AI-Tool Interaction Protocol¶
The foundation that makes ToolUniverse’s 600+ tools universally accessible to any AI system
Overview¶
Just as HTTP standardizes client-server communication across the web, ToolUniverse implements an AI-Tool Interaction Protocol that standardizes how AI models interact with scientific tools. This protocol abstracts away the complexity of 600+ heterogeneous tools (machine learning models, databases, APIs, robotics systems) behind a unified interface.
┌─────────────────┐
│ AI Model │ ← Your LLM, Agent, or Reasoning Model
│ (GPT, Claude, │
│ Gemini, etc.) │
└─────────┬───────┘
│ AI-Tool Interaction Protocol
│
┌─────────▼───────┐
│ ToolUniverse │ ← Protocol Implementation
│ Ecosystem │
└─────────┬───────┘
│
┌─────────▼───────┐
│ 600+ Tools │ ← ML Models, APIs, Databases, etc.
│ (Heterogeneous │
│ Backends) │
└─────────────────┘
Protocol Components¶
The AI-Tool Interaction Protocol comprises three essential elements:
Specification Schema - How tools describe themselves
Interaction Schema - How to request tool execution
Communication Methods - Local Python and remote MCP
1. Tool Specification Schema¶
Every tool in ToolUniverse exposes itself through a standardized specification:
{
"name": "UniProt_get_entry_by_accession",
"description": "Get complete JSON entry for a UniProtKB accession",
"parameters": {
"type": "object",
"properties": {
"accession": {
"type": "string",
"description": "UniProtKB accession (e.g., P05067)",
"required": true
}
}
},
"return_schema": {
"type": "object",
"description": "Complete UniProtKB entry with protein information"
}
}
Schema Components:
Name: Unique tool identifier following Database_action_description format
Description: Clear explanation of tool’s purpose and functionality
Parameters: Detailed argument specifications with types and requirements
Return Schema: Structure of expected output data
2. Interaction Schema¶
All tool interactions follow a uniform request format:
{
"name": "Tool_identifier",
"arguments": {
"parameter1": "value1",
"parameter2": "value2"
}
}
Example Requests:
# Protein information request
{
"name": "UniProt_get_entry_by_accession",
"arguments": {
"accession": "P05067"
}
}
# Drug safety analysis request
{
"name": "FAERS_count_reactions_by_drug_event",
"arguments": {
"medicinalproduct": "aspirin"
}
}
# ML model prediction request
{
"name": "boltz2_docking",
"arguments": {
"protein_id": "1ABC",
"ligand_smiles": "CCO"
}
}
3. Communication Methods¶
Local Communication (Python):
from tooluniverse import ToolUniverse
tu = ToolUniverse()
tu.load_tools()
# Direct tool execution
result = tu.run({
"name": "OpenTargets_get_associated_targets_by_disease_efoId",
"arguments": {"efoId": "EFO_0000249"}
})
Remote Communication (MCP):
