"Sequential Thinking Model Context Protocol (MCP) Server" matching MCP servers:

An MCP server that integrates AI retrievals with NASA's Common Metadata Repository (CMR), allowing users to search NASA's catalog of Earth science datasets through natural language queries.

A Python implementation of the Model Context Protocol (MCP) server that enables searching and extracting information from arXiv papers, designed to be extensible with additional MCP tools.

Provides a tool for dynamic and reflective problem-solving by breaking complex problems into manageable steps with support for revision, branching, and hypothesis generation.

Connects to wetlab protocol resources such as protocols.io, enabling users to access and manage experimental protocols via natural language.

An MCP server for navigating thought processes using branches, supporting thought cross-references and priority tracking to enhance insight generation and structured idea exploration.

Access to many public datasets right from your LLM application.

Enables complete Minto pyramid analysis through a 6-phase pipeline with sequential thinking, evidence gathering, and structured outputs.

Enables formal logical reasoning, mathematical problem-solving, and proof construction across 11 logic systems including propositional, predicate, modal, fuzzy, and probabilistic logic. Integrates external solvers (Z3, ProbLog, Clingo) for advanced reasoning, with support for proof storage, argument scoring, and cross-system translation.

Provides five rigorous reasoning protocols (debate, red team, audit_argument, threat_model, check_study) that run on the AI you're already using, requiring no extra API keys or costs.

An MCP server implementation that leverages Google's Gemini API to provide analytical problem-solving capabilities through sequential thinking steps without code generation.

Provides local, evidence-aware research memory with human review, revision history, and an inspector, allowing assistants to preserve source reports, inferences, assumptions, and evidence across sessions.

Enables AI agents to perform corpus analysis tasks such as discovery, search, and data access via the Model Context Protocol.

Provides source-grounded Christian research material with exact passages, provenance, and stable citations via a REST API.

Enables running the same prompt(s) across roughly 300 models from Anthropic, OpenAI, Google, Meta, Mistral, DeepSeek, Qwen and others, then comparing per-model outputs, latency, token usage, error rates and billed cost side by side. Supports free cost estimates before launching, asynchronous run creation with status polling and cancellation, prepaid x402 credit top-ups, and an optional AI-written comparison summary of the results.

Provides access to ancient text corpora via stdio, enabling corpus listing, status checks, searching, and text/metadata retrieval with provenance envelopes.

Compress OCR-heavy PDFs into dense packed images so agents can work with long visual documents.

Provides provenance-aware access to selected Sefaria Bible context, including Targumim, Mishnah, Jewish commentary, Josephus/Philo, and lexicon lookups, with on-demand caching of long-tail Sefaria texts.

A local stdio MCP server that gives Claude on-demand access to any Kaggle competition's brief, rules, evaluation, data description, leaderboard, top discussions and public notebooks, using the user's own Kaggle credentials and a disk cache with no hosted infrastructure. It also exposes a live tool for spotting forum topics and comments posted since the last fetch.

Provides AI assistants with structured, authoritative knowledge of the HICAR atmospheric model, including namelist options, physics schemes, output variables, documentation, and source code.

A comprehensive Model Context Protocol (MCP) server providing advanced access to the ChEMBL chemical database.