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Glama

LiveDataLink

by io.github.blackboxfoundry

weather_current

Read-onlyIdempotent

Get current weather conditions for any location worldwide. Returns temperature, feels-like, humidity, wind speed and direction, cloud cover, pressure, precipitation, UV index, and visibility. Use this for 'what's the weather?', 'is it raining in Houston?', 'how hot is it outside?', 'what's the temperature in New York?', 'do I need a jacket?', or any current weather question. Works for any city, zip code, or place name globally.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
locationYesCity, zip code, or place name (e.g., 'Houston, TX', '77001', 'Paris')

Schema Changelog

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

  1. First observed

TDQS

A4.2/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false. The description goes beyond these by specifying the exact set of returned conditions, such as temperature, humidity, wind, cloud cover, pressure, precipitation, UV index, and visibility, and by emphasizing global location coverage.

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 front-loaded with the core purpose and return fields, then gives practical example queries. It is slightly longer than necessary due to some repetition between 'any location worldwide' and 'Works for any city, zip code, or place name globally,' but the example queries earn their place for routing accuracy.

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 simple one-parameter tool with no output schema, the description is complete: it states what the tool does, what data it returns, what kinds of queries it supports, and what location formats are accepted. No critical information is missing for an agent to invoke it correctly.

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

Parameters3/5

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

The input schema already documents the location parameter with examples like 'Houston, TX' and '77001', and schema coverage is 100%. The description restates that any city, zip code, or place name works, which reinforces the parameter's semantics but does not add meaningful new detail beyond the schema.

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 a specific verb and resource: 'Get current weather conditions for any location worldwide.' It enumerates the returned fields and uses 'current' to distinguish itself from the sibling weather_forecast tool. This makes its purpose unmistakable.

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 description provides explicit use examples such as 'is it raining in Houston?' and 'what's the temperature in New York?', plus a catch-all 'or any current weather question.' It gives clear context for when to use the tool, though it does not explicitly state when not to use it or name weather_forecast as the alternative.

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

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TDQS

B3.3/5.0
Disambiguation2/5

Several tool clusters overlap heavily—company due-diligence and risk tools (counterparty_risk_score, company_trust_check, entity_dossier, issuer_diligence_dossier, resolve_entity, entity_resolve), carrier vetting tools, sanctions screening tools, and recall tools all have subtle boundary distinctions. While descriptions are detailed, an agent navigating 294 tools will frequently struggle to pick the right one.

Naming Consistency3/5

Most tools follow a readable snake_case domain-prefix pattern (fdic_, edgar_, sanctions_, congress_), which helps. However, verb placement is inconsistent—search_available_datasets vs cdc_dataset_query, resolve_entity vs entity_resolve—and synonyms like search, lookup, get, detail, fetch, and status are used interchangeably.

Tool Count1/5

294 tools is an extreme number for a single MCP server, far beyond what an agent can reliably hold in context or select from accurately. The presence of tool-group discovery helpers mitigates but does not solve the fundamental scale problem.

Completeness4/5

The data breadth is genuinely extensive, covering finance, health, legal, real estate, transportation, energy, cyber, education, and many other domains, often with generic query fallbacks. Still, some capabilities are shallow or incomplete—package tracking stops at a link, property tools are demo-only in places, and caselaw coverage is limited—so it is not a fully complete surface.

Resources