# io.github.mpzarde/fabric-mcp (npm · @angrydad67/fabric-mcp-server)

MCP server for Fabric AI patterns

- Trust score: 69/100 (medium)
- Change this week: +23
- Registry status: active
- Liveness: live
- Owner verified: no
- Last scored: 2026-08-03

## Components

- npm · `@angrydad67/fabric-mcp-server`: 69/100 (this document), [markdown](https://verifymcp.io/servers/mpzarde-fabric-mcp/angrydad67-fabric-mcp-server.md), [page](https://verifymcp.io/servers/mpzarde-fabric-mcp/angrydad67-fabric-mcp-server)

## Channel facts

- Registry: `npm`
- Package: `@angrydad67/fabric-mcp-server`
- Version: `1.0.0`
- Transport: `stdio`

## 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 2026-08-03.

- **Supply Chain Security**: 86/100
  - No malware found by supply-chain analysis.
  - Only part of the dependency tree could be resolved (94 of 98), so this covers what we could see, not the whole tree.
  - No install/post-install scripts declared.
  - Only part of the dependency tree could be resolved (94 of 98), so this covers what we could see, not the whole tree.
- **Provenance & Transparency**: 45/100
  - Source repository is publicly reachable at the declared URL.
  - Provenance check failed: no build-provenance attestation is published.
  - Clear OSI-approved license (MIT).
  - Actively maintained (last published 166 days ago).
  - Disclosure check failed: no security disclosure policy was found in the source repository.
- **Schema Quality & AI Usability**: 77/100
  - AI-judged instruction clarity (excellent).
  - Tool/resource definitions use about 802 tokens (~42/item across 19 items; 19 tools + 0 resources), lean.
  - Usage-examples check failed: none of the tools include examples.
- **Stability & Change Management**: 23/100
  - Stability observed for 7 of 30 days with no destabilising changes; credit accrues until the full window elapses.
- **Tool Coverage**: 100/100
  - 100% of tools have a non-trivial description (not blank, and not just the tool's name).
  - 100% of tool parameters carry a description.
- **Capabilities**: 100/100
  - Implements a supported MCP spec version (2025-11-25); the latest is 2026-07-28.

## Install

### Claude

```bash
claude mcp add mpzarde-fabric-mcp -- npx -y @angrydad67/fabric-mcp-server
```

### Codex

```bash
codex mcp add mpzarde-fabric-mcp -- npx -y @angrydad67/fabric-mcp-server
```

### opencode

```json
{
  "$schema": "https://opencode.ai/config.json",
  "mcp": {
    "mpzarde-fabric-mcp": {
      "type": "local",
      "command": [
        "npx",
        "-y",
        "@angrydad67/fabric-mcp-server"
      ],
      "enabled": true
    }
  }
}
```

### OpenClaw

```bash
openclaw mcp add mpzarde-fabric-mcp --command npx --arg -y --arg @angrydad67/fabric-mcp-server
```

### Hermes

```yaml
mcp_servers:
  mpzarde-fabric-mcp:
    command: "npx"
    args: ["-y", "@angrydad67/fabric-mcp-server"]
```

### Other

```json
{
  "mcpServers": {
    "mpzarde-fabric-mcp": {
      "command": "npx",
      "args": [
        "-y",
        "@angrydad67/fabric-mcp-server"
      ]
    }
  }
}
```

## 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.

### 2026-08-03 (score 69, +49)

- [security regression] Provenance: unverified → fail
- [security improvement] Install scripts: unverified → pass
- [security improvement] Known CVEs: unverified → partial
- [functional improvement] Schema quality: unverified → excellent
- [functional improvement] Tool coverage: unverified → 100
- [functional improvement] License: unverified → pass
- [functional improvement] Dependency health: unverified → partial
- [functional improvement] Maintenance: unverified → pass
- [functional improvement] MCP protocol: unverified → pass
- [functional improvement] Stability: unverified → 0.23
- [functional] Licence: MIT

### 2026-08-02 (score 20, −16)

- [functional regression] Tool coverage: 100 → unverified

### 2026-08-01 (score 36, +10)

- [security improvement] Malware scan: unverified → pass
- [security] Stability: Stability not yet verified: we do not have a sandbox capture of the MCP schema this version of the package serves yet.
- [functional regression] Capabilities: pass → unverified

### 2026-07-31 (score 26, +2)

- [functional] We updated how we score, so this day's move reflects our rubric, not a change to the server

### 2026-07-30 (score 24, −22)

- [functional regression] Tool coverage: 100 → unverified
- [functional] First check of Schema quality: unverified

### 2026-07-27 (score 46)

First indexed and scored.

## MCP tools (19)

### `health_check` (~31 tokens)

Check if Fabric, yt-dlp, and other dependencies are installed and configured correctly. Provides installation instructions if needed.

