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Rowan MCP Server

by k-yenko
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Python
Remote

submit_msa_workflow

Generate multiple sequence alignments for protein sequences to support structure prediction tools like AlphaFold2/ColabFold, Chai-1, and Boltz-1, enabling evolutionary analysis and homology modeling.

Instructions

Submit a multiple sequence alignment (MSA) workflow using Rowan v2 API.

Args: initial_protein_sequences: JSON string list of protein sequences (amino acid strings) output_formats: JSON string list of desired output formats. Valid options: 'colabfold', 'chai', 'boltz' name: Workflow name for identification and tracking folder_uuid: UUID of folder to organize this workflow. Empty string uses default folder. max_credits: Maximum credits to spend on this calculation. 0 for no limit.

Generates multiple sequence alignments for protein sequences using advanced alignment algorithms optimized for structure prediction tools. Useful for:

  • Protein structure prediction with AlphaFold2/ColabFold

  • Structure prediction with Chai-1

  • Structure prediction with Boltz-1

  • Evolutionary analysis and homology modeling

Valid output formats:

  • 'colabfold': MSA format for ColabFold/AlphaFold2 structure prediction

  • 'chai': MSA format optimized for Chai-1 structure prediction

  • 'boltz': MSA format optimized for Boltz-1 structure prediction

Returns: Workflow object representing the submitted workflow

Examples: # MSA for ColabFold structure prediction result = submit_msa_workflow( initial_protein_sequences='["MKLLVLGLLLAAAVPGTRAAQMSFKLIGTEYFTLQIRGRERFEMFRELN"]', output_formats='["colabfold"]', name="Insulin MSA for ColabFold" )

# MSA for multiple prediction tools
result = submit_msa_workflow(
    initial_protein_sequences='["MKTAYIAKQRQISFVKSHFSRQ"]',
    output_formats='["colabfold", "chai", "boltz"]',
    name="Multi-tool MSA"
)

# MSA for Chai-1 structure prediction
result = submit_msa_workflow(
    initial_protein_sequences='["GSTLGRIADRDLLELDTLAAKVPSDGAKDLVTDIVNRQIYDG"]',
    output_formats='["chai"]',
    name="Chai-1 MSA"
)

This workflow can take 10-30 minutes depending on sequence length.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
initial_protein_sequencesYesJSON string list of protein sequences (e.g., '["MKLLV...", "MAHQR..."]')
output_formatsYesJSON string list of output formats - must be 'colabfold', 'chai', or 'boltz' (e.g., '["colabfold", "chai"]')
nameNoWorkflow name for identification and trackingMSA Workflow
folder_uuidNoUUID of folder to organize this workflow. Empty string uses default folder
max_creditsNoMaximum credits to spend on this calculation. 0 for no limit

Tool Definition Quality

A4.2/5.0
Behavior4/5

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

With no annotations provided, the description carries full burden and does well by disclosing key behavioral traits: it's a submission tool (implies mutation), mentions runtime ('10-30 minutes depending on sequence length'), and explains the return value ('Returns: Workflow object representing the submitted workflow'). However, it doesn't mention error conditions, authentication needs, or rate limits.

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

Conciseness3/5

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

The description is well-structured with clear sections (Args, Valid output formats, Returns, Examples), but contains some redundancy (parameter explanations repeated from schema) and could be more front-loaded. The runtime information at the end is useful but could be integrated earlier.

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

Completeness4/5

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

For a 5-parameter mutation tool with no annotations and no output schema, the description does well by explaining the purpose, usage scenarios, parameters, return values, and providing examples. It covers the essential context needed to use the tool effectively, though it could benefit from more behavioral details like error handling.

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

Parameters3/5

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

Schema description coverage is 100%, so the schema already documents all parameters thoroughly. The description adds minimal value beyond the schema - it repeats parameter explanations and provides examples but doesn't add significant semantic context that isn't already in the schema descriptions.

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

Purpose5/5

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

The description clearly states the specific action ('Submit a multiple sequence alignment workflow') and resource ('using Rowan v2 API'), distinguishing it from sibling tools like submit_protein_cofolding_workflow or submit_docking_workflow by focusing exclusively on MSA generation for protein sequences.

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

Usage Guidelines5/5

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

The description explicitly lists when to use this tool ('Useful for: Protein structure prediction with AlphaFold2/ColabFold, Structure prediction with Chai-1, Structure prediction with Boltz-1, Evolutionary analysis and homology modeling') and provides clear alternatives through the output format options, helping users choose appropriate configurations.

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

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