# Start MCP server
tooluniverse-smcp --port 8000
# AI assistants connect via MCP
# Tools become available in Claude, ChatGPT, etc.
Core Operations¶
The protocol defines two fundamental operations that AI systems use to interact with ToolUniverse:
Find Tool Operation¶
Purpose: Discover relevant tools based on natural language descriptions
Input: Natural language query describing desired functionality
Output: List of relevant tool specifications
# How AI models discover tools
query = "predict protein binding affinity"
# Protocol returns relevant tools:
tools_found = [
"boltz2_docking",
"ADMETAI_predict_properties",
"ChEMBL_search_similar_molecules"
]
Implementation Methods:
Keyword Search: Fast TF-IDF matching with morphological processing
LLM-based Search: Contextual reasoning for complex queries
Embedding Search: Semantic similarity using fine-tuned models
Call Tool Operation¶
Purpose: Execute a selected tool with specified arguments
Input: Tool name and structured arguments
Output: Structured results from tool execution
# Standardized execution across all tool types
request = {
"name": "boltz2_docking",
"arguments": {
"protein_id": "P05067",
"ligand_smiles": "CC(=O)OC1=CC=CC=C1C(=O)O"
}
}
result = tu.run(request)
# Consistent result structure
{
"binding_affinity": -8.2,
"binding_probability": 0.85,
"confidence_score": 0.92,
"metadata": {
"model_version": "boltz-2",
"execution_time": "2.3s"
}
}
Tool Types & Backend Abstraction¶
The protocol successfully abstracts diverse tool backends:
Machine Learning Models¶
# Protein structure prediction
{
"name": "boltz2_docking",
"arguments": {"protein_id": "1ABC", "ligand_smiles": "CCO"}
}
# ADMET property prediction
{
"name": "ADMETAI_predict_admet_properties",
"arguments": {"smiles": "CCO", "properties": ["BBB_penetrance"]}
}
Database APIs¶
# GraphQL database (OpenTargets)
{
"name": "OpenTargets_get_associated_targets_by_disease_efoId",
"arguments": {"efoId": "EFO_0000249"}
}
# REST API (UniProt)
{
"name": "UniProt_get_entry_by_accession",
"arguments": {"accession": "P05067"}
}
Scientific Software Packages¶
# Bioinformatics tools
{
"name": "get_biopython_info",
"arguments": {"package": "Bio.SeqIO"}
}
# Analysis packages
{
"name": "Enrichr_analyze_gene_list",
"arguments": {"genes": ["BRCA1", "BRCA2"], "library": "KEGG_2021_Human"}
}
AI Agents & Tools¶
# Literature review agent
{
"name": "conduct_literature_review_and_summarize",
"arguments": {"topic": "HMG-CoA reductase inhibitors"}
}
# Hypothesis generation
{
"name": "HypothesisGenerator",
"arguments": {"context": "Alzheimer's disease treatment"}
}
Error Handling & Validation¶
The protocol includes robust error handling:
Input Validation¶
# Automatic parameter validation
request = {
"name": "UniProt_get_entry_by_accession",
"arguments": {
"accession": "INVALID_ID" # Invalid format
}
}
# Protocol returns structured error
{
"status": "error",
"error_type": "ValidationError",
"message": "Invalid UniProt accession format",
"details": {
"parameter": "accession",
"expected_format": "P12345 or Q9Y261",
"received": "INVALID_ID"
}
}
Protocol Extensions¶
Tool Composition¶
The protocol supports chaining tools for complex workflows:
# Composed workflow example
workflow = {
"name": "drug_discovery_pipeline",
"arguments": {
"disease": "hypercholesterolemia",
"steps": [
"target_identification",
"compound_screening",
"ADMET_prediction",
"patent_analysis"
]
}
}
Human-in-the-Loop¶
Expert feedback integration through the protocol:
# Request human expert consultation
{
"name": "consult_human_expert",
"arguments": {
"question": "Which HMG-CoA reductase inhibitor shows best safety profile?",
"context": {"compounds": ["lovastatin", "pravastatin", "simvastatin"]},
"expertise_required": "pharmacology"
}
}
Remote Tool Integration¶
MCP-based remote tool registration:
# Register external MCP server tools
{
"name": "register_mcp_tools",
"arguments": {
"server_url": "mcp://expert-system.company.com:8080",
"tool_categories": ["proprietary_ml_models", "private_databases"]
}
}
Protocol Benefits¶
For AI Developers: - Single interface for 600+ diverse tools - No tool-specific integration work required - Automatic error handling and validation - Consistent response formats
For Scientists: - Focus on research logic, not technical implementation - Reproducible workflows across different tools - Easy tool discovery and experimentation - Human expertise integration when needed
For Tool Creators: - Standardized way to expose functionality - Automatic AI compatibility - Built-in documentation and validation - Community discoverability
Implementation Details¶
The protocol is implemented through ToolUniverse’s core components:
Tool Registry: Maps tool names to implementation classes
Tool Caller: Handles protocol parsing and execution
Tool Finder: Implements the three discovery strategies
Tool Manager: Manages local and remote tool registration
Validation Engine: Ensures request/response compliance
Tip
The protocol makes ToolUniverse unique: While other frameworks provide tool orchestration, ToolUniverse creates a universal language for AI-tool interaction, similar to how HTTP enabled the modern web.