# io.github.farssaadeldin7-ux/emotional-resonance-analyzer (mcpb · emotional-resonance-analyzer.mcpb)

Map the modelled emotional arc of a cut against the attention its form expects — a structural...

- Trust score: 35/100 (low)
- Registry status: active
- Liveness: live
- Owner verified: no
- Last scored: 2026-10-22

## Components

- mcpb · `emotional-resonance-analyzer.mcpb`: 35/100 (this document), [markdown](https://verifymcp.io/servers/farssaadeldin7-ux-emotional-resonance-analyzer/emotional-resonance-analyzer.md), [page](https://verifymcp.io/servers/farssaadeldin7-ux-emotional-resonance-analyzer/emotional-resonance-analyzer)

## Channel facts

- Registry: `mcpb`
- Package: `https://github.com/farssaadeldin7-ux/claude-plugin-suite/releases/download/mcpb-v0.1.3/emotional-resonance-analyzer.mcpb`
- Version: `0.1.3`
- Transport: `stdio`
- SHA-256: `d7402caabd704adc7055e90a4708ede1d5f9c37fc180b9be9dd8a49b3ae8e6aa`

## Trust breakdown

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. Scores are 0–100 per category. Scoring method: https://verifymcp.io/docs/scoring (what has changed: https://verifymcp.io/docs/scoring/changelog)

Scored 2026-10-22.

- **Supply Chain Security**: 0/100
  - No malware scan is available for this kind of package: the supply-chain vendors we use do not cover it. This is a permanent gap in our coverage, not a finding about the package.
  - Known CVEs could not be checked: we found no dependency list to check in this bundle: either it runs a compiled binary, whose bundled manifests we do not read, or none of the package.json, requirements.txt, pyproject.toml or uv.lock files it ships for its runtime declares a dependency we can read (a requirements.txt that only points at a setup.py project, a local path, or a nested requirements file we cannot follow counts as none).
  - Install-script risk not yet assessed.
  - Dependency health could not be checked: we found no dependency list to check in this bundle: either it runs a compiled binary, whose bundled manifests we do not read, or none of the package.json, requirements.txt, pyproject.toml or uv.lock files it ships for its runtime declares a dependency we can read (a requirements.txt that only points at a setup.py project, a local path, or a nested requirements file we cannot follow counts as none).
- **Provenance & Transparency**: 32/100
  - Source repository is publicly reachable at the declared URL.
  - Provenance check failed: no build-provenance attestation is published.
  - License check failed: no license is declared.
  - Actively maintained (last published 1 days ago).
  - Disclosure check failed: no security disclosure policy was found in the source repository.
- **Schema Quality & AI Usability**: 79/100
  - 100% of prompts and resources have a non-trivial description (not blank, and not just the item's name).
  - AI-judged instruction clarity (good).
  - Context-footprint check failed: tool/resource definitions use about 1987 tokens (~141/item across 14 items; 14 tools + 0 resources), over budget; trim descriptions and params.
  - Usage-examples check failed: none of the tools include examples.
- **Stability & Change Management**: 0/100
  - Stability not yet verified: not enough scan history yet (needs a 30-day window).
- **Tool Coverage**: 98/100
  - 100% of tools have a non-trivial description (not blank, and not just the tool's name).
  - 94% of tool parameters carry a description.
- **Tool Safety**: 100/100
  - No prompt-injection markers were found in the server instructions, tool names or descriptions we captured.
  - We read all 14 captured tool definition(s), and no name or description among them implies an irreversible operation.
  - An AI judge read all 15 captured unit(s) of tool text and found none that tries to manipulate the model reading it.
- **Capabilities**: 20/100
  - Spec-recency check failed: implements MCP spec 2024-11-05; the latest is 2026-07-28.

**Unverified: 2 categories.** Categories scored 0 because we could not verify them: a data source with nothing on this package, evidence we could not reach, or a check we could not run. We only credit what we can confirm.

## Install

### How do I install the io.github.farssaadeldin7-ux/emotional-resonance-analyzer MCP server?

io.github.farssaadeldin7-ux/emotional-resonance-analyzer ships as an MCPB bundle, a single file you download and open in an MCP host that supports MCPB bundles, such as Claude Desktop. The host reads the launch command from the bundle's own manifest, so there is no command to copy. Before opening it, check the file's SHA-256 matches the one the MCP registry declares for it, d7402caabd704adc7055e90a4708ede1d5f9c37fc180b9be9dd8a49b3ae8e6aa; the commands to do that are on this page. If it does not match, do not open it.

