Skip to main content
Glama

search

Search stored SQL conversions, code patterns, and conversion context to retrieve historical queries, templates, and metadata for reuse in PySpark data engineering.

Instructions

Deprecated. Prefer convert. Still registered this minor version.

Search stored conversions, code patterns, and context data.

Modes:

conversions Search previously converted SQL queries and history. Parameters: query, limit If query is empty, returns recent conversion history.

patterns Search stored code patterns by description or template. Parameters: query, limit, min_usage_count If query is empty, returns all stored patterns with min_usage_count.

context Retrieve stored conversion context. Parameters: conversion_id or key

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
keyNo
modeYes
limitNo
queryNo
conversion_idNo
min_usage_countNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observedv0.0.4

TDQS

A4.3/5.0
Behavior4/5

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

With no annotations, the description carries the behavioral burden. It adds useful behavior beyond the schema by explaining empty-query defaults for conversions and patterns and by using verbs like 'retrieve' and 'search' to imply read-only behavior. It does not discuss side effects, auth, or rate limits, but those are less critical for a search-style tool.

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 with a deprecation warning up front, a one-line summary, and clearly labeled mode sections. Each sentence provides useful operational guidance with no filler.

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?

The mode breakdown and parameter semantics give enough detail to call the tool correctly, and the output schema covers return-value shape. The main gap is not clarifying how the context mode relates to the sibling context tool or exactly how key differs from conversion_id.

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 must compensate. It does so well by mapping each mode to its applicable parameters and explaining empty-query behavior for conversions and patterns, plus the context mode's key/conversion_id inputs. This adds meaning far beyond the raw 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 searches stored conversions, code patterns, and context data, and it breaks behavior into three named modes. It names convert as the preferred alternative, but it does not fully differentiate from the sibling context tool, so it stops short of a 5.

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?

The deprecation notice explicitly tells the agent to prefer convert, which is strong when-not-to-use guidance. It also explains mode-specific parameter usage, but it does not spell out when search should still be used over convert or other siblings.

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

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/AnnasMazhar/pyspark_mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server