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Glama

Search News

search_news
Read-onlyIdempotent

Search global news articles by keyword (e.g., "climate change", "AI regulation"). Returns title, description, content snippet, source, and publication date. Supports language and country filters.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
maxNoMax articles to return (1-100, default 10)
langNoLanguage code (e.g., "en", "fr", "de"). Default: "en"
queryYesSearch keywords (e.g., "electric vehicles")
_apiKeyYesGNews API key
countryNoCountry code (e.g., "us", "gb", "ca"). Omit for global

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
articlesYesList of news articles matching the search
returnedYesNumber of articles returned in this response
total_articlesYesTotal number of articles matching the search query

Schema Changelog

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

  1. First observed

TDQS

A4/5.0
Behavior3/5

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint, so the agent knows this is a safe, idempotent read operation. The description adds context on returned fields and filters, which is helpful but not extensive. It does not disclose pagination behavior or rate limits, which are partly covered by the schema's 'max' parameter.

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 extremely concise: two sentences that cover purpose, return fields, and filters. No wasted words. Every sentence adds value.

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?

Given the rich annotations, complete schema, and presence of an output schema, the description is adequately complete. It covers the core functionality and return format. A slight gap is that it does not explicitly state it requires an API key, but that is evident from the required parameters in the schema.

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?

Schema description coverage is 100%, so the baseline is 3. The description adds minimal additional meaning beyond the schema: it mentions 'content snippet' (though not explicitly in schema descriptions) and confirms supports for language/country filters. The examples in the schema already provide usage context.

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's purpose: searching global news articles by keyword. It specifies what is returned (title, description, content snippet, source, publication date) and the available filters (language, country). This distinguishes it from sibling tools like 'top_headlines' or 'deep_research', which serve different purposes.

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 clear context for when to use this tool (searching news by keyword) but does not explicitly state when not to use it or mention alternatives. It implies its usage through the verb 'search' and the mention of filters, but lacks explicit exclusions or comparison to siblings.

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

A3.9/5.0
Disambiguation4/5

Most tools have distinct, well-described purposes, but there is some overlap, especially among prediction market tools (bet_research, polymarket_arbitrage, etc.) and between ask_pipeworx and ask_pipeworx_grounded. Agents might occasionally select the wrong tool without careful reading.

Naming Consistency3/5

Tool names follow a mix of snake_case and camelCase (e.g., ai_visibility_check vs discover_tools). Some names are descriptive but inconsistent in style (subscribe, unsubscribe, list_subscriptions). Pattern is not uniform.

Tool Count3/5

With 32 tools, the server covers many domains (news, financials, prediction markets, entity resolution, memory). While each tool has a justification, the count feels heavy for a single server, and some tools could be consolidated.

Completeness3/5

The tool set spans a wide range of data sources and operations, but there are notable gaps. For news, only search and top headlines exist without advanced filtering. Prediction markets lack order placement tools. The broad scope means depth is sacrificed in some areas.