Feedback Synthesis MCP
PYPI · FEEDBACK-SYNTHESIS-MCP · 2 COMPONENTS · SCANNED SEP 20
Synthesize GitHub Issues, HN and App Store reviews into ranked pain clusters. Pay-per-call x402.
How this component scores in each security and reliability category. Every signal is checked automatically from public evidence about the published package, including repeated runs of it in an isolated sandbox, and we only credit what we can confirm. How we score → Why this is hard to score →
Supply Chain Security100
- No malware found by supply-chain analysis.Pass
- No known CVEs affecting this package version or its production dependencies.Pass
- Runs hatchling.build at install time, a recognised native-build step with no shell scripting around it. View diagnostics → Pass
- 2 of 41 dependencies flagged as unhealthy. View diagnostics → Partial
Provenance & Transparency45
- Source repository is publicly reachable at the declared URL. View diagnostics → Pass
- Provenance check failed: no build-provenance attestation is published. See how to fix → View diagnostics → Fail
- Clear OSI-approved license (MIT).Pass
- Actively maintained (last published 163 days ago).Pass
- Disclosure check failed: no security disclosure policy was found in the source repository. See how to fix → Fail
Schema Quality & AI Usability64
- AI-judged instruction clarity (good).Pass
- Context-footprint check failed: tool/resource definitions use about 731 tokens (~182/item across 4 items; 4 tools + 0 resources), over budget; trim descriptions and params. See how to fix → Fail
- Usage-examples check failed: none of the tools include examples. See how to fix → Fail
Stability & Change Management87
- Stability observed for 26 of 30 days with no destabilising changes; credit accrues until the full window elapses.Partial
Tool Coverage100
- 100% of tools have a non-trivial description (not blank, and not just the tool's name).Pass
- 100% of tool parameters carry a description.Pass
- Structured output schemas are declared (100% of tools); any adoption earns full credit.Pass
Tool Safety100
- No prompt-injection markers were found in the server instructions, tool names or descriptions we captured.Pass
- We read all 4 captured tool definition(s), and no name or description among them implies an irreversible operation.Pass
- An AI judge read all 5 captured unit(s) of tool text and found none that tries to manipulate the model reading it.Pass
Capabilities100
- Implements a current MCP spec version (2026-07-28).Pass
How do I install the Feedback Synthesis MCP server?
Feedback Synthesis MCP runs locally as a PyPI package, launched with uvx feedback-synthesis-mcp. Ready-made configuration for Claude, Cursor, VS Code, Codex and 5 more is on this page, copied from each client's own documentation.
pypi · feedback-synthesis-mcp
claude mcp add sapph1re-feedback-synthesis-mcp -- uvx feedback-synthesis-mcp
{
"mcpServers": {
"sapph1re-feedback-synthesis-mcp": {
"command": "uvx",
"args": [
"feedback-synthesis-mcp"
]
}
}
} {
"servers": {
"sapph1re-feedback-synthesis-mcp": {
"command": "uvx",
"args": [
"feedback-synthesis-mcp"
]
}
}
} codex mcp add sapph1re-feedback-synthesis-mcp -- uvx feedback-synthesis-mcp
{
"$schema": "https://opencode.ai/config.json",
"mcp": {
"sapph1re-feedback-synthesis-mcp": {
"type": "local",
"command": [
"uvx",
"feedback-synthesis-mcp"
],
"enabled": true
}
}
} openclaw mcp add sapph1re-feedback-synthesis-mcp --command uvx --arg feedback-synthesis-mcp
mcp_servers:
sapph1re-feedback-synthesis-mcp:
command: "uvx"
args: ["feedback-synthesis-mcp"] {
"McpServers": {
"sapph1re-feedback-synthesis-mcp": {
"Transport": "stdio",
"Command": "uvx",
"Arguments": [
"feedback-synthesis-mcp"
]
}
}
} assistant mcp add sapph1re-feedback-synthesis-mcp -t stdio -c uvx -a feedback-synthesis-mcp
{
"mcpServers": {
"sapph1re-feedback-synthesis-mcp": {
"command": "uvx",
"args": [
"feedback-synthesis-mcp"
]
}
}
} Every change we have recorded for this component, newest first. Security-relevant changes are always shown. ▲ marks a change for the better, ▼ a change for the worse; unmarked changes are neutral.
- 20 Sept 26 +1
No change was recorded against any check on this day. Stability & Change Management went from 83 to 87. That category is still filling its 30-day observation window: 25 days of observed history at the previous scan, 26 at this one. The score rises as the window fills, whether or not the server changes.
- 18 Sept 26 −3
- Stability: pass → 0.80 functional
- 17 Sept 26 +1
- Stability: 0.97 → pass security
- 15 Sept 26 +16
- Malware scan: unverified → pass ▲ security
- 14 Sept 26 −15
- Malware scan: pass → unverified ▼ security
- 13 Sept 26 +1
No change was recorded against any check on this day. Stability & Change Management went from 83 to 87. That category is still filling its 30-day observation window: 25 days of observed history at the previous scan, 26 at this one. The score rises as the window fills, whether or not the server changes.
