"Slow thinking, distributed thinking, and reasoning abilities research" matching MCP servers:

Enables declarative FDTD validation, guarded cloud-task workflows, and normalization of Tidy3D simulation results into metadata and bounded CSV observables, while keeping credentials and cloud-execution privileges isolated.

The server functions as an MCP server to interact with Notion for retrieving and creating survey data, integrating with the Claude Desktop Client for conducting and reviewing surveys.

MCP server that turns operator-authorized local or synthetic source records into auditable research briefs, with every finding linked to exact excerpts and hashes. Exposes a single build_research_brief tool over deterministic, no-network stdio.

Exposes a synthetic issue tracker and pipeline warehouse as callable tools so an agent can answer operational questions about tickets, pipeline health, runs, incidents, and governed metrics with every claim cited to the exact tool call it came from. All writes are proposal-only, requiring human approval through a gated apply path that logs each step for audit.

Enables users to convert a vendor partner directory (such as PartnerPage or the Shopify Partner Directory) or a trade body member list into one company row per listing, each carrying website, domain, tier, tags, and location for Clay-ready output. It runs the extraction on Apify, returning dataset rows for up to 20 directory URLs with optional deduping, detail enrichment, and a cost cap.

Enables local, offline ledger and replay operations for alpha research via five typed stdio tools, allowing status checks, report retrieval, method-card listing, replay execution, and run stopping without exposing live or submission capabilities.

Enables offline-first alpha research by providing a local stdio interface to query run status and reports, list approved method cards, execute replay runs, and stop runs, all without any submission capability.

Enables agent tools to validate queries, assess token costs, and extract structured datasets from unstructured text using multiple LLM providers.

This server facilitates scalable discovery and execution of OpenAPI endpoints using semantic search and high-performance processing, overcoming limitations of large spec handling for streamlined API interactions.

Exposes local DuckDB pizza sales data as MCP tools for querying store-level and total sales metrics via natural language, enabling local LLMs to perform real-time analytical queries.

Enables AI to interact with Microsoft Access (.mdb, .accdb) and SQLite 3 databases, including read-only support for Access 97 databases. Supports executing SQL queries, importing/export data from CSV and Excel files, and storing notes about database files.

Provides stock market data and computes technical indicators like EMA, MACD, and RSI across multiple timeframes using the LongPort API and TA-Lib. It enables users to perform detailed financial analysis and multi-timeframe market reviews through standard MCP tools.

Enables schema-aware exploration of JSON data by uploading samples, flattening nested structures, and using heuristic search with token overlap and fuzzy matching to find field paths for target names, accelerating ETL and API onboarding workflows.

Enables users to preprocess, analyze, and visualize CSV data through comprehensive tools for data manipulation, statistical analysis, and graph generation.

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

Lets AI assistants work on sensitive files by reasoning over schemas and masked output while deterministic local code handles raw data, never exposing real values to the model.

Enables parallel tagging and classification of CSV data using multiple LLM providers with structured output, confidence scores, and optional reasoning for batch classification tasks.

Enables LLMs to retrieve, analyze, and visualize stock prices and financial report data for quantitative trading research and investment analysis. Provides real-time and historical stock data, financial statement analysis, key metric calculations, and trading signal visualization.

An MCP server that enables advanced CSV analysis and data visualization using Google's Gemini AI and Plotly. It allows users to perform exploratory data analysis, generate interactive charts, and conduct complex reasoning on tabular data.

Enables assistants to reach a local market-research lab over MCP, including provenance-tagged market snapshots, falsifiable theses with invalidation rules, strategy backtesting with overfitting checks, committee reviews, portfolio analysis, and report export.

Enables agents to synchronize, merge, and reconcile distributed state across nodes using conflict-free last-write-wins element sets with Lamport/vector-clock causality, token-bucket gossip dissemination, and quorum-based commit coordination. It runs entirely on the Python standard library and exposes these primitives to Claude Desktop, Cursor, and Windsurf over the MCP JSON-RPC stdio protocol.

Exposes local data profiling, PII classification, and plan-based cleansing against DuckDB, Postgres, or SQLMesh tables through four tools (list_tables, profile, propose_cleansing_plan, apply_cleansing_plan). All reasoning stays aggregate-only — format signatures, counts, and match rates rather than raw cell values — and cleaning plans execute only after validation against a fixed operation vocabulary and explicit confirmation.

Enables AI agents to conduct cited equity research with independent committee views, reproducible backtests, and a locally stored, auditable track record through five MCP tools.

Enables AI assistants to query and explore distributed data across EdgeLake nodes through SQL operations, resource discovery, and schema inspection. Supports complex queries with joins, aggregations, and metadata fields across multiple databases and tables.

Enables a local MCP-compatible AI client to query saved World of Warcraft: Forever auction, character, profession, recipe, item-source, and map data from a read-only local dashboard. It works without an AI subscription and keeps all research data on your own machine.

A Model Calling Protocol server that enables LLMs to perform rigorous Bayesian analysis and probabilistic reasoning, including inference, model comparison, and predictive modeling with uncertainty quantification.

Enables market-data analysis and quantitative research over a local Parquet lake with tools for bars, indicators, scans, backtests, and safe SQL queries.

An MCP server that exposes Rucio distributed data management operations as tools for LLMs. Designed for ATLAS physicists working with grid data on analysis facilities, but usable with any Rucio instance.

Enables AI assistants to manage distributed computing jobs through the Joblet platform, supporting job orchestration, workflows, storage, networks, and monitoring.

Enables MCP clients to access A-share research data, run quantitative screening and Chan-theory/factor analysis, scan dragon leaders and sector rotation, and manage recommendation feedback with realistic entry-price validation.