Resources and next steps

Last updated on 2026-03-28 | Edit this page

Estimated time: 20 minutes

Overview

Questions

  • What other tools are available?
  • Where can I find help?

Objectives

  • List alternative AI coding tools.
  • Locate documentation and support.
  • Understand plugins and Model Context Protocol (MCP).

AI tool landscape

The ecosystem of AI tools for research is expanding. For coding, tools like Claude Code (Anthropic’s CLI), GitHub Copilot, and Aider (a CLI agent that works with multiple models) provide terminal support.

Some researchers use AI-native code editors like Cursor to interact with an entire repository. Models like Gemini 2.5 and DeepSeek-V3/R1 have increased the speed of these interactions.

Research-specific tools are also emerging. Elicit and Consensus focus on scientific paper discovery and evidence-based claims. Google’s NotebookLM allows you to ground an AI’s knowledge in your own collection of research PDFs for summarizing and querying documents.

Extending capabilities

AI tools can now connect with other software. Many browser-based models offer extensions for Google Drive, WolframAlpha, and other services. The Model Context Protocol (MCP) is an open standard that allows AI assistants to connect to local or remote data sources—such as a PostgreSQL database or your local file system—without requiring you to upload data to a central server. In December 2025, Anthropic donated MCP to the Agentic AI Foundation under the Linux Foundation. The 2026 MCP roadmap (published March 2026) focuses on enterprise authentication, audit trails, and agent-to-agent communication.

Caution

MCP security: shadow IT risk

MCP servers are installed by individual researchers or developers, often without institutional IT oversight. Security researchers have identified risks including prompt injection via malicious MCP servers and tools that silently exfiltrate data. Before connecting an MCP server, verify it is actively maintained and from a trusted source. Your institution’s IT policy may not yet cover MCP-enabled tools.

Local models and open tooling

For researchers handling sensitive data or requiring reproducibility, local AI is a growing trend. Running models on your own hardware ensures that data does not leave your machine.

  • Ollama: A tool for running open-weights models (like Llama 3, Mistral, or Gemma) locally on macOS, Linux, and Windows. It provides a CLI and a local API.
  • Aider: A CLI coding agent that connects to proprietary APIs and local models (via Ollama). It is designed for pair programming and refactoring in the terminal.
  • Qwen Coder: High-performance open models from Alibaba (e.g., Qwen2.5-Coder) that can be run offline for security.
Callout

Privacy and performance

Local models offer privacy but require significant hardware (especially GPU VRAM) to match the performance of cloud models like Gemini 2.5. Many researchers use a hybrid approach: cloud models for general scripting and local models for sensitive data.

Automated research discovery

For time-sensitive research, AI agents can scan social platforms, developer forums, and prediction markets to identify trends before they appear in traditional journals. This “real-time literature mapping” works by prompting the agent to synthesize signal from fast-moving sources alongside traditional ones.

Callout

A deep research prompt pattern

Use a prompt like this with any AI assistant that has web search access:

“Act as a research orchestrator. Find the most discussed [TOPIC] from the last 30 days across Reddit, X, and Hacker News.

  1. Discovery: Identify the top 3 tools or methodologies mentioned.
  2. Sentiment: Quote the most upvoted critique for each.
  3. Verification: Cross-reference these with web sources for real-world impact.
  4. Validation: Apply ‘Layer 4: Domain Plausibility’—identify one trend that sounds plausible but may be an AI-generated ‘vibe’ without empirical backing.”

Multi-model ensembles

In advanced workflows, you can use different models for different tasks. For example, one model generates code, another critiques it, and a third writes validation tests. This reduces the chance of a single model’s bias affecting results.

GitHub Agentic Workflows

GitHub Agentic Workflows (technical preview, February 2026) let you write repository automation goals in plain Markdown instead of YAML. GitHub Actions executes them using an LLM. This is a direct production application of the Markdown-as-spec pattern taught in this lesson.

Research-relevant use cases include automated issue triage, CI failure analysis, and pull request review. See the GitHub Agentic Workflows changelog for current status.

Provenance tracking

Include metadata in the header of AI-generated files to ensure reproducibility:

  • Model name and version.
  • Date and a link to the prompt used.
  • A hash of the context files provided.

Cost and efficiency

With long-context models, it is easy to include unnecessary files in prompts, which increases costs.

  • Monitor tokens: Check your API usage dashboard.
  • Optimize context: Only include the files needed for the current task.

Citing and crediting AI

Transparent attribution is essential for open science. Academic standards (including COPE, Nature, and Elsevier) state that AI tools cannot be listed as authors because they lack legal accountability. Instead, cite them as methodological tools.

1. In code repositories (README.md): Add an AI usage section to your project documentation:

Callout

Example attribution

  • Model: Google Gemini 2.5 Flash
  • Role: Spec drafting and spec-guided cleaning.
  • Verification: Verified by [Your Name] via GEMINI.md rules and validate_data.py.

2. In manuscripts: Cite the model in the methods or acknowledgements section.

  • Example: “We used Google Gemini 2.5 Flash to assist with data cleaning scripts. Prompts and raw outputs are available in the supplementary material.”

Resources and standards

Checklist

Summary checklist

Separating utility from marketing is a challenge. New tools appear daily, but many are more hype than substance.

Discussion

Finding tools

Where do you hear about new AI tools? Social media, academic journals, or word of mouth? Note that while social media is fast, reputable channels are more reliable.

Identifying hype

Before adopting a new tool, consider these points:

Checklist

Hype detection

Reputable sources

Follow sources that focus on the practical and ethical aspects of AI in research:

Callout

From vibes to evals

When AI scripts become critical for research, move to systematic evaluation. Create a “gold standard” dataset of known correct answers and test new iterations of AI-generated code against it.

Checklist
Challenge

Challenge: Your research protocol

Draft a plan for integrating these tools into your research workflow while maintaining rigor.

  1. Select a project: Which project would benefit most from an AI cleaning or validation pipeline?
  2. Choose your gates: Which approval gates (Test-first, Diff budget, Snapshot) will you use?
  3. Define requirements: What are 2-3 requirements for that project that the AI cannot change?
  4. Verification strategy: Will you use a second model, unit tests, or metamorphic checks?

A pre-defined protocol reduces decision fatigue during complex coding sessions. Deciding how to verify work early prevents approval fatigue later.

Key Points
  • Gemini, Claude, and Copilot serve different needs.
  • Community support is vital.
  • Plugins and MCP allow AI to connect to external data.