Skip to main content
Glama

Google Trends API

trends___get_context

Read-onlyIdempotent

What people are posting, reading and betting about a keyword right now. Returns recent posts, videos, news stories, discussions and prediction markets per source, with engagement (views, likes, score) and top comments. Use it to explain why something is trending or what the conversation says; get_time_series and get_growth measure how much. Costs 1 request per source. Returns a plan-limit message when the request is rate limited or the monthly quota is used up.

Input Schema

NameRequiredDescriptionDefault
daysNoLook back this many days, 1 to 30. Default 7.
limitNoItems per source, 1 to 20. Default 10.
sourceYesOne source, or up to 5 comma-separated. Valid: 'News', 'YouTube', 'TikTok', 'Instagram', 'X', 'Reddit', 'Bluesky', 'Hacker News', 'Polymarket', 'GitHub'.
keywordYesAny name or phrase, e.g. 'nintendo switch 2'.
commentsNoTop comments for the first N items of each source, 0 to 5. Default 2. Only for 'YouTube', 'TikTok', 'Instagram', 'X', 'Reddit', 'Bluesky', and 'Hacker News'.

Schema Changelog

Changes observed during successful MCP inspections.

No schema history has been recorded yet.

TDQS

A4.7/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint, openWorldHint and destructiveHint=false, so safety is covered. The description adds genuinely non-structured behavior: cost of 1 request per source, and that a plan-limit message is returned when rate limited or the monthly quota is exhausted — useful operational context an agent cannot get elsewhere.

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?

Front-loaded with what it returns, then the distinguishing use case, then cost and error behavior. Every sentence carries distinct information; no restatement of the tool name or filler.

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?

No output schema exists, so the description must describe return values — and it does, enumerating item types, engagement metrics and top comments. Combined with cost and quota-message disclosure, an agent has everything needed to call this correctly and interpret limits.

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 description coverage is 100%, so the baseline is 3. The description still adds meaning by tying results to sources ('per source') and clarifying that comments and engagement are per-item, which explains how the source, limit and comments parameters interact.

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?

States a specific verb-plus-resource: retrieves what people are posting, reading and betting about a keyword right now, returning posts, videos, news, discussions and prediction markets. It also explicitly distinguishes itself from the measurement siblings ('get_time_series and get_growth measure how much'), so an agent can pick correctly without opening another schema.

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?

Gives an explicit use case ('use it to explain why something is trending or what the conversation says') and names the alternative tools with the condition that selects them (measurement vs. explanation). Nothing about when to reach for it is left to inference.

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

Try in Browser

Glama MCP Gateway

Add one secure layer between your agents and this server.