Skip to main content
Glama
Luissalet

Laplace's Hoard MCP Server

by Luissalet
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

data_register

Idempotent

Turn a local file or folder into a SQL-queryable dataset. Registers CSV, Excel, Parquet, JSON, SQLite and returns schema with profile.

Instructions

Register a local file or folder so it can be queried with SQL: CSV/TSV, Parquet, JSON/NDJSON, Excel, SQLite.

path is an absolute path on this computer (e.g. C:\Users\me\ventas.xlsx). The dataset name defaults to the file name made SQL-safe ("Ventas 2024" becomes Ventas_2024) - use the returned name. Excel registers one dataset per sheet ("__"), SQLite one per table, a folder all files matching options.glob (default "*.csv"). CSV options: delimiter, header, encoding (utf-8/utf-16/latin-1; auto-detected when omitted, so Windows-1252 exports work without setting anything), date_format (e.g. "%d/%m/%Y"; two-digit-year day-first dates like "13/02/25" are auto-detected already), decimal_separator and thousands_separator. Spanish-style numbers ("-1.150,00", "51,05") are detected automatically and become exact numbers - the result lists them under numbers_converted; pass decimal_separator="." to keep such a column as text, or both separators to force another convention. Excel also takes skip_rows (a title row above the real header is detected and skipped automatically; set this to override). Returns the schema and profile (like data_describe); a single-row result with nested list columns also gets a hint suggesting UNNEST. Re-registering the same path refreshes it. Not read-only: it copies the data into the local catalogue (the original file is never modified).

Keywords: load this file, open this spreadsheet, register dataset, import CSV, read Excel, cargar este archivo, abrir esta hoja de cálculo, registrar datos, importar CSV, leer Excel.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameNo
pathYes
optionsNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.7/5.0
Behavior5/5

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

Even though annotations provide readOnlyHint=false, idempotentHint=true, and destructiveHint=false, the description adds valuable side-effect detail: data is copied into the local catalogue, the original file is never modified, and re-registering the same path refreshes it. This goes well beyond the boolean hints.

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 long but logically structured: purpose first, then path/naming, format-specific behavior, options, return value, and side effects. Some edge-case detail and the trailing keyword block could be trimmed, but the density is justified by the tool's complexity and each section builds on the previous one.

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

Completeness5/5

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

For a complex registration tool with no output schema, the description thoroughly covers what the tool does, supported formats, options, return behavior, and side effects. It even explains the schema/profile return and the UNNEST hint edge case. The only minor omission is explicit confirmation that the `name` parameter overrides the default, which is inferable from context.

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?

With 0% schema description coverage, the description carries the full parameter burden. It explains path must be absolute, naming defaults to a SQL-safe file name, Excel registers one dataset per sheet, SQLite one per table, folder uses glob defaulting to *.csv, and details CSV/Excel options like encoding, date_format, decimal separators, and skip_rows. This gives the name and options parameters concrete meaning.

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 opens with 'Register a local file or folder so it can be queried with SQL', giving a specific verb and resource plus the tool's core purpose. It enumerates supported formats and clearly differentiates it from siblings like data_query, data_describe, and data_list as the ingestion step.

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?

It specifies when to use the tool: to load local files or folders for SQL querying, with concrete scenarios like Excel sheets, SQLite tables, and folder globs. It lacks an explicit 'use data_query instead when data is already registered' statement, but the ingestion role and refresh behavior make the usage context clear.

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

Deploy Server

Other Tools