TPLCP Reference MCP

This local MCP server turns the 2026 CLCP orientation corpus into a searchable, citation-preserving reference source for TPLCP report work. It keeps source files local, preserves relative paths and SHA-256 identifiers, prioritizes current internal standards, and excludes credential-oriented files from search by default.

Data flow

1. Run tools/corpus_extract.py against the orientation folder. The extractor inventories every file, hashes exact duplicates, and writes files.jsonl plus readable text under texts/.

2. Run build_index.py to create tplcp_reference.sqlite3 in that extraction directory.

3. Set TPLCP_INDEX_DIR to the extraction directory and run server.py over MCP stdio.

The checked-in server is intentionally separate from the source corpus. Do not commit the extracted text cache or any source documents containing client records, credentials, or personal information.

Related MCP server: Zotero Chunk RAG

Tools

• tplcp_retrieve_for_question: preferred first step for a natural-language question. Returns a ranked evidence pack with controlling guidance first, examples second, and contextual references third.

• tplcp_search_reference: paged full-text search with role/module filters and source provenance.

• tplcp_get_reference_document: bounded, path- or SHA-256-addressed excerpts.

• tplcp_get_standards: highest-authority current TPLCP standards for a topic.

• tplcp_get_report_examples: illustrative report/template patterns.

• tplcp_list_modules: orientation module map.

• tplcp_reindex_reference: explicit local cache rebuild after a new extraction.

Skill suite

The skills/ directory contains the 15 operational TPLCP skills defined by the reporting playbook:

1. tplcp-reporting-orchestrator

2. tplcp-medical-review-summary

3. tplcp-life-care-plan

4. tplcp-medical-cost-projection

5. tplcp-report-writing-standards

6. tplcp-executive-summary

7. tplcp-clinical-recommendation-research

8. tplcp-standardized-assessment-interview

9. tplcp-coding-costing-methodology

10. tplcp-vendor-survey-atus

11. tplcp-life-expectancy

12. tplcp-rebuttal-analysis

13. tplcp-deposition-trial-prep

14. tplcp-report-qa

15. tplcp-privacy-source-control

Each skill has a required SKILL.md and UI metadata under agents/openai.yaml. The skills retrieve current guidance through the MCP and do not contain the private orientation corpus.

To install the skills into a local Codex skills directory:

mkdir -p "${CODEX_HOME:-$HOME/.codex}/skills"
cp -R skills/* "${CODEX_HOME:-$HOME/.codex}/skills/"

After installation, connect the MCP server, then invoke the orchestrator or a specialized skill by name. Keep the local corpus extraction and SQLite index outside the repository.

Question behavior

For a natural-language question, use tplcp_retrieve_for_question first. The tool makes the source hierarchy explicit:

1. Current internal TPLCP standards, workflow guidance, templates, and orientation instructions are the controlling source of truth.

2. TPLCP and external report examples show structure and patterns but do not override current guidance.

3. Clinical, costing, testimony, and educational materials provide context and require professional judgment.

The calling model should answer from the returned excerpts, preserve source paths and page numbers as citations, and identify when the corpus does not contain enough information to answer confidently. The tool retrieves evidence; it does not make medical, legal, or case-specific conclusions.

Example MCP configuration

{
  "mcpServers": {
    "tplcp-reference": {
      "command": "/absolute/path/to/.venv/bin/python",
      "args": ["/absolute/path/to/tplcp_reference_mcp/server.py"],
      "env": {
        "TPLCP_INDEX_DIR": "/absolute/path/to/analysis/clcp_corpus"
      }
    }
  }
}

Use TPLCP_INDEX_DB instead when the SQLite file is stored separately.

Local MCP client connection

The current server works locally over stdio. Configure a local MCP-capable client with:

{
  "mcpServers": {
    "tplcp-reference": {
      "command": "/absolute/path/to/python",
      "args": ["/absolute/path/to/tplcp-reference-mcp/server.py"],
      "env": {
        "TPLCP_INDEX_DIR": "/absolute/path/to/clcp_corpus"
      }
    }
  }
}

ChatGPT or hosted GPT connection

Hosted ChatGPT cannot reach a process listening only on this Mac. It needs an HTTPS MCP endpoint. The recommended path for this private source of truth is OpenAI's Secure MCP Tunnel:

tunnel-client init \
  --sample sample_mcp_stdio_local \
  --profile tplcp-reference \
  --tunnel-id <tunnel_id> \
  --mcp-command "env TPLCP_INDEX_DIR='/absolute/path/to/clcp_corpus' '/absolute/path/to/python' '/absolute/path/to/tplcp-reference-mcp/server.py'"

tunnel-client doctor --profile tplcp-reference --explain
tunnel-client run --profile tplcp-reference

Then create a developer-mode app in ChatGPT, choose Tunnel, select the tunnel, scan the tools, and test it in a new chat. Keep the app read-only by allowlisting the retrieval, standards, examples, document, and module tools. Do not expose this corpus through an unauthenticated public URL.

For a directly hosted deployment, run server.py --transport streamable-http and expose only the authenticated HTTPS /mcp endpoint. The server supports this transport, but the private tunnel is safer for source material containing company standards and case-related references.