"An overview of knowledge graphs" matching MCP servers:

An automated system that processes raw text through a three-stage pipeline to assess data quality, enhance information, and generate structured knowledge graphs. It provides tools for triple extraction with confidence scoring and creates interactive HTML visualizations of the resulting graph.

Enables corpus-grounded research over Lacanian seminar and écrits texts by retrieving passages, building evidence contracts, and returning only answers whose claims survive citation and entailment validation — abstaining instead of answering from model knowledge. It also exposes the corpus for direct reading and lets users audit every claim back through answer → claim → passage → session → seminar → witness/source.

A scientific reasoning framework that leverages graph structures and the Model Context Protocol (MCP) to process complex scientific queries through an Advanced Scientific Reasoning Graph-of-Thoughts (ASR-GoT) approach.

Read-only MCP server for the July 2026 survey of AI in open-source design systems, enabling agents to query 19 systems' affordances, coercion techniques, and platform data via 9 tools, 2 resources, and 2 prompts.

Enables fetching Google's AI Overview answers and cited sources as structured JSON, with support for batch queries and country/language targeting.

Provides tools for managing qualitative research knowledge graphs, enabling structured representation of research projects, participants, interviews, observations, codes, themes, and findings.

Provides tools for managing quantitative research knowledge graphs, enabling structured representation of research projects, datasets, variables, hypotheses, statistical tests, models, and results.

Real human judgment as agent tools -- an AI agent can ask a question and get back a structured, schema-validated JSON answer from a real quality-scored human. 16 response types (yes/no, ratings, rankings, A/B tests, sentiment, image/video/audio review, voice/video/photo capture). Fully programmatic signup with a $5 free trial credit, no card required.

Enables grounded, dated research from a curated AI-knowledge repository, offering free BM25 retrieval and a research loop that combines the knowledge base, curated sources, and the open web with tiered citations.

Per-unit cost of 13 back-office tasks done by a person vs by software, in the US, Greece and Ukraine, with an official statistic behind every hourly wage. Bundled data, works offline.

Enables AI clients to analyze any company domain and determine its AI maturity tier (commercialized, deployed, declared, or none) with supporting evidence, via a single tool backed by an Apify actor.

Bilingual (EN/ES) AI-writing detection that shows the evidence instead of a percentage: named tells with line and column, hidden-character inspection, and citation cross-checking against a document's own bibliography. Seven of its nine tools run entirely locally and never touch the network.

Search and collect data from the National Assembly Library of Korea's OpenAPI (21 DB types) and export to xlsx/csv/json/sqlite.

Exposes NCBI PubMed as MCP tools for searching literature, fetching abstracts, exploring citation graphs, and finding author publications without requiring an API key.

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

Enables LLM agents to query and analyze hh.ru job vacancies via full-text and semantic search, with tools for vacancy lookup, skill statistics, market overview, and candidate profile comparison.

A read-only FastMCP server for academic research that lets agents search papers, inspect metadata, follow citation graphs, generate BibTeX, and read full texts of open-access PDFs via semantic scholar, OpenAlex, arXiv, DBLP, and Crossref.

Enables searching and reading of AI/ML papers from conference proceedings and arxiv, with tools for full-text search, table of contents, and content grep.

MCP server for the DAG Studio causal-inference engine, enabling AI agents to construct, analyze, and validate causal directed acyclic graphs (DAGs) through tools for analysis, data simulation, and code generation.

Provides access to 9 AI executives offering board-ready business intelligence, competitor analysis, trend monitoring, and more via agent-payable endpoints.