"OpenRouter thinking or reasoning capabilities" matching MCP servers:

Enables AI assistants to consult other AI models via OpenRouter for help with coding tasks.

An MCP server that enhances sequential thinking with meta-cognitive capabilities including confidence tracking, hypothesis testing, and organized memory storage through graph-based libraries and structured JSON documents.

An MCP server that structures AI reasoning as directed acyclic graphs of semantic thoughts, enabling explicit dependencies, assumption tracking, and cascade invalidation for transparent decision-making.

Enables AI assistants like Claude or ChatGPT to query OpenRouter's 300+ models for pricing, context length, capabilities, and rankings through tools such as model-card, compare-models, cheapest-for, cost-estimator, and usage-rankings, using a public, key-less API with 6-hour caching.

Enables AI agents to delegate tasks to cheaper models via the OpenRouter API, with live pricing, cost-tier auto-selection, and policy-based delegation.

Enables AI agents to browse, inspect, and compare LLMs from OpenRouter's catalog with normalized per-million-token pricing, context windows, and modalities. Supports filtering by search, free-only status, minimum context, or modality.

Provides seamless access to 200+ AI models through OpenRouter's unified API, featuring multi-model collaboration, vision support, intelligent benchmarking, and collective intelligence capabilities for enhanced decision-making.

Remote MCP server for dynamic and reflective problem-solving through structured thinking. Enables step-by-step reasoning with branching, revision tracking, and session management.

Provides chat and image analysis capabilities through OpenRouter.ai's diverse model ecosystem, enabling both text conversations and powerful multimodal image processing with various AI models.

An MCP server that enables managing multiple lines of thought with features like branch navigation, cross-references between related thoughts, and insight generation from key points.

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.

Facilitates structured creative thinking through sequential thought processing, helping users shift from reactive problem-solving to proactive outcome creation using structural tension analysis and stage-based thinking workflows.

Facilitates structured, progressive thinking through defined cognitive stages (Problem Definition, Research, Analysis, Synthesis, Conclusion), enabling users to break down complex problems, track thought progression, and generate summaries of their thinking process.

Enables AI agents to perform dynamic and reflective problem-solving through a chain of thoughts, allowing them to break down complex problems, revise past thoughts, and explore logic branches before reaching a conclusion.

Provides a sequentialthinking tool for dynamic, reflective problem-solving via chain-of-thought reasoning. Supports local Stdio and remote SSE deployment on Google Cloud Run.

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

Enables AI assistants to work through complex problems step-by-step with dynamic thought processes, allowing for revision of previous steps, exploration of alternative approaches, and flexible planning as understanding deepens.

Enables structured, step-by-step problem-solving with dynamic revision and branching capabilities. Supports breaking down complex problems into manageable steps while allowing course corrections and alternative reasoning paths.

A deterministic verification gate for MCP clients that independently checks model outputs against evidence, contradictions, calibration, and provenance without relying on LLM self-assessment.

Guides problem-solving by breaking down complex problems into steps and recommending appropriate MCP tools for each stage, with confidence scores and rationales for tool suggestions.