Skip to main content
Glama

Uuid

by io.github.pipeworx-io

Remember

remember
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

A4.9/5.0
Behavior5/5

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

Annotations indicate idempotentHint=true, readOnlyHint=false, destructiveHint=false. The description adds behavioral context: memory is scoped by identifier, persistent for authenticated users, and 24-hour retention for anonymous sessions. No contradiction with annotations.

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 concise and well-structured. It opens with the primary purpose, then provides usage examples, and ends with pairing instructions. Every sentence adds value without redundancy.

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?

The description is complete for the tool's simplicity. It explains the storage semantics, scope, and lifetime. Despite no output schema, the behavior is fully disclosed.

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% with good parameter descriptions. The description adds context by providing naming conventions (e.g., 'subject_property', 'target_ticker') and explaining the key-value nature, but could be slightly more detailed about formatting expectations.

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 purpose: 'Save data the agent will need to reuse later.' It specifies the verb (save/store) and resource (key-value pair), and distinguishes from sibling tools like recall and forget by mentioning them explicitly.

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?

The description provides explicit guidance on when to use: 'Use when you discover something worth carrying forward (a resolved ticker, a target address, a user preference, a research subject).' It also mentions alternatives for retrieval (recall) and deletion (forget).

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

All Uuid toolsInspect
Try in Browser
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

A3.8/5.0
Disambiguation3/5

Most tools have distinct purposes, but several overlap heavily: ask_pipeworx_beta explicitly matches ask_pipeworx exactly, ai_visibility_check is a single-entity version of scan_competitor_ai_presence, and multiple Polymarket tools (polymarket_edges, polymarket_arbitrage, polymarket_edge_tracker, polymarket_fill_risk) share fuzzy boundaries. The lengthy descriptions help, but an agent could easily select the wrong tool.

Naming Consistency4/5

The overwhelming majority follow a clear verb_noun or noun_phrase pattern (ask_pipeworx, resolve_entity, validate_claim, generate_uuid, discover_tools). The ask_pipeworx_beta/ask_pipeworx_grounded variants and brand-name tools like pipeworx_trending are minor deviations, but the overall convention is remarkably consistent across 33 tools.

Tool Count2/5

33 tools is heavy for a single MCP server, especially one named 'Uuid' where only 2 tools (generate_uuid, validate_uuid) relate to the apparent name. The remaining 31 tools constitute a sprawling data-research platform that would normally be its own server. The count stretches beyond what is typically coherent.

Completeness3/5

As a data-research platform, the surface is quite complete: lookup, grounded answers, deep research, entity resolution, comparisons, claim validation, subscriptions, memory, and feedback are all covered. However, for the nominal 'Uuid' domain, only generation and validation exist — missing anything like UUID namespace generation, timestamp extraction from v1/v7, or bulk operations — creating a glaring mismatch between server name and actual tool scope.

Resources