- Download bundle: `https://github.com/farssaadeldin7-ux/claude-plugin-suite/releases/download/mcpb-v0.1.3/emotional-resonance-analyzer.mcpb`
- SHA-256: `d7402caabd704adc7055e90a4708ede1d5f9c37fc180b9be9dd8a49b3ae8e6aa`

### Verify on macOS / Linux

```bash
echo "d7402caabd704adc7055e90a4708ede1d5f9c37fc180b9be9dd8a49b3ae8e6aa  emotional-resonance-analyzer.mcpb" | shasum -a 256 -c
```

### Verify on Windows (PowerShell)

```powershell
(Get-FileHash -Algorithm SHA256 .\emotional-resonance-analyzer.mcpb).Hash -eq "d7402caabd704adc7055e90a4708ede1d5f9c37fc180b9be9dd8a49b3ae8e6aa"
```

## Changelog

Every change recorded for this component, newest first. Days that predate change tracking, or that we cannot explain, say so: "we were watching and nothing happened" and "we were not watching" are different claims.

### 2026-10-21 (score 35)

First indexed and scored.

## MCP tools (14)

### `form_conventions` (~92 tokens)

The attention conventions of each encoded form — short_doc, feature_doc, branded, youtube_longform, broadcast: when the first question must open, typical departure points, the central-close window and the talking-head limit. Conventions as practitioners describe them, not audience measurements. Omit form to list all of them.

Input parameters:

- `form` (string): short_doc, feature_doc, branded, youtube_longform or broadcast.

### `scoring_anchors` (~74 tokens)

The scoring vocabulary the skill scores with: valence anchors (-3..+3), intensity anchors (0..5), the scene-boundary definition, the series-pattern readings, the Q&A ledger format and the edit effort scale. Scoring itself is a reading the editor or the skill makes — this only returns the anchors.

### `dropoff_causes` (~96 tokens)

The five diagnosable drop-off causes — no question open, stakes not personalised, tonal monotony, premature resolution, texture starvation — each with its mechanical tell, threshold, standard fix and a worked example, plus the priority order when tells overlap. Omit cause to list all five.

Input parameters:

- `cause` (string): no_question_open, stakes_not_personalised, tonal_monotony, premature_resolution or texture_starvation.

### `check_tells` (~261 tokens)

Run the five drop-off tells mechanically against a scored scene table and Q&A ledger: tripped stretches by timecode and cause, ledger findings, overlaps with priority, and total exposed run-time. Threshold arithmetic on scores the editor supplies — it does not read the film, and a tripped tell is a stretch to watch again, not a proven defect. Requires a paid plan.

Input parameters:

- `form` (string): Optional form id; its talking-head limit and central-close window replace the generic baselines.
- `questions` (array, required): The Q&A ledger. Each question: id, opens (timecode), closes (timecode or omitted), how (on_screen, implied, off_screen, never), weight (central, major, minor). Exactly one should be central.
- `scenes` (array, required): The scored scene table, in timeline order. Each scene: timecode (start, "MM:SS"), valence (-3..+3 integer), intensity (0..5 integer); optionally duration, description, opens/closes (question ids), in…
- `total_runtime` (string): Total run-time, e.g. "11:40". Defaults to the end of the last scene.

### `plot_arc` (~181 tokens)

The valence and intensity series read as a derivative: per-scene changes, flat stretches with the end scores quoted, the largest gap between valence changes of two or more points, intensity peaks and whether each one lands, and the last-quarter trend. Arithmetic on the editor's scores — flat stretches are the problem, not low stretches, and nothing here is a score of the film. Requires a paid plan.

Input parameters:

- `scenes` (array, required): The scored scene table, in timeline order. Each scene: timecode (start, "MM:SS"), valence (-3..+3 integer), intensity (0..5 integer); optionally duration, description, opens/closes (question ids), in…
- `total_runtime` (string): Total run-time. Defaults to the end of the last scene.

### `engagement_graph` (~313 tokens)

The modelled engagement curve, drawn: one point per scene, computed deterministically from the method's existing arithmetic — the editor's intensity score, the valence movement between consecutive scenes, and a penalty per drop-off tell from the check_tells logic — with every flagged stretch shaded, written as a self-contained SVG line chart the editor can look at. Modelled from scene scores: it is not a measurement of any audience, predicts nothing, and a real retention curve outranks it entirely. Requires a paid plan.

Input parameters:

- `form` (string): Optional form id; named in the chart and used for the tell thresholds.
- `output_path` (string): Where to write the SVG. Defaults to ./engagement-graph.svg in the server's working directory.
- `questions` (array): The Q&A ledger. Each question: id, opens (timecode), closes (timecode or omitted), how (on_screen, implied, off_screen, never), weight (central, major, minor). Exactly one should be central.
- `scenes` (array, required): The scored scene table, in timeline order. Each scene: timecode (start, "MM:SS"), valence (-3..+3 integer), intensity (0..5 integer); optionally duration, description, opens/closes (question ids), in…
- `total_runtime` (string): Total run-time, e.g. "11:40". Defaults to the end of the last scene.

### `reconcile_curve` (~289 tokens)

Classify each drop in a real retention curve against the tripped tells: explained (lands on or just after a tell — with the cause), unexplained (usually performance, music or grade, which this cannot see), or model-only (a tell the audience never minded — the model was wrong there). The curve wins; no flag is adjusted to fit it. Requires a paid plan.

