trendzeist-mcp
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| TRENDZEIST_HL | No | UI language for Google Trends. | en-US |
| TRENDZEIST_TZ | No | Timezone offset in minutes. | 360 |
| TRENDZEIST_BACKOFF | No | Exponential backoff factor. | 1.5 |
| TRENDZEIST_PROXIES | No | Comma-separated proxy URLs for rotation. | |
| TRENDZEIST_RETRIES | No | Retry attempts on transient errors. | 3 |
| TRENDZEIST_CACHE_DIR | No | Persistent cache location; use 'off' to disable. | OS user cache dir |
| TRENDZEIST_LOG_LEVEL | No | Python logging level (stderr). | WARNING |
| TRENDZEIST_MIN_INTERVAL | No | Minimum seconds between Google requests. | 2.0 |
Instructions
Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.
This server publishes no instructions, or was last inspected before Glama recorded them.
Capabilities
Features and capabilities supported by this server
Protocol revision2025-11-25
| Capability | Details |
|---|---|
| tools | {
"listChanged": false
} |
| prompts | {
"listChanged": false
} |
| resources | {
"subscribe": false,
"listChanged": false
} |
| experimental | {} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| interest_over_timeB | Search-interest time series (0-100) for 1-5 keywords, with per-keyword summary (mean, latest, peak, direction: rising/stable/falling). Long series are downsampled to ~60 points. gprop: '' (web), 'news', 'youtube', 'images', 'froogle'. |
| compare_keywordsA | Compare 2-5 keywords head-to-head: relative share, leader and trend direction for each. Use to pick the strongest angle among alternatives. |
| related_queriesB | Top and rising search queries related to a keyword. 'rising' includes % growth and is_breakout (>5000%); best single source of fresh blog angles. |
| related_topicsA | Top and rising Knowledge-Graph topics related to a keyword. Google often returns nothing here; if available=false fall back to related_queries. |
| interest_by_regionC | Where a keyword is searched most. resolution: COUNTRY (worldwide), REGION (states/provinces within geo), CITY, DMA (US metro areas). |
| suggest_keywordsA | Google's entity suggestions for a keyword (title, type, mid). Use to disambiguate ambiguous terms or find the canonical topic id. |
| trending_nowA | Real-time trending searches (last ~24h) for a country or US state, with approximate traffic and linked news headlines. Good for newsjacking. |
| list_categoriesA | Browse/search Google Trends category ids (e.g. 'coffee' -> Food & Drink >
Coffee & Tea). Pass the id as |
| discover_topicsA | One-shot topic discovery for blog ideation. For 1-5 seed keywords, pulls
trend direction plus rising/top related queries, de-duplicates and ranks
candidates as breakout > rising > evergreen. Partial failures are reported
per seed in |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
| blog_ideas_from_trends | Guided workflow: turn Google Trends data into a prioritised list of blog post ideas. |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
No resources | |
TDQS
Scored across 9 tools
Each tool serves a distinct purpose in the Google Trends domain: time series, comparison, related queries/topics, regional breakdown, suggestions, real-time trends, category listing, and topic discovery. There is no overlap that would confuse an agent.
All tool names follow a consistent verb_noun pattern (e.g., interest_over_time, compare_keywords, related_queries), using snake_case throughout. The verbs clearly indicate the action, and nouns are domain-relevant.
With 9 tools, the server is well-scoped for a Google Trends analysis domain. Each tool covers a distinct aspect of trend research without redundancy, making the count ideal for the purpose.
The toolset covers the core Google Trends workflows: time series, comparison, related terms, regional data, real-time trends, and suggestions. Missing would be something like historical category comparisons or multi-region breakdowns, but these are minor gaps that agents can work around.