How do I use T5Chem MCP Server?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@T5Chem MCP Server Predict retrosynthesis for CCO." That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

T5Chem MCP Server

by bugatti742

Overview Schema Source Related Servers Score Discussions Admin

Python

Local

T5Chem

License: MIT Python 3.10+

A Unified Deep Learning Model for Multi-task Reaction Predictions with MCP (Model Context Protocol) support.

Features

Retrosynthesis Prediction: Predict reactants from a product molecule
Product Prediction: Predict products from reactants and reagents
Reagents Prediction: Predict required reagents for a reaction
Molecule Validation: Validate SMILES strings
Molecular Properties: Calculate detailed molecular properties
MCP Server: Integrate with AI assistants through Model Context Protocol

Related MCP server: MIST - Model Intelligence System for Tasks

Installation

# Clone the repository
git clone https://github.com/bugatti742/t5chem.git
cd t5chem

# Install with MCP support
pip install -e ".[mcp]"

# Or install all dependencies
pip install -e .

Download Pre-trained Model

Large model files are NOT included in the repository. Download them separately:

# Download USPTO multi-task model
wget https://yzhang.hpc.nyu.edu/T5Chem/models/USPTO_MT_model.tar.bz2
tar -xjvf USPTO_MT_model.tar.bz2 -C model/

Usage

As MCP Server

Start the MCP server:

# Using default model path (model/)
t5chem-mcp

# Specify custom model path
t5chem-mcp --model_dir /path/to/your/model

Available MCP Tools

predict_retrosynthesis: Predict retrosynthesis routes
predict_product: Predict product from reactants
predict_reagents: Predict reagents for a reaction
validate_molecule: Validate SMILES strings
get_molecule_properties: Get molecular properties

Command Line

# Batch prediction
t5chem predict --data_dir data/sample/reactants/ --model_dir model/

# Training
t5chem train --data_dir data/sample/reactants/ --output_dir model/ --task_type reactants

Requirements

Python 3.10+
PyTorch 2.2+
Transformers 4.38+
RDKit 2022.9+
MCP SDK 1.0+

Citation

Jieyu Lu and Yingkai Zhang, J Chem Inf Model, 62, 1376 - 1387 (2022)

License

MIT License

Available Tools

5 tools

get_molecule_propertiesD

ParametersJSON Schema

Name	Required	Description	Default
`smiles`	Yes

TDQS

D1/5.0

Behavior1/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Tool has no description.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness1/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Tool has no description.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness1/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Tool has no description.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters1/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Tool has no description.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose1/5

Does the description clearly state what the tool does and how it differs from similar tools?

Tool has no description.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines1/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Tool has no description.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

predict_productD

ParametersJSON Schema

Name	Required	Description	Default
`model_dir`	No
`num_beams`	No
`num_preds`	No
`reagents_smiles`	No
`reactants_smiles`	Yes

TDQS

D1/5.0

Behavior1/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Tool has no description.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness1/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Tool has no description.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness1/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Tool has no description.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters1/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Tool has no description.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose1/5

Does the description clearly state what the tool does and how it differs from similar tools?

Tool has no description.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines1/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Tool has no description.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

predict_reagentsD

ParametersJSON Schema

Name	Required	Description	Default
`model_dir`	No
`num_beams`	No
`num_preds`	No
`product_smiles`	Yes
`reactants_smiles`	Yes

TDQS

D1/5.0

Behavior1/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Tool has no description.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness1/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Tool has no description.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness1/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Tool has no description.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters1/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Tool has no description.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose1/5

Does the description clearly state what the tool does and how it differs from similar tools?

Tool has no description.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines1/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Tool has no description.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

predict_retrosynthesisD

ParametersJSON Schema

Name	Required	Description	Default
`model_dir`	No
`num_beams`	No
`num_preds`	No
`product_smiles`	Yes

TDQS

D1/5.0

Behavior1/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Tool has no description.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness1/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Tool has no description.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness1/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Tool has no description.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters1/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Tool has no description.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose1/5

Does the description clearly state what the tool does and how it differs from similar tools?

Tool has no description.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines1/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Tool has no description.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

validate_moleculeD

ParametersJSON Schema

Name	Required	Description	Default
`smiles`	Yes

TDQS

D1/5.0

Behavior1/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Tool has no description.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness1/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Tool has no description.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness1/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Tool has no description.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters1/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Tool has no description.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose1/5

Does the description clearly state what the tool does and how it differs from similar tools?

Tool has no description.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines1/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Tool has no description.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections.

5 tool updatesv0.1.0Jul 7, 2026
- First observedget_molecule_properties
- First observedpredict_product
- First observedpredict_reagents
- First observedpredict_retrosynthesis
- First observedvalidate_molecule

Install Server

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TDQS

C2.1/5.0

Scored 2026-07-07 06:00 across 5 tools

Disambiguation5/5

Each tool targets a distinct chemical task: properties, product prediction, reagent prediction, retrosynthesis, and validation. There is no overlap in functionality.

Naming Consistency5/5

All tools follow a consistent verb_noun pattern (e.g., get_molecule_properties, predict_product) with clear verbs and nouns, enhancing predictability.

Tool Count5/5

Five tools cover the essential operations for a chemistry prediction server without being excessive or insufficient for the stated purpose.

Completeness4/5

Core workflows (property retrieval, forward/reverse prediction, validation) are covered. A tool for reaction conditions (e.g., temperature/solvent) is a minor gap but does not impede typical use.

Maintenance

ActivityStale

ResponsivenessNo issues

Resources

GitHub Repository

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