste. Highly efficient.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Covers key aspects: write action, validation, rule for table name, and if_exists behavior (via schema). Output schema exists, so return values are covered. Slightly more detail on validation process would be ideal, but overall sufficient.

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 coverage is 100%, so baseline is 3. The description does not add significant new meaning for parameters beyond what's in the schema (e.g., valid SQL identifier is already in schema description).

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?

Clearly states 'Load a CSV file into a SQLite table', specifying verb, resource, and validation via pandas. Distinguishes itself from siblings as 'the only WRITE tool', making its purpose unambiguous.

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

Usage Guidelines4/5

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

Explicitly notes it's the only write tool, providing clear context for when to use it. Does not directly list when not to use alternatives, but this is strongly implied by the write-vs-read distinction.

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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