REPOGEO REPORT · LITE

mims-harvard/ToolUniverse

Default branch main · commit 08c54283 · scanned 5/20/2026, 1:42:03 PM

GitHub: 1,362 stars · 207 forks

Scan history for this repo

Score trend below includes all ready runs (older left, newer right; scroll horizontally if needed). The table is collapsed by default—expand for newest-first rows, 10 per page.

Score trend (left → right: older → newer)

2 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

AI VISIBILITY SCORE

40 /100

Critical

Category recall

0 / 2

Not recommended in any query

Rule findings

2 pass · 0 warn · 0 fail

Objective metadata checks

AI knows your name

3 / 3

Direct prompts that named your repo

HOW TO READ THIS REPORT

Action plan is what to do next — copy-pasteable changes prioritized by impact. Category visibility is the real GEO test: when a user asks an AI a brand-free question that should surface mims-harvard/ToolUniverse, does the AI actually recommend you — or your competitors? Objective checks verify the metadata signals AI engines weight first. Self-mention check detects whether AI even knows you exist by name.

Action plan — copy-paste fixes

3 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION

highabout#1

Update repository description to highlight evaluation focus

Why:

CURRENT

Democratizing AI scientists with ToolUniverse

COPY-PASTE FIX

A comprehensive framework for evaluating and benchmarking LLMs on multi-step, complex tool-use capabilities for scientific research.

highreadme#2

Reposition README's opening to emphasize benchmarking

Why:

CURRENT

# ToolUniverse: Democratizing AI scientists

COPY-PASTE FIX

# ToolUniverse: A Framework for Benchmarking LLM Tool-Use in Science

ToolUniverse provides a comprehensive framework for evaluating and benchmarking Large Language Models (LLMs) on their multi-step, complex tool-use capabilities, specifically tailored for scientific research and experimentation.

mediumtopics#3

Enhance topics with evaluation and benchmarking keywords

Why:

CURRENT

agents, ai-agents, ai-communication, ai-for-science, ai-scientists, automated-science, autonomous-agents, co-pilot, co-scientist, llms, lrm, mcp-servers, reasoning-agent, reasoning-language-models, scientific-skill, tool-use

COPY-PASTE FIX

llm-evaluation, llm-benchmarking, tool-use-evaluation, scientific-llms, llm-for-science, agents, ai-agents, ai-communication, ai-for-science, ai-scientists, automated-science, autonomous-agents, co-pilot, co-scientist, llms, lrm, mcp-servers, reasoning-agent, reasoning-language-models, scientific-skill, tool-use

Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

Category visibility — the real GEO test

Brand-free queries asked to google/gemini-2.5-flash. Did AI recommend you, or someone else?

Same questions for every model — switch tabs to compare answers and rankings.

Recall

0 / 2

0% of queries surface mims-harvard/ToolUniverse

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

LangChain

Recommended in 2 of 2 queries

COMPETITOR LEADERBOARD

LangChain · recommended 2×
LlamaIndex · recommended 2×
AutoGPT · recommended 1×
BabyAGI · recommended 1×
OpenAI API · recommended 1×

CATEGORY QUERY
How can I build autonomous AI agents for scientific research and experimentation?
you: not recommended
AI recommended (in order):
1. AutoGPT
2. BabyAGI
3. LangChain
4. LlamaIndex
5. OpenAI API
6. Hugging Face Transformers
7. Accelerate
8. ChemML
9. RDKit
10. PyTorch
11. TensorFlow
AI recommended 11 alternatives but never named mims-harvard/ToolUniverse. This is the gap to close.
Show full AI answer
CATEGORY QUERY
What framework helps LLMs reason and use external tools for complex problem-solving?
you: not recommended
AI recommended (in order):
1. LangChain
2. LlamaIndex
3. Microsoft Guidance
4. OpenAI Assistants API
5. Haystack
AI recommended 5 alternatives but never named mims-harvard/ToolUniverse. This is the gap to close.
Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

Metadata completeness
pass
README presence
pass

Self-mention check

Does AI even know your repo exists when asked about it directly?

Compared to common alternatives in this category, what is the core differentiator of mims-harvard/ToolUniverse?
pass
AI named mims-harvard/ToolUniverse explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
If a team adopts mims-harvard/ToolUniverse in production, what risks or prerequisites should they evaluate first?
pass
AI named mims-harvard/ToolUniverse explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
In one sentence, what problem does the repo mims-harvard/ToolUniverse solve, and who is the primary audience?
pass
AI named mims-harvard/ToolUniverse explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

Embed your GEO score

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MARKDOWN (README)

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HTML

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Pro

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Deep reports10 / month
Brand-free category queries5 vs 2 in Lite
Prioritized action items8 vs 3 in Lite