frame-check-mcp 1.1.1

Frame Check MCP server: deterministic structural framing analysis for AI-generated documents, with default-on frame-divergence block per FRAME_DIVERGENCE_CONTRACT_v1 c1.0. MCP surface delegates V4.2 judgment to the caller's agent model; zero Frame Check LLM cost per query.

Frame Check

See what any document does not show you.

Frame Check is a deterministic structural framing analysis tool. It names which analytical perspectives a document takes, which it omits, and how it positions the reader, and it cross-checks the document's numeric claims against primary sources a language model can't reach (SEC EDGAR, FRED, World Bank, and others). It makes no LLM call of its own, so the same document always returns the same reading at no model cost.

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.

Verifying the wheel (sigstore attestation)

Every published wheel ships with a sigstore build-provenance attestation generated inside the GitHub Actions publish workflow via OIDC. Adopters who want to verify the wheel was built from this repository's CI (and not modified between the runner and PyPI) can do so with the gh CLI:

pip download frame-check-mcp --no-deps -d /tmp/fc-verify
gh attestation verify /tmp/fc-verify/frame_check_mcp-*.whl \
  --owner Clarethium

A passing verification proves the wheel artifact's hash matches the one signed by the publish workflow run for the corresponding tag, with the workflow file path and git SHA recorded in the attestation. Verification is optional; security-conscious deployments and packaging mirrors may want it as part of their install pipeline.

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 signals, not verified labels.
• 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, beta layer from the structural profile. It surfaces candidate matches, under-detection markers, density caveats, and confidence states rather than confident labels, so you can see where the tool is unsure instead of trusting an overconfident verdict.

Calibration figures, honest limits, and the methodology behind them live in the methodology at frame.clarethium.com/corpus/methodology.

Why this and not just an LLM

An MCP-compatible AI client can already analyse a document by prompting an LLM. Frame Check earns its install footprint where the LLM falls short:

• Determinism. The structural layer returns the same numbers for the same input across runs, deploys, and model versions. An LLM asked "what frames does this document use" gives a different answer each time and a different answer per model. Reproducible analysis needs the deterministic shape; opinions can layer on top.
• Zero per-query cost. Frame Check's MCP server makes no LLM call server-side. The caller's agent does the prose interpretation if the user wants that. This means a frame-check on a 10,000-word document costs the user $0.00, not the $0.05 to $0.50 an LLM call would charge.
• Explicit absence. The frame-divergence block names what the document does not address by comparing matched frames against the Frame Vocabulary Standard catalog. An LLM asked "what's missing" hallucinates plausible-sounding gaps; Frame Check enumerates catalog entries that did not fire on the text and says so.
• Calibrated detection. The named-pattern layer is labelled beta in the API responses (engine_status: beta) and surfaces under-detection markers rather than confident labels. You get an honest "this is uncertain" instead of a confident guess.
• Source verification. Numeric claims with provider coverage get cross-checked against SEC EDGAR / FRED / World Bank / REST Countries / Alpha Vantage / Wolfram Alpha at provider pricing tiers (zero or user-keyed). An LLM asked "is this number right" cannot fetch primary sources; Frame Check does.

Deterministic, source-grounded measurement is not work an LLM is suited to do. Frame Check provides that layer so the LLM can lean on it instead of being asked to do that work in-band.

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 point 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, understand frame divergence, read the worked examples). The full inventory:

• docs/MCP_SERVER.md: MCP server reference (tools, resources, prompts)
• docs/COOKBOOK.md: five recipes for common adopter tasks (frame-check before agent commit, divergence at decision points, source-grounded verification, two-LLM comparison, custom FVS rule)
• docs/FRAME_DIVERGENCE_CONTRACT_v1.md: interface contract for the Frame Divergence emission shape (c1.0)
• 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 methodology behind the Frame Vocabulary Standard is documented at frame.clarethium.com/corpus/methodology

Running tests

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

Or directly via pytest:

python3 -m pytest -q

26 test files under tests/, ~30 seconds end-to-end. Includes 40 adversarial dispatcher test functions in tests/test_mcp_adversarial.py (parametrized into 63 tests at collection time), a per-module 80% coverage gate on the seven wheel-surface modules (scripts/check_per_module_coverage.py), the cookbook-recipe contract suite (tests/test_cookbook_recipes.py), and the genre-classifier + frame-divergence 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 .github/CITATION.cff for the citable form. Frame Check is authored by Lovro Lucic.

Contributing

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

Issues

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