Content Evidence Studio

Original Python tools for evidence-first marketing work through ChatGPT-compatible MCP, OpenAI Responses agents, or a local CLI. Version 0.1.0 is an independently implemented alpha release; it is not a fork, a renamed Claude project, or a claim of feature parity with larger reference projects.

Working capabilities

• create_brief: audience/goal brief, source ledger, outline and acceptance criteria.

• review_draft: unknown citation IDs, quantitative-claim review flags, prohibited terms and duplicate headings.

• compare_revision: line diff plus added/removed citation inventory.

• suggest_internal_links: inspectable English lexical overlap over supplied pages.

• plan_content_refresh: normalized daily click comparisons with scope confirmation.

The OpenAI agent runner can draft and revise using these tools and supplied evidence. Deterministic tools do not pretend to generate finished prose. No web research, CMS publishing, embeddings, plagiarism check or claim-entailment verification is bundled. Citation existence is not proof of truth.

Related MCP server: content-qa-mcp

Run the offline example

Python 3.11+ is required. No API key is needed for this example.

python -m venv .venv
. .venv/bin/activate
pip install -e '.[dev]'
content-evidence-studio tools
content-evidence-studio run create_brief examples/demo.json --out output/demo
pytest -q
ruff check .

The command creates report.json, report.csv, report.md and an escaped report.html. JSON contains the complete structured result; CSV contains tabular findings or metrics when available. No output is published automatically. Inputs are validated and bounded. Errors fail explicitly instead of producing invented success.

Use with ChatGPT and OpenAI

content-evidence-studio serve
# Local HTTP endpoint: http://127.0.0.1:8765/mcp
content-evidence-studio serve --transport streamable-http
# Requires OPENAI_API_KEY and OPENAI_MODEL in your environment:
content-evidence-studio agent examples/task.txt --out output/agent

See ChatGPT connection instructions. A ChatGPT connection needs a reachable endpoint or supported tunnel; this repository is not already hosted or connected to your account. The local HTTP service has no application authentication and must not be exposed directly. Deterministic MCP tools need no OpenAI key. The agent runner is optional and makes billable API calls.

Repository layout

Quality and scope

Tests cover important numerical, validation and trust boundaries, including a real MCP stdio discovery/call round trip. OpenAI agent tests use a fake client; they do not prove live model quality. CI runs tests, lint and dependency auditing across Python 3.11–3.13. See validation for the recorded local result and untested deployment boundaries.

This release prioritizes inspectable behavior over code volume. It does not claim to equal the references' line counts or broad feature sets. Scope and research explains the conceptual inspiration and remaining gaps. Source implementation was written independently; reference README descriptions informed the problem selection.

Read SECURITY.md before deployment. Contributions follow CONTRIBUTING.md and the community conduct policy. Licensed under MIT.