"How to check console logs and network activity" matching MCP servers:

Enables structural framing analysis of documents, identifying perspectives, omissions, and reader positioning, with numerical claim verification against authoritative sources.

Enables verifying citations in reference lists and BibTeX by detecting fabricated or mismatched references and retractions, and returning corrected BibTeX.

Scans markdown vaults to detect contradictory claims (quantitative and negation) between notes with high concept overlap, and provides tools for analysis and reconciliation.

Enables deliverable conformance and evidence-chain checks by turning requirements into executable specs, validating numbers against a provenance ledger, and auditing PDF/OOXML metadata and archive manifests.

Checks whether a trading backtest survives its own statistics: deflated Sharpe, multiple-testing correction against a best-of-N-noise benchmark, minimum track record length, and fill realism. Takes no market data and no API keys, and cannot recommend a trade — it only reports that a result is weaker than claimed or not yet provable.

Verifies claims with verdicts (supported/disputed/unverifiable), confidence scores, and cited sources by cross-referencing FoundryNet Data Network and web search.

Transforms raw Google Search Console signals into actionable marketing insights, such as detecting cannibalization, segmenting search intent, and identifying citation opportunities, for any MCP-compliant AI agent.

Enables evaluation of digital marketplaces by classifying demand signals, scoring commercial viability, sanity-checking claims, and comparing candidate channels.

Open corporate carbon emissions data for AI agents. Search companies and retrieve Scope 1, 2 and 3 disclosure history, Mycelium and Transparency scores, and per-scope figures with reported-vs-estimated provenance on every line.

Converts PDF files to text for use with MCP-compatible applications like Cursor IDE.

Multi-dimensional data quality validation and statistical anomaly detection for LLM training data, with auto-fix pipeline and MCP tools for AI IDE integration.

Enables analysis of academic author networks and research collaborations by retrieving co-authors and research keywords from sources like Semantic Scholar, OpenAlex, Crossref, and Google Scholar.

CNN's Fear & Greed index for the US stock market: current score and rating, the seven component indicators, and about a year of daily history. Go standard library only, no API key required.

Deterministic US equity-compensation tax optimizer. Six tools cover ISO/AMT exercise scheduling, NSO sell-vs-hold, RSU vest-and-sell, single-stock concentration, protective put / zero-cost collar pricing, and Section 1202 QSBS qualification. Federal plus 50-state plus DC tax math.

Institutional-grade quantitative stock analysis and research signals for AI agents via the Model Context Protocol (MCP).

One-call Australian health and welfare data plumbing via AIHW — cited responses for practical analysis, not a data broker.

An MCP-native pipeline for collecting structured intelligence on higher education institutions using the WHED schema, enabling scraping, extraction, validation, and saving of profiles.

This project implements a Model Context Protocol (MCP) server providing Formula One racing data using the Python FastF1 library. Inspired by an existing TypeScript server, it offers similar F1 data functionalities natively in Python via FastF1.

Enables Claude Code to convert PDF files to high-quality PNG images, download academic papers, and batch process PDFs with automatic folder organization.

MCP server that enables AI assistants to drive systematic literature review workflows, including screening, downloading, converting, and reviewing papers through natural language conversation.