Input parameters:

- `after_window_seconds` (number): How far after a tripped stretch a drop still counts as "just after". Default 15.
- `drops` (array, required): The measured drops, read off the platform's retention curve by the editor.
- `form` (string): Optional form id.
- `questions` (array, required): The Q&A ledger. Each question: id, opens (timecode), closes (timecode or omitted), how (on_screen, implied, off_screen, never), weight (central, major, minor). Exactly one should be central.
- `scenes` (array, required): The scored scene table, in timeline order. Each scene: timecode (start, "MM:SS"), valence (-3..+3 integer), intensity (0..5 integer); optionally duration, description, opens/closes (question ids), in…
- `total_runtime` (string): Total run-time. Defaults to the end of the last scene.

### `log_analysis` (~220 tokens)

Record an analysis in the local log — film, cut version, form, what was flagged, the three chosen cuts with effort ratings, and whether real retention data was supplied. Logging two versions of the same film is how a recut gets compared honestly. Requires a paid plan; the licence check is the only thing this tool sends anywhere.

Input parameters:

- `cuts` (array): The three highest-leverage cuts, ranked. Give exactly three — a ranked list of eleven changes is a way of not having an opinion.
- `film` (string, required): Working title, for later recognition.
- `findings_summary` (string): The tripped tells in one or two lines.
- `form` (string): The form judged against.
- `notes` (string)
- `retention_data_supplied` (boolean): Whether a real retention curve was in the room. If true, the curve's reading outranks the log's flags.
- `total_runtime` (string)
- `version` (string): Cut version, e.g. "assembly", "fine cut 2".

### `review_analyses` (~88 tokens)

The local record of past analyses — list them, filter by film to set two versions of a cut side by side, or fetch one in full. Reading only; it makes no new claim. Requires a paid plan.

Input parameters:

- `analysis_id` (string): Fetch one analysis in full.
- `film` (string): Filter by film title.
- `limit` (number): Default 20.

### `license_status` (~51 tokens)

Check the Emotional Resonance Analyzer licence on this machine: plan, included capabilities, remaining quota and seat usage. Call this when a paid tool reports a licence problem, or when the user asks what their plan includes.

### `license_activate` (~68 tokens)

Store and activate a Emotional Resonance Analyzer licence key on this machine. Use when the user pastes a licence key, or after a purchase completes.

Input parameters:

- `license_key` (string, required): The licence key, e.g. PS-XXX-00000-00000-00000-0000

### `start_checkout` (~75 tokens)

Begin a Emotional Resonance Analyzer purchase. Returns a Stripe Checkout link. Call list_plans first if the user has not chosen a plan.

Input parameters:

- `email` (string): Email address for the receipt and licence.
- `plan` (string, required): Plan id: "pro" or "team". Use list_plans to see the options.

### `list_plans` (~25 tokens)

List the available Emotional Resonance Analyzer plans, prices and what each one includes.

### `billing_portal` (~31 tokens)

Open the Stripe billing portal to change plan, update the card, or cancel the Emotional Resonance Analyzer subscription.

## Diagnostics

Captured diagnostic sections: Provenance. The full working is on the page: https://verifymcp.io/servers/farssaadeldin7-ux-emotional-resonance-analyzer/emotional-resonance-analyzer#diagnostics

## Score history

- 2026-10-22: 35
- 2026-10-21: 35

## Common questions

### What is the io.github.farssaadeldin7-ux/emotional-resonance-analyzer MCP server?

io.github.farssaadeldin7-ux/emotional-resonance-analyzer is an MCP server listed in the public MCP registry as io.github.farssaadeldin7-ux/emotional-resonance-analyzer. Map the modelled emotional arc of a cut against the attention its form expects, a structural... This page covers its MCPB bundle (https://github.com/farssaadeldin7-ux/claude-plugin-suite/releases/download/mcpb-v0.1.3/emotional-resonance-analyzer.mcpb).

### Is the io.github.farssaadeldin7-ux/emotional-resonance-analyzer MCP server safe to use?

io.github.farssaadeldin7-ux/emotional-resonance-analyzer scores 35 out of 100 on VerifyMCP. 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 io.github.farssaadeldin7-ux/emotional-resonance-analyzer MCP server expose?

io.github.farssaadeldin7-ux/emotional-resonance-analyzer exposes 14 tools: form_conventions, scoring_anchors, dropoff_causes, check_tells, plot_arc, and 9 more. Their descriptions and schemas cost roughly 1,864 tokens of context every time the server is loaded.

## Links

- Repository: https://github.com/farssaadeldin7-ux/claude-plugin-suite
- Changelog RSS feed: https://verifymcp.io/servers/farssaadeldin7-ux-emotional-resonance-analyzer/emotional-resonance-analyzer.xml
- Changelog JSON feed: https://verifymcp.io/servers/farssaadeldin7-ux-emotional-resonance-analyzer/emotional-resonance-analyzer.json
- HTML version of this page: https://verifymcp.io/servers/farssaadeldin7-ux-emotional-resonance-analyzer/emotional-resonance-analyzer