- 11 Sept 26 −3
- Stability: pass → 0.80 functional
- 10 Sept 26 +1
- Stability: 0.97 → pass security
Diagnostic detail from the automated scan of this channel: what the scanner observed at each step, so you can see exactly where a check passed or failed. It is informational only and never changes the trust score.
Captured 20 Sept 2026 · Analysed pypi/feedback-synthesis-mcp@0.1.1
Provenance No attestation
The registry publishes no build provenance for this version, so there is nothing to verify.
| Result | No attestation |
|---|---|
| Ecosystem | pypi |
Background: How many MCP packages publish verified provenance →
Install scripts 1 script
| Hook | Tier | Command |
|---|---|---|
| build_backend | allowlisted | hatchling.build |
Background: Why install scripts are a supply-chain risk →
Dependencies 41 packages
| Packages resolved | 41 |
|---|---|
| Stale | 2 |
| Tree resolution | Complete |
Background: SBOMs and build attestations, explained →
The tools this component advertises to a client, with an estimated token cost for each. Expand a tool to see its parameters and schema. The per-tool counts are indicative and are not scored directly; the schema's total context footprint is one signal in Schema Quality & AI Usability. A tool's description is untrusted text the model reads on every call, which is what makes this list a security surface and not just an inventory: how tool poisoning works →
get_pain_points Get Pain Points ~138
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.
| Name | Type | Req | Description |
|---|---|---|---|
| max_items | integer | – | Max items to collect (default 100) |
| source | object | – | Source spec with 'type' (github_issues/hackernews/appstore) and 'target'. Example: {"type": "github_issues", "target": "owner/repo", "labels": ["bug"]} |
| top_n | integer | – | Number of top pain points to return (default 5) |
Structured output declared, but exposes no named fields.
No examples provided.
get_sentiment_trends Get Sentiment Trends ~120
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.
| Name | Type | Req | Description |
|---|---|---|---|
| granularity | string | – | Time bucket size — 'weekly' (default) or 'monthly' |
| since | string | – | Start date for trend analysis (ISO 8601, default 6 months ago) |
| sources | array | – | List of source specs (same format as synthesize_feedback) |
Structured output declared, but exposes no named fields.
No examples provided.
search_feedback Search Feedback ~154
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.
| Name | Type | Req | Description |
|---|---|---|---|
| limit | integer | – | Max results to return (default 20) |
| query | string | – | Search terms (e.g. 'authentication mobile' or 'pricing too expensive') |
| since | – | – | ISO 8601 datetime filter (e.g. '2026-01-01T00:00:00Z') |
| sources | – | – | Filter by source types (e.g. ['github_issues', 'appstore']) |
| target | string | – | Filter by target repo/app (e.g. 'owner/repo') |
Structured output declared, but exposes no named fields.
No examples provided.
synthesize_feedback Synthesize Feedback ~239
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.
| Name | Type | Req | Description |
|---|---|---|---|
| focus | string | – | Analysis focus — 'pain_points' (default) or 'feature_requests' |
| max_items_per_source | integer | – | Max feedback items to collect per source (default 200) |
| since | – | – | ISO 8601 datetime to filter items (e.g. '2026-01-01T00:00:00Z') |
| sources | array | – | 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/rep… |
Structured output declared, but exposes no named fields.
No examples provided.
What is the Feedback Synthesis MCP server?
Feedback Synthesis MCP is listed in the public MCP registry as io.github.sapph1re/feedback-synthesis-mcp. Synthesize GitHub Issues, HN and App Store reviews into ranked pain clusters. Pay-per-call x402. This page covers its PyPI package (feedback-synthesis-mcp).
Is the Feedback Synthesis MCP server safe to use?
Feedback Synthesis MCP scores 81 out of 100 on VerifyMCP. We found no known CVEs affecting it as of 20 September 2026. That is a record of what we were able to check automatically, not an endorsement. The category breakdown on this page shows every signal behind the number, including the ones we could not confirm.
What tools does the Feedback Synthesis MCP server expose?
Feedback Synthesis MCP exposes 4 tools: synthesize_feedback, get_pain_points, search_feedback, get_sentiment_trends. Their descriptions and schemas cost roughly 651 tokens of context every time the server is loaded.
Is the Feedback Synthesis MCP server still maintained?
Feedback Synthesis MCP is still listed as active in the MCP registry. We last reached this channel on 20 September 2026. Those dates come from our own scans of the registry and the channel itself, not from anything the publisher announced.
What licence is the Feedback Synthesis MCP server under?
Feedback Synthesis MCP declares the MIT licence, which is OSI-approved. That covers the source only, and says nothing about the cost of any service it calls.