# Building an MCP Server That Verifies Its Sources: Inside footnote-mcp

> Source: <https://dev.to/kazkozdev/building-an-mcp-server-that-verifies-its-sources-inside-footnote-mcp-v2-1lj5>
> Published: 2026-07-18 06:15:28+00:00

footnote-mcp is a Python MCP server installable via pip, Docker, or pipx. No API keys required — it falls back to scraped Bing + DuckDuckGo search and automatic headless Chromium for JavaScript-heavy pages.

The core tool is `evidence_entailment`

. It takes a claim and a source text, and returns whether the claim is supported, unsupported, or contradicted. The heuristic backend extracts numeric and named-entity tokens from both the claim and source, then checks for exact matches and contradictions. On its design domain — numeric and factual data claims — it achieves 100% accuracy on a labeled benchmark set.

For semantic cases (negation, paraphrase), the `ollama`

backend uses a local LLM as a judge.

Three tools build on this: `corroborate_claim`

triangulates a claim across multiple sources, `locate_claim_span`

finds the exact supporting sentence with character offsets, and `build_research_debug_report`

produces a compact report of queries, URLs, and verification gaps.

`web_read`

fetches pages through a 5-tier escalation ladder: HTTP (curl_cffi) to rotating proxy to headless Chromium to Chromium through proxy to hosted scrape API (Firecrawl/ScrapingBee). A block/quality detector decides when to escalate, and per-domain rate limiting, circuit breakers, and negative cache keep it polite.

`web_search`

supports Tavily, Brave, Google, or scraped Bing + DuckDuckGo as fallback. Pass `semantic: true`

to reorder results by meaning using local Ollama embeddings.

Beyond text, the server handles tables, CSV/XLSX/PDF/JSON, date validation, unit resolution, and time series reconciliation. For JavaScript-heavy pages, 10 browser tools let you drive a headless Chromium session. When generic parsers fail, the server can synthesize sandboxed extraction code through a controlled recipe system.

The heuristic backend achieves 100% accuracy on numeric and factual data claims (n=15). Overall accuracy including semantic cases is 83% (n=18). The blind spot is purely semantic negation and paraphrase — the `ollama`

backend closes that gap.

```
pip install footnote-mcp
python -m playwright install chromium
footnote-mcp
```

Then add to your MCP client config and start researching. Full source at [github.com/KazKozDev/footnote-mcp](https://github.com/KazKozDev/footnote-mcp).

Feedback welcome — especially on the verification approach. The benchmark is designed to be extended, so if you have a claim/source pair that should be caught, open an issue or PR.