Metadata-Version: 2.4
Name: frame-check-mcp
Version: 1.0.8
Summary: 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.
Author-email: Lovro Lucic <lovro.lucic@gmail.com>
License-Expression: Apache-2.0
Project-URL: Homepage, https://blog.clarethium.com/frame-check
Project-URL: Repository, https://github.com/Clarethium/frame-check
Project-URL: Issues, https://github.com/Clarethium/frame-check/issues
Project-URL: Changelog, https://github.com/Clarethium/frame-check/blob/master/CHANGELOG.md
Project-URL: Security, https://github.com/Clarethium/frame-check/blob/master/SECURITY.md
Project-URL: Methodology, https://frame.clarethium.com/corpus/methodology/
Project-URL: Frame Library, https://frame.clarethium.com/corpus/library/
Keywords: mcp,model-context-protocol,framing,text-analysis,agents,claude-desktop,cursor,frame-divergence,decision-readiness
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Text Processing :: Linguistic
Requires-Python: >=3.10
Description-Content-Type: text/markdown
License-File: LICENSE
License-File: NOTICE
Requires-Dist: PyYAML<7.0,>=6.0
Provides-Extra: test
Requires-Dist: pytest>=7.0; extra == "test"
Requires-Dist: pytest-timeout>=2.0; extra == "test"
Requires-Dist: pytest-cov>=4.0; extra == "test"
Dynamic: license-file

# Frame Check

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[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.19888849.svg)](https://doi.org/10.5281/zenodo.19888849)
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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`.

### 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; 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.

## 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. Citable
  research 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 reports detector
  F1 = 0.36 against expert labelers in a pre-registered validation,
  below the useful threshold of 0.4. That number is in this README,
  in the API responses (`engine_status: beta`), and the wheel ships
  the under-detection-marker pivot rather than confident labels.
  Honest calibration matters more than confident output.
- **Source verification.** Numeric claims with provider coverage get
  cross-checked against SEC EDGAR / FRED / World Bank / 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.

The wedge is not the LLM's job. Frame Check makes it possible for
the LLM to lean on a deterministic, source-grounded measurement
layer 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`](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/`](data/worked_examples/) for the full set.

## Documentation

Browse [`docs/README.md`](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/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)
- `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

25 test files under `tests/`, ~30 seconds end-to-end. Includes 40 adversarial dispatcher test functions in `tests/test_mcp_adversarial.py` (parametrized into more tests at collection time), 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 `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
