Frame Check

See what any document does not show you.

Frame Check is a structural framing analysis tool. It names which perspectives a document takes, which it omits, and how it positions the reader. Numerical claims are cross-checked against authoritative sources where coverage exists.

Quickstart (MCP server)

The PyPI package frame-check-mcp is the Model Context Protocol server. It runs locally and gives any MCP-compatible AI client (Claude Desktop, Cursor, Cline, Continue.dev, etc.) deterministic structural framing analysis as a tool.

pip install frame-check-mcp

Then point your MCP client at the installed entry point. For Claude Desktop, add to claude_desktop_config.json:

{
  "mcpServers": {
    "frame-check": {
      "command": "frame-check-mcp"
    }
  }
}

Restart the client. Then in any conversation: "Can you frame-check this document?" Full install + verification details in docs/MCP_SERVER.md.

What it does

Pass a document and Frame Check returns:

• A structural framing profile: which of five analytical perspectives (causes, risks, stakeholders, trends, uncertainty) the document covers, which it omits, and the density of each.
• Voice and epistemic posture: how the document positions the reader, and what share of claims are attributed to sources.
• Temporal orientation: whether the document grounds its conclusions in historical data, present state, or projections.
• Frame Vocabulary Standard candidate matches: named frame patterns whose rule-based signals fire on the text, each with identification cues and worked examples. Matches are candidate-level; precision against multi-source labeling is an active research question.
• Source-network verification: numeric claims checked against SEC EDGAR, FRED, World Bank, REST Countries, Alpha Vantage, and Wolfram Alpha where those providers have coverage.
• An optional AI narrative interpreting framing at prose level. Labelled distinctly so readers do not conflate language-model interpretation with deterministic measurement.

Approach

Structural measurement is the floor. Every framing claim the tool makes is computed from deterministic pattern matchers and always returns the same result for the same input. AI-assisted interpretation is available as enrichment where an API key is configured, but is labelled as such and never hidden behind the structural layer.

Verification is bounded. The tool only verifies numeric claims against providers with genuine coverage for the claim type, and it surfaces its own calibration results (precision, recall, F1 per provider) rather than asserting verdicts without evidence.

Named-pattern detection is a separate reliability layer from the structural profile. Detector F1 = 0.36 against expert labelers in a pre-registered validation, below the useful threshold of 0.4. The pivot to evidence surfacing (under-detection markers, density caveats, confidence states) rather than confident labels is the load-bearing claim at v0; a reader-aid study (Track B, pre-registered) tests whether this surfacing actually helps a reader see framing they would otherwise miss.

Honest limits and the methodology that generates them live in the public canon at github.com/Clarethium/lodestone.

Worked example

Same prompt, four frontier LLMs, four materially different framing signatures. data/worked_examples/four-llms-on-bitcoin-retirement-2026.md runs Claude Haiku 4.5, GPT-5, Grok 4.1 Fast Reasoning, and Gemini 2.5 Flash against an investment question and surfaces the per-model structural shape: voice, coverage, frame matches, sourcing rate. The sovereignty case in plain form: your AI is one framing choice among several, not the framing.

Five more published examples live alongside it: framings of an LLM response to a life-decision prompt, an AI-company founder essay, an FOMC monetary-policy statement, and a Source-Network verification pass on an LLM-summarised earnings release, plus a divergence walk-through on Claude's Bitcoin retirement recommendation. See data/worked_examples/ for the full set.

Documentation

Browse docs/README.md for reading paths organised by intent (install + use, evaluate the methodology, understand frame divergence, validate the substrate, verify the audit, read the worked examples). The full inventory:

• docs/MCP_SERVER.md: MCP server reference (tools, resources, prompts)
• docs/FRAME_DIVERGENCE_CONTRACT_v1.md: interface contract for the Frame Divergence emission shape (c1.0)
• docs/VALIDATION_PROGRAM.md: observational + formal validation plans
• docs/RATERS.md: rater protocol for the validation program
• data/frame_library/: 20-entry Frame Vocabulary Standard catalog
• data/worked_examples/: published worked examples with multi-LLM comparisons + per-document Frame Check analysis (6 entries)
• The Frame Vocabulary Standard's methodology canon lives at github.com/Clarethium/lodestone

Running tests

pip install -e .[test]
python3 run_tests.py

Or directly via pytest:

python3 -m pytest -q

24 test files, ~30 seconds end-to-end. Includes 40+ adversarial dispatcher test functions in tests/test_mcp_adversarial.py (parametrized into more tests at collection time) plus the V4.2 engine + classifier coverage.

License

Apache-2.0 for code; CC-BY-4.0 for the FVS library and worked examples (see NOTICE for the per-directory enumeration).

Citation

If Frame Check is useful in your work, see CITATION.cff for the citable form. Frame Check is authored by Lovro Lucic.

Contributing

Sign-off-by-DCO required per CONTRIBUTING.md. Governance per GOVERNANCE.md (BDFL model with named forcing functions for canon-promotion decisions). External rater engagement per docs/RATERS.md.

Issues

https://github.com/Clarethium/frame-check/issues