Feedback Synthesis MCP
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| EVM_PRIVATE_KEY | Yes | Your EVM wallet private key for x402 micropayments on Base mainnet (USDC). Required for all tool calls. |
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": true
} |
| logging | {} |
| prompts | {
"listChanged": false
} |
| resources | {
"subscribe": false,
"listChanged": false
} |
| experimental | {} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| synthesize_feedbackA | Synthesize customer feedback from multiple sources into ranked pain clusters. Collects feedback from GitHub Issues, Hacker News, and/or App Store Reviews, then runs a multi-pass LLM pipeline to extract and rank pain clusters with evidence. Returns up to 10 ranked pain clusters with impact scores, evidence links, and suggested actions. Takes 10-60 seconds depending on volume. Args: sources: List of source specs. Each has 'type' (github_issues/hackernews/appstore) and 'target' (owner/repo, search query, or app bundle ID). Example: [{"type": "github_issues", "target": "owner/repo"}, {"type": "hackernews", "target": "MyProduct"}] max_items_per_source: Max feedback items to collect per source (default 200) since: ISO 8601 datetime to filter items (e.g. '2026-01-01T00:00:00Z') focus: Analysis focus — 'pain_points' (default) or 'feature_requests' |
| get_pain_pointsA | Quickly extract top pain points from a single feedback source. Faster and cheaper than synthesize_feedback — single LLM pass, one source. Returns the top N pain points with frequency counts and sample evidence URLs. Args: source: Source spec with 'type' (github_issues/hackernews/appstore) and 'target'. Example: {"type": "github_issues", "target": "owner/repo", "labels": ["bug"]} max_items: Max items to collect (default 100) top_n: Number of top pain points to return (default 5) |
| search_feedbackA | Search raw feedback items across cached sources using full-text search. Useful for drilling into a specific topic after synthesis. Searches previously collected feedback without triggering new LLM processing. Fast and cheap. Args: query: Search terms (e.g. 'authentication mobile' or 'pricing too expensive') sources: Filter by source types (e.g. ['github_issues', 'appstore']) target: Filter by target repo/app (e.g. 'owner/repo') since: ISO 8601 datetime filter (e.g. '2026-01-01T00:00:00Z') limit: Max results to return (default 20) |
| get_sentiment_trendsA | Get time-series sentiment analysis across feedback sources. Shows how sentiment shifts over time — useful for tracking the impact of releases, bug fixes, or feature launches. Returns weekly/monthly sentiment scores with notable shifts and likely causes. Args: sources: List of source specs (same format as synthesize_feedback) since: Start date for trend analysis (ISO 8601, default 6 months ago) granularity: Time bucket size — 'weekly' (default) or 'monthly' |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
No prompts | |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
No resources | |
TDQS
Scored across 4 tools
Each tool has a clearly distinct purpose with minimal overlap: get_pain_points extracts pain points from a single source, get_sentiment_trends analyzes sentiment over time, search_feedback performs full-text searches on cached data, and synthesize_feedback synthesizes multiple sources into pain clusters. The descriptions explicitly differentiate them, such as noting get_pain_points is faster than synthesize_feedback for single sources, eliminating confusion.
All tool names follow a consistent verb_noun pattern with snake_case: get_pain_points, get_sentiment_trends, search_feedback, and synthesize_feedback. This uniformity makes the set predictable and easy to understand, enhancing usability for agents without any deviations in style.
With 4 tools, this server is well-scoped for feedback synthesis, covering key operations like extraction, analysis, search, and synthesis. Each tool serves a unique function, and the count is neither too sparse nor bloated, fitting typical MCP server ranges for a focused domain.
The toolset covers core feedback analysis workflows effectively, including single-source extraction, multi-source synthesis, sentiment tracking, and search. A minor gap exists in lacking explicit update or deletion tools for managing cached feedback, but agents can work around this, and the surface supports comprehensive analysis without dead ends.