validate_uuidValidate and parse a UUID (keyless, offline): checks the format and returns the version (1-8), variant, whether it is nil/max, and — for time-based versions (v1, v7) — the embedded timestamp as an ISO date.
| Name | Required | Description | Default |
|---|---|---|---|
| uuid | Yes | A UUID, e.g. "550e8400-e29b-41d4-a716-446655440000". |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / examplesAdded value: +[
+ {
+ "uuid": "550e8400-e29b-41d4-a716-446655440000"
+ },
+ {
+ "uuid": "6ba7b810-9dad-11d1-80b4-00c04fd430c8"
+ }
+]Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, etc., and the description adds that it is keyless, offline, and returns specific fields like version and timestamp only for v1/v7. No contradictions.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is one sentence, well-structured, front-loaded, and contains no unnecessary words. Every detail earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Despite no output schema, the description thoroughly explains return values (version, variant, nil/max, timestamp for v1/v7) and usage context (keyless, offline), fully covering the tool's behavior.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 100% schema coverage for a single string parameter, the description does not add new parameter-level detail beyond what the schema provides, resulting in a baseline score.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description specifies a clear verb ('Validate and parse') and resource ('UUID'), and distinguishes from sibling tools like 'generate_uuid' and 'validate_claim' by detailing the specific parsing behavior.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description hints at usage context with 'keyless, offline,' but does not explicitly state when not to use or compare to alternatives. However, the uniqueness of UUID validation relative to siblings makes it clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
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.
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.
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.
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.