# io.github.lluvr/frame-check (pypi · frame-check-mcp)

Deterministic structural framing analysis for AI-generated documents; no LLM call of its own.

- Trust score: 80/100 (high trust)
- Registry status: active
- Liveness: live
- Owner verified: no
- Last scored: 2026-11-06

## Components

- pypi · `frame-check-mcp`: 80/100 (this document), [markdown](https://verifymcp.io/servers/lluvr-frame-check/frame-check-mcp.md), [page](https://verifymcp.io/servers/lluvr-frame-check/frame-check-mcp)

## Channel facts

- Registry: `pypi`
- Package: `frame-check-mcp`
- Version: `1.2.1`
- 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 2026-11-06.

- **Supply Chain Security**: 100/100
  - No malware found by supply-chain analysis.
  - No known CVEs affecting this package version or its production dependencies.
  - No install/post-install scripts declared.
  - 0 of 1 dependencies flagged as unhealthy.
- **Provenance & Transparency**: 100/100
  - Source repository is publicly reachable at the declared URL.
  - Cryptographically verified build provenance (signed, bound to lluvr/frame-check).
  - Clear OSI-approved license (Apache-2.0).
  - Actively maintained (last published 0 days ago).
  - Publishes a security disclosure policy (SECURITY.md).
- **Schema Quality & AI Usability**: 87/100
  - 100% of prompts and resources have a non-trivial description (not blank, and not just the item's name).
  - AI-judged instruction clarity (excellent).
  - Tool/resource definitions use about 8519 tokens (~83/item across 102 items; 2 tools + 100 resources), lean.
  - 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**: 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.
- **Tool Safety**: 100/100
  - No prompt-injection markers were found in the server instructions, tool names or descriptions we captured.
  - We read all 2 captured tool definition(s), and no name or description among them implies an irreversible operation.
  - An AI judge read all 4 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: 1 category.** A category scored 0 because we could not verify it: 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.lluvr/frame-check MCP server?

io.github.lluvr/frame-check runs locally as a PyPI package, launched with uvx frame-check-mcp. Ready-made configuration for Claude, Cursor, VS Code, Codex and 5 more is on this page, copied from each client's own documentation.

### Claude

```bash
claude mcp add lluvr-frame-check -- uvx frame-check-mcp
```

### Cursor

```json
{
  "mcpServers": {
    "lluvr-frame-check": {
      "command": "uvx",
      "args": [
        "frame-check-mcp"
      ]
    }
  }
}
```

### VS Code

```json
{
  "servers": {
    "lluvr-frame-check": {
      "command": "uvx",
      "args": [
        "frame-check-mcp"
      ]
    }
  }
}
```

### Codex

```bash
codex mcp add lluvr-frame-check -- uvx frame-check-mcp
```

### opencode

```json
{
  "$schema": "https://opencode.ai/config.json",
  "mcp": {
    "lluvr-frame-check": {
      "type": "local",
      "command": [
        "uvx",
        "frame-check-mcp"
      ],
      "enabled": true
    }
  }
}
```

### OpenClaw

```bash
openclaw mcp add lluvr-frame-check --command uvx --arg frame-check-mcp
```

### Hermes

```yaml
mcp_servers:
  lluvr-frame-check:
    command: "uvx"
    args: ["frame-check-mcp"]
```

### Netclaw

```json
{
  "McpServers": {
    "lluvr-frame-check": {
      "Transport": "stdio",
      "Command": "uvx",
      "Arguments": [
        "frame-check-mcp"
      ]
    }
  }
}
```

### Vellum

```bash
assistant mcp add lluvr-frame-check -t stdio -c uvx -a frame-check-mcp
```

### Other

```json
{
  "mcpServers": {
    "lluvr-frame-check": {
      "command": "uvx",
      "args": [
        "frame-check-mcp"
      ]
    }
  }
}
```

## 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.

### 2026-11-06 (score 80, +15)

- [security improvement] Malware scan: unverified → pass

### 2026-11-05 (score 65)

First indexed and scored.

## MCP tools (2)

### `frame_check` (~1168 tokens)

Read a document with a structural lens: which analytical perspectives it covers, which it omits, how confidently it speaks, and which framing patterns from the Frame Vocabulary Standard fire on it.

Use this when the user pastes a document and asks for a structural read, when you want to self-audit your own last response before the user acts on it, or when the user pastes another AI's reply and asks what that AI did structurally.

Zero-arg invocation works for any English analytical document: `frame_check(document_text=<text>)`. The defaults produce the full output, including the divergence block (perspectives the document does not use) and a per-call suggested_next_actions block. Pass the optional parameters only when the user has provided the relevant context (source material, decision goal, working-memory budget).

Compose ONE insight grounded in the cited measurements (a reading the user could not see by reading the document themselves), not a walk through the measurements one by one. The measurements are Frame Check's; the reading is yours. Cite each measurement as Frame Check's; frame the reading as a reading ('the pattern reads as X'), never as a verdict ('the document is X'). Repeated calls with identical inputs return identical measurements; zero LLM cost on the deterministic path.

