review-miner
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
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 |
|---|---|
| review_top_appsA | List the current top charts (App Store by category, or Steam top sellers / most played / new releases). Use this to pick competitors before fetching their reviews. |
| review_search_appsA | Find an app/game and its ID by name. Returns IDs in 'store:id' form for the other tools. |
| review_fetchA | Fetch recent reviews for one app/game and return stats (rating mix, rating per version, top complaint terms) plus the most helpful review texts. Best for 'why do users hate X?'. |
| review_compare_appsA | Compare recent reviews of 2-8 competitors side by side: rating, negative share and top complaint terms per app, plus a few negative samples each. Use it to find gaps that every competitor fails at (= product opportunity). |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
| find_opportunities | Find product gaps in a category by mining competitors' negative reviews. |
| competitor_teardown | Deep dive into what users love and hate about one app or game. |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
No resources | |
TDQS
Scored across 4 tools
Each tool targets a distinct step or scope: chart discovery, app lookup, single-app review fetching, and multi-app comparison. There is no meaningful overlap in purpose, and the descriptions clearly distinguish when to use each.
All tools use the review_ prefix and mostly follow an action-oriented pattern. The only minor deviation is review_fetch, which omits an explicit object noun like the others, but the convention remains readable and predictable.
Four tools are well-scoped for a review-mining workflow: discover apps, resolve IDs, fetch reviews, and compare competitors. Each tool earns its place without redundancy.
The core lifecycle is covered: finding apps, retrieving reviews with stats, and comparing multiple apps. Minor gaps exist around filtering by time range or exporting raw reviews, but the surface supports the primary use cases.