"OpenRouter thinking or reasoning capabilities" matching MCP servers:

Enables AI agents to query OpenRouter model information including prices, ELO rankings, context, and perform comparisons.

A Python MCP server that lets MCP hosts call OpenRouter models for chat, image generation, embeddings, and model search via FastMCP with .env support and retry handling.

Provides computational tools for systematic reasoning, including boolean evaluation, date arithmetic, object counting, state tracking, and format validation, to enhance agent capabilities and eliminate calculation errors in reasoning tasks.

Provides systems thinking models as composable analysis lenses for architecture, infrastructure, DevOps, incident analysis, and technical decision-making.

An enhanced sequential thinking tool optimized for programming tasks that helps break down complex coding problems into structured, self-auditing thought steps with branching and revision capabilities.

An MCP server for discovering and querying over 300 AI models available on OpenRouter. It enables users to list, search, filter, compare, and get detailed information about models with pricing, context limits, and capabilities.

An MCP server that provides access to 400+ AI models via OpenRouter, enabling chat, comparison, and document analysis through Claude.

Lets a Codex parent delegate bounded work to explicitly approved OpenRouter model/provider routes with strict permission controls, selected-file review, and approval-gated artifact creation.

Provides AI systems with structured thinking frameworks and reasoning tools to maintain consistent problem-solving patterns across conversations. Enables multi-step reasoning, decision analysis, and systematic troubleshooting through invocable mental models.

Enables coding agents to obtain probabilistic decisions from Jev AI via OpenRouter for classification, scoring, and validation, with tools like jev_check, jev_classify, jev_score, and jev_evaluate.

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.

Provides reasoning budget estimates for AI tasks, making effort and time trade-offs visible through a compact control interface.

A self-hosted MCP server that provides tools for structured reasoning, confidence calibration, and detecting recurring gaps in AI outputs, aiming to reduce workload by improving verification and attention allocation.

MCP server for deep algorithmic and mathematical reasoning, wrapping any OpenAI-compatible LLM endpoint to return only the final distilled answer without reasoning traces. Supports per-call model and endpoint overrides, defaulting to vibethinker-3b via LM Studio.

Structures debugging processes into a persistent knowledge graph, enabling problem decomposition, hypothesis testing, and reusable solution discovery.

A tool that implements Claude Shannon's problem-solving methodology to help break down complex problems into structured steps including problem definition, constraints, modeling, validation, and implementation.

MCP server for structured reasoning with cognitive trap detection, verification, and context compression

Enables AI agents to achieve production-ready solutions through iterative refinement and recursive thinking processes. It features token optimization via context compression and session-based tracking to improve problem-solving depth while minimizing cost.

Enables dynamic and reflective problem-solving through a structured thinking process with tools for sequential analysis, tree of thoughts, self-critique, and integration with external knowledge and codebase analysis.

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.