### `run_fabric_pattern` (~58 tokens)

Run any Fabric pattern with custom input. Use this for patterns not covered by specific tools.

Input parameters:

- `input` (string, required): The input text to process with the pattern
- `pattern` (string, required): The name of the Fabric pattern to run

### `get_youtube_transcript` (~32 tokens)

Fetch transcript from a YouTube video URL

Input parameters:

- `url` (string, required): The YouTube video URL

### `list_fabric_patterns` (~16 tokens)

List all available Fabric patterns

### `analyze_youtube_video` (~104 tokens)

Fetch a YouTube transcript and analyze it with a Fabric pattern in one step. This ensures the complete transcript is processed without truncation. Use this instead of calling get_youtube_transcript and then a pattern separately.

Input parameters:

- `pattern` (string, required): The Fabric pattern to apply (e.g., 'extract_wisdom', 'summarize', 'analyze_claims', 'create_quiz', etc.)
- `url` (string, required): The YouTube video URL

### `analyze_file` (~94 tokens)

Read a file and analyze it with a Fabric pattern in one step. This ensures the complete file content is processed without truncation. Useful for analyzing documents, code files, logs, etc.

Input parameters:

- `file_path` (string, required): The absolute path to the file to analyze
- `pattern` (string, required): The Fabric pattern to apply (e.g., 'summarize', 'explain_code', 'analyze_logs', etc.)

### `analyze_url` (~92 tokens)

Fetch content from a URL and analyze it with a Fabric pattern in one step. Works with articles, documentation, blog posts, etc. This ensures complete content is processed without truncation.

Input parameters:

- `pattern` (string, required): The Fabric pattern to apply (e.g., 'extract_wisdom', 'summarize', 'analyze_paper', etc.)
- `url` (string, required): The URL to fetch and analyze

### `fabric_extract_wisdom` (~41 tokens)

Extract key insights, quotes, and wisdom from any content (articles, videos, podcasts)

Input parameters:

- `input` (string, required): The input text to process

### `fabric_summarize` (~29 tokens)

Create a concise summary of content

Input parameters:

- `input` (string, required): The input text to process

### `fabric_analyze_claims` (~32 tokens)

Analyze and fact-check claims made in content

Input parameters:

- `input` (string, required): The input text to process

### `fabric_create_quiz` (~30 tokens)

Generate quiz questions from content for learning

Input parameters:

- `input` (string, required): The input text to process

### `fabric_to_flashcards` (~30 tokens)

Convert content into flashcards for studying

Input parameters:

- `input` (string, required): The input text to process

### `fabric_analyze_paper` (~30 tokens)

Analyze academic papers or technical documents

Input parameters:

- `input` (string, required): The input text to process

### `fabric_summarize_git_diff` (~34 tokens)

Summarize git diff output for code reviews

Input parameters:

- `input` (string, required): The input text to process

### `fabric_analyze_logs` (~30 tokens)

Analyze log files for issues and patterns

Input parameters:

- `input` (string, required): The input text to process

### `fabric_analyze_incident` (~30 tokens)

Analyze security incidents or system failures

Input parameters:

- `input` (string, required): The input text to process

### `fabric_create_coding_project` (~32 tokens)

Generate project structure and planning from an idea

Input parameters:

- `input` (string, required): The input text to process

### `fabric_explain_code` (~28 tokens)

Explain code in simple terms

Input parameters:

- `input` (string, required): The input text to process

### `fabric_improve_writing` (~29 tokens)

Improve writing quality and clarity

Input parameters:

- `input` (string, required): The input text to process

## Diagnostics

Captured diagnostic sections: Provenance, Dependencies. The full working is on the page: https://verifymcp.io/servers/mpzarde-fabric-mcp/angrydad67-fabric-mcp-server#diagnostics

## Score history

- 2026-08-03: 69
- 2026-08-02: 20
- 2026-08-01: 36
- 2026-07-31: 26
- 2026-07-30: 24
- 2026-07-28: 46
- 2026-07-27: 46

## Links

- npm package: https://www.npmjs.com/package/@angrydad67/fabric-mcp-server
- Socket report: https://socket.dev/npm/package/@angrydad67/fabric-mcp-server
- Repository: https://github.com/mpzarde/fabric-mcp
- Changelog RSS feed: https://verifymcp.io/servers/mpzarde-fabric-mcp/angrydad67-fabric-mcp-server/changelog.xml
- Changelog JSON feed: https://verifymcp.io/servers/mpzarde-fabric-mcp/angrydad67-fabric-mcp-server/changelog.json
- HTML version of this page: https://verifymcp.io/servers/mpzarde-fabric-mcp/angrydad67-fabric-mcp-server