HITL Navigator — MCP Server V1.0 eSTARHelper · Build 30-OC · 2026-10-15
Mode 5 of 5
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What this gives you: your agentic Ai (Claude Desktop, Cursor, Windsurf, custom MCP Client) gains a
deterministic verification tool. Prompt + Ai response + reference material go in;
BM25 page rank, proximity ladder, and cited-quote check come back.
No LLM in the verification layer. No verdict rendered. A named human is accountable at policy time or audit time.
1. Install into Claude Desktop
- Open your
claude_desktop_config.json. On macOS: ~/Library/Application Support/Claude/claude_desktop_config.json. On Windows: %APPDATA%\Claude\claude_desktop_config.json.
- Add the
hitl-navigator block below inside the top-level "mcpServers" object. Replace REPLACE-WITH-ABSOLUTE-PATH-TO/server with the absolute path to your local server/ folder.
- Restart Claude Desktop.
- In a new conversation ask: "What tools do you have from hitl-navigator?" — the three tools should list.
{
"mcpServers": {
"hitl-navigator": {
"command": "python",
"args": ["-m", "mcp_server.hitl_navigator_mcp"],
"cwd": "REPLACE-WITH-ABSOLUTE-PATH-TO/server"
}
}
}
2. The three tools your agent gets
verify_ai_response(prompt, ai_response, reference_pages, terms?, n_window?, stem_variant?)
The main verification pack. Returns bm25, proximity_ladder, terms_used, and a disclaimer reminding the agent that this is evidence, not a verdict.
check_cited_quote(reference_pages, quote)
Deterministic verbatim lookup. Returns {found, page, note}. Use it when the Ai claims to have quoted the source verbatim.
about()
Server identity + stance. Point your agent here first if you want it to know what HITL Navigator is and refuses to do.
3. Cursor / Windsurf / other MCP Clients
Any MCP Client that reads a mcp.json or equivalent works with the same block above. Consult your client's docs for the exact config path.
4. Standalone dev run
cd server
python -m mcp_server.hitl_navigator_mcp
Runs over stdio. Use the MCP Inspector (npx @modelcontextprotocol/inspector python -m mcp_server.hitl_navigator_mcp) to interact from a UI.
5. Why an agent should call us
- Zero Ai in the verification layer. BM25 + proximity are pure functions. Same inputs, same output, forever — agents get reproducibility their own LLM can't provide.
- No verdict rendered. Your agent stays in charge of what to do with the evidence — act, escalate, or ignore under the policy its operator set.
- Human accountability preserved. Whether the human signs at policy time (thresholds authored once) or at audit time (log review), the trail is named. That is what regulators accept.
- Model-agnostic. Works with any Ai output the agent passes in. ChatGPT, Claude, Gemini, Grok, Perplexity, Llama — all the same on our side.