Input parameters:

- `compose_budget` (string): Default 'standard'. Switch to 'minimal' for tighter working-memory budgets (per-turn self-audit loops, batch document processing) or to 'full' when you specifically need the inline claim_level_treatm…
- `divergence_rendering` (string): Default 'list'. Switch to 'teaching_questions' when the absent-frame records should carry a teaching_question field for surfacing as questions rather than identifiers. All other modes return the same…
- `document_text` (string, required): The document to analyse. English. 300 to 10,000 words is the validated range; under 100 words the analysis carries a low-confidence note, over 1,000,000 characters returns a truncation guidance. Mark…
- `include_divergence` (boolean): Default true. You do not need to pass this. The divergence block (frame patterns the document does not use, sorted by signal_strength) is the headline output and ships by default. Set explicitly to f…
- `include_frame_opportunities` (boolean): Default false. Set true when the user wants up to 3 LLM-augmented document-specific questions composed from absent-frame teaching questions plus the document's content. Adds frame_opportunities to th…
- `source_text` (string): Pass when the user has the source material the document was supposed to ground in (research report, SEC filing, primary source). Unlocks digit-level source-fidelity verification (Layer 4) plus a sent…
- `user_context` (string): Pass when the user has stated their situation, role, or decision context in the conversation (for example, 'I am a startup founder making a hire decision in healthcare AI', 'reviewing a research pape…
- `user_goal` (string): Pass when the user has stated a goal for invoking Frame Check. One of: 'decide' (working through a choice), 'brainstorm' (exploring options), 'persuade' (writing to influence), 'learn' (understanding…

### `frame_compare` (~332 tokens)

Compare the framing of two documents on the same subject. Surfaces shared blind spots, unique coverage gaps, voice / temporal / epistemic deltas, and a structured framing-differences narrative with per-dimension reader implications.

Use this when the user has two documents on the same subject (two AI responses to the same prompt, two analyst memos, an earnings release versus a press summary) and wants to see how they frame the same question differently.

Pass `document_a_label` and `document_b_label` only when the user has named the documents (for example 'Gemini response' and 'Claude response'). Otherwise the comparison narrative falls back to 'Document A' and 'Document B'.

Cite each measurement as Frame Check's; never imply that one document is better, more rigorous, or more biased than the other. The structural comparison surfaces what differs; the reader judges what the difference means. Repeated calls with identical inputs return identical results. No LLM is invoked.

Input parameters:

- `document_a_label` (string): Optional short label for document A (e.g. 'Industry view' or 'Gemini response'). Used in the comparison narrative. Defaults to 'Document A'.
- `document_a_text` (string, required): The first document to compare. English. 300-10,000 words.
- `document_b_label` (string): Optional short label for document B. Defaults to 'Document B'.
- `document_b_text` (string, required): The second document to compare. English. 300-10,000 words. Should be on the same subject as document_a_text for the comparison to be meaningful.

## Diagnostics

Captured diagnostic sections: Provenance, Dependencies. The full working is on the page: https://verifymcp.io/servers/lluvr-frame-check/frame-check-mcp#diagnostics

## Score history

- 2026-11-06: 80
- 2026-11-05: 65

## Common questions

### What is the io.github.lluvr/frame-check MCP server?

io.github.lluvr/frame-check is an MCP server listed in the public MCP registry as io.github.lluvr/frame-check. Deterministic structural framing analysis for AI-generated documents; no LLM call of its own. This page covers its PyPI package (frame-check-mcp).

### Is the io.github.lluvr/frame-check MCP server safe to use?

io.github.lluvr/frame-check scores 80 out of 100 on VerifyMCP. We found no known CVEs affecting it as of 6 November 2026. It declares no install or post-install scripts. Its build provenance is signed and verified. 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.lluvr/frame-check MCP server expose?

io.github.lluvr/frame-check exposes 2 tools: frame_check, frame_compare. Their descriptions and schemas cost roughly 1,500 tokens of context every time the server is loaded.

### Is the io.github.lluvr/frame-check MCP server still maintained?

io.github.lluvr/frame-check is still listed as active in the MCP registry. We last reached this channel on 6 November 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 io.github.lluvr/frame-check MCP server under?

io.github.lluvr/frame-check declares the Apache-2.0 licence, which is OSI-approved. That covers the source only, and says nothing about the cost of any service it calls.

### What did VerifyMCP test on the io.github.lluvr/frame-check MCP server?

VerifyMCP ran 15 checks on io.github.lluvr/frame-check, covering Malware scan, Known CVEs, Install scripts, Dependency health and 11 more. One item could not be completed, a gap on our side rather than a finding about io.github.lluvr/frame-check: Stability. One category scores 0 for that reason, because we only credit what we can confirm. Endpoint Security and Transport & Reachability do not apply to a PyPI package, so they were not tested. The score does not measure everything, and the scoring documentation lists what it leaves out.

## Links

- PyPI project: https://pypi.org/project/frame-check-mcp/
- Socket report: https://socket.dev/pypi/package/frame-check-mcp
- Repository: https://github.com/lluvr/frame-check
- Changelog RSS feed: https://verifymcp.io/servers/lluvr-frame-check/frame-check-mcp.xml
- Changelog JSON feed: https://verifymcp.io/servers/lluvr-frame-check/frame-check-mcp/changelog.json
- Trust report (JSON): https://verifymcp.io/servers/lluvr-frame-check/frame-check-mcp.json
- HTML version of this page: https://verifymcp.io/servers/lluvr-frame-check/frame-check-mcp