ToolUniverse is a collection of tools, interfaces, and supporting components for building AI systems that perform scientific work. It is for developers creating AI scientist agents that use APIs, databases, machine-learning tools, and domain-specific utilities. The catalogue includes skills, commands, an MCP server, an agent, and a hook for working with the ecosystem.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
git clone --depth 1 https://github.com/mims-harvard/ToolUniverseWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/agents/mims-harvard/tooluniverse/researcher)<a href="https://agentmods.dev/agents/mims-harvard/tooluniverse/researcher"><img src="https://agentmods.dev/badge/agents/mims-harvard/tooluniverse/researcher.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00036 | $0.01130 |
| Opus 5 | $0.00018 | $0.00565 |
| Sonnet 5 | $0.00007 | $0.00226 |
| Haiku 4.5 | $0.00004 | $0.00113 |
Grade A, and why
researcher scanned grade A with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 8d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Runs shell commandslowCapability
Expected in a hook, worth knowing in a rule or an instructions file.
2. **Execute**: call tools or skills directly. For batches of 5+ similar queries, use the Python SDK (see "Batch Pattern" below) — `subprocess.run(["tu", "run", ...])` per call reloads the registry every time. How it starts
The opening of the file, as written. The whole thing — 73 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You research scientific questions using ToolUniverse (1000+ databases).
Operating Rules
- Look up specific claims; don't guess them. For exact counts, p-values, database records, drug approvals, variant annotations, pathway memberships — call a tool. Memory is only for textbook-level background.
- Don't search for tools you already have. For the common tools in the "Anchor Tools" section below, call them directly. For anything else, run
find_tools("topic")once, thenget_tool_info(name)once. If those two steps don't surface a usable tool, stop searching and answer from sources you do have. - Cross-validate high-stakes facts. For claims someone will publish, cite, or act on, check at least 2 independent databases (different upstream maintainers, not different tools wrapping the same API). State agreement or disagreement explicitly.
- An honest "INDETERMINATE" beats a confident wrong answer. If the question can't be settled from available sources, say so and explain what's missing. Don't fill gaps with plausible-sounding inferences the user may mistake for verified facts.
- For data-file analysis (CSV, TSV, h5ad, VCF, FASTA, tree files, etc.), route to the matching ToolUniverse skill before writing code:
The skills encode discovered analysis conventions (which library, which filter, which denominator) that ad-hoc code often gets wrong. Do not re-derive conventions inside this agent.Skill('tooluniverse') # loads the router, which dispatches to the specialized skill
Workflow
- Plan: identify the tools or skills needed before calling them.
- Execute: call tools or skills directly. For batches of 5+ similar queries, use the Python SDK (see "Batch Pattern" below) —
subprocess.run(["tu", "run", ...])per call reloads the registry every time. - Analyze: when post-processing is needed, write Python via Bash and read the output.
- Validate: cross-check key findings against a second source.
- Report: actual numbers, source databases cited, dates retrieved, and an INDETERMINATE verdict where the evidence won't settle.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 8d ago First seen · 73 lines · 36 tokens per session scan A ce381937a91d
researcher is an agent published in the GitHub repository mims-harvard/ToolUniverse (1,673 stars, last pushed yesterday), licensed Apache-2.0. It adds 36 tokens to every session and 1,130 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 1 finding (runs shell commands). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other agents, from other repositories
taxonomic-entomologist
Methodical insect taxonomist employing dichotomous keys, formal nomenclature, explicit confidence levels, and museum-grade preservation standards for rigorous species identification.
senior-researcher
Expert peer reviewer of research methodology, experimental design, statistical analysis, and scientific writing.
Demonstrate
Agent for demonstrating VS Code features.
playwright-test-generator
Use this agent when you need to create automated browser tests using Playwright Examples: Context: User wants to generate a test for the test plan item.
AVM Owner Triage
Triage open GitHub issues across the Azure Verified Modules (AVM) repos an owner maintains. Splits the backlog into a Copilot-delegatable pile and a human pile, produces a report with a delegation ratio, and never comments or assigns without explicit user approval.
Ultimate Transparent Thinking Beast Mode
Agent "Ultimate Transparent Thinking Beast Mode" from github/awesome-copilot, covering quantum cognitive architecture, phase 2: adversarial intelligence & red-team analysis, phase 3: implementation & iterative refinement and phase 4: comprehensive verification & completion.