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Glama

Oxylabs

by io.github.pipeworx-io

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Idempotent

Save data the agent will need to reuse later — across this conversation or across sessions. Use when you discover something worth carrying forward (a resolved ticker, a target address, a user preference, a research subject) so you don't have to look it up again. Stored as a key-value pair scoped by your identifier. Authenticated users get persistent memory; anonymous sessions retain memory for 24 hours. Pair with recall to retrieve later, forget to delete.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
keyYesMemory key (e.g., "subject_property", "target_ticker", "user_preference")
valueYesValue to store (any text — findings, addresses, preferences, notes)

Schema Changelog

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

  1. First observed

TDQS

A4.6/5.0
Behavior4/5

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

Adds behavioral context beyond annotations: scoped by identifier, persistent for authenticated users, 24-hour retention for anonymous. However, lacks return value details and error handling information.

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?

Well-structured and concise: first sentence states purpose, second gives usage guidance, third explains scope and persistence, fourth mentions companion tools. No unnecessary words.

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: purpose, usage, scoping, persistence, and companion tools. Minor gap: behavior on duplicate keys not addressed, but idempotentHint implies safe overwrite.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, but description adds examples of valid keys and clarifies value can be any text, supplementing the schema's basic descriptions.

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 clearly states the tool saves data for reuse across conversations/sessions. It specifies it stores key-value pairs scoped by identifier and distinguishes from sibling tools like recall and forget.

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

Usage Guidelines5/5

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

Explicitly states when to use: when discovering something worth carrying forward (e.g., ticker, address). Provides alternatives: pair with recall to retrieve, forget to delete.

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

All Oxylabs toolsInspect
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TDQS

A4/5.0
Disambiguation3/5

Several tools overlap in purpose, particularly the ask_pipeworx family (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded) where beta currently matches stable exactly, and the polymarket_* cluster with similar names. However, detailed descriptions clarify each tool's specific role, so an agent can usually select correctly with careful reading.

Naming Consistency3/5

All names use snake_case and are generally descriptive, but they mix conventions: many are verb_noun (list_subscriptions, validate_claim), while others are noun phrases (entity_profile, recent_changes). Prefixes like oxylabs_ and polymarket_ are consistent, but the lack of a uniform verb-first pattern reduces predictability.

Tool Count2/5

With 34 tools, the server exceeds the 25-tool threshold for 'too many'. While the broad scope (data lookup, scraping, prediction markets, memory, subscriptions) warrants a larger surface, the sheer number makes it difficult for agents to quickly identify the right tool without extensive scanning.

Completeness4/5

The tool set covers the core domain thoroughly: question answering, deep research, entity resolution, comparison, validation, web scraping, prediction market analysis, memory management, and subscriptions. Minor gaps like limited e-commerce scraping beyond Amazon and no direct data-writing tools exist, but they do not critically hamper workflows.

Resources