Discover the Best AI Tools

Webotee MCP gives AI assistants access to collected Amazon seller, brand, niche and buy-box research, including historical observations. It is a hosted, paid-plan connector with OAuth and usage limits, not live shopping or account automation; findings need validation.

Marktplaats MCP searches Marktplaats.nl and 2dehands.be listings, compares prices and watches new ads. Hosted access is read-only; local logged-in sessions can message or bid after preview and confirmation, with bids binding. It uses unofficial undocumented endpoints that may change, and automated ad placement is deliberately unsupported.

amazon-in-mcp searches Amazon India listings, retrieves product details and ranks offers with a fixed value formula. It scrapes public HTML without paid APIs and can fail on bot checks or layout changes. Price history is a Keepa link, not fetched history; returned Amazon links carry a configurable affiliate tag.

A runnable research agent that plans searches, reads web sources, records evidence and produces cited reports with resumable checkpoints. Requires configured model access; citations do not guarantee factual correctness, and its web service lacks user authentication and permission isolation.

A point-cloud, mesh and Gaussian-splat viewer with MCP controls for AI agents to open scenes, adjust cameras and inspect screenshots. New headless capabilities require the development checkout and Chromium; large scenes remain memory intensive.

Zero-API-Key Web Search offers Python CLI and MCP search using DuckDuckGo by default, with optional self-hosted SearXNG or paid Bright Data. Optional local Laya models rerank results and classify stance, falling back to lexical methods. Laya is independent of hosted Jev; scores are not calibrated truth verification, and internet access and provider limits still apply.

CNKI Research Workflow Tools combines a Python SQLite research ledger, resumable screening and extraction records, citation conversion and CSV/XLSX export with browser workflow guidance. CNKI browsing requires legitimate login or institutional access and human handling of access challenges. Optional Jev screening is off by default and is not a verified substitute for reading papers.

Decision-Native Agent Runtime is a research implementation for paging and refining bounded model choices, managing context and validating tool execution. Its core CLI requires explicit plans and approval; it is not a finished autonomous agent and the snapshot has no final licensing policy.

Filter MCP Server exposes Bloom, counting Bloom, Cuckoo and simplified SuRF filters for membership, prefix and range experiments and comparisons. It is an educational research implementation, not a production-optimized filter engine or a full SuRF implementation.

Jev Search routes web queries and ranks Search1API links and snippets using Jev or Cloudflare Clef, with editable search filters rather than generated answers. It needs network access and configured search and decision-provider credentials. Relevance scores do not verify truth, and an installed PWA does not make searches work offline.

Python library for training neural networks on hydrological datasets using configurable models and training settings. Built on PyTorch and requires Python 3.10+. Users must prepare suitable data and configurations; it is a research library, not a ready-made warning service.

Research framework for global and regional Earth-system forecasting with training and inference scripts. Requires prepared datasets, checkpoints and the documented CUDA environment. The README lists coupled ocean-atmosphere code as not yet released.

Graph-learning library for materials models and interatomic potentials. Current releases use PyTorch Geometric and have removed DGL support. Requires Python 3.11+ and suitable model weights; optional acceleration depends on the hardware and model.

Materials-modeling package with pretrained interatomic potentials, an ASE calculator and training tools. Supports JAX and optional PyTorch backends; analytical Hessians are currently JAX-only. Requires Python 3.10+, appropriate model weights and scientific computing dependencies.

Research software for identifying viruses and plasmids in sequencing data, with viral taxonomy, provirus detection and protein annotation. Requires a separate reference database and computational dependencies. Its license restricts use to accredited academic institutions for internal non-commercial research and development; commercial use needs a separate license.

Research framework that modifies a coding agent and evaluates the resulting variants on coding benchmarks such as SWE-bench and Polyglot. Requires Docker, benchmark setup and model API credentials. It is an experimental research system, not a guarantee of reliable autonomous improvement.

Configurable research-agent application with CLI and web interfaces, tools, skills and persistent state. Requires a model provider; agents can edit files and run commands, and research outputs require independent checking.

Multi-agent research pipeline proposing, implementing and evaluating linear-attention architectures. Requires model configuration, database and retrieval services, plus experimental compute; published research results are not performance guarantees.

Framework for evolving programs against a scoring function and synthesizing systems from specifications. Optimization needs model access and synthesis needs a coding agent; outcomes depend on the evaluator, workload and validation.

Evolutionary coding library and CLI that generates and scores program variants using language models and a user-supplied evaluator. Requires model access and evaluation resources; better benchmark scores do not establish general correctness.

Python client and CLI for literature and graph-based research through the hosted SciAtlas API. Requires an API token; full review and idea workflows need the repository checkout, not just the client package. Hosted data is not bundled.

MCP access to DC Hub data-center, power-grid, fiber and capacity intelligence for site and market research. Access has anonymous/key-based quotas and source-specific freshness. Capacity-introduction tools can share company requirements with providers after terms acceptance; the service is not entirely read-only and does not guarantee available capacity.

Embeds headless PyMOL for molecular views and rendered images, with optional GROMACS/LAMMPS trajectory handling and clathrate-hydrate analysis. Requires an interpreter with pymol2, typically from conda; generic isolated launchers lack that dependency. Arbitrary-code passthrough is off by default, and the optional MDAnalysis bridge has a separate GPL license.

A hosted regulatory-research service with a local stdio bridge for FDA device evidence, recall and postmarket records, reimbursement references and NHTSA vehicle data. Anonymous quotas are measured in work units, with optional keyed allowances. Calls update quota/telemetry state; risk signals are research support, not regulatory advice or proof that a device is safe.