How do I use MCP Sequence Simulation 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., "@MCP Sequence Simulation Server simulate paired-end Illumina reads at 30x coverage for this sequence" 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.

MCP Sequence Simulation Server

by coreymhudson

Overview Schema Related Servers Score Discussions

TypeScript

Remote

MCP Sequence Simulation Server

An MCP (Model Context Protocol) server for simulating DNA and amino acid sequences using various evolutionary models and algorithms. This server provides powerful tools for sequence generation, mutation simulation, evolutionary modeling, and phylogenetic analysis.

Features

🧬 DNA Sequence Generation

Random DNA Generation: Generate sequences with specified GC content
Markov Chain Models: Context-dependent sequence generation
Codon-Biased Generation: Realistic protein-coding sequences
Customizable Parameters: Length, GC content, seed for reproducibility

📊 FASTQ Sequencing Simulation

NGS Read Simulation: Generate realistic next-generation sequencing reads
Platform-Specific Models: Illumina, 454, Ion Torrent, and PacBio quality models
Paired-End Support: Both single-end and paired-end sequencing reads
Error Modeling: Configurable sequencing error rates with realistic quality scores
Coverage Control: Generate reads to achieve specified coverage depths
NEAT-Based: Implementation inspired by published NEAT methodology

🧭 Protein Sequence Generation

Random Protein Generation: Uniform amino acid distribution
Hydrophobic-Biased: Membrane protein-like sequences
Disorder-Prone: Intrinsically disordered protein sequences
Custom Composition: User-defined amino acid frequencies

🔬 Sequence Mutation

Substitution Mutations: Point mutations with transition/transversion bias
Insertion/Deletion Events: Indel mutations
Multiple Iterations: Track changes over time
Both DNA and Protein: Support for nucleotide and amino acid sequences

🌳 Evolutionary Simulation

Population Evolution: Simulate populations over generations
Selection Pressure: Configurable fitness functions
Lineage Tracking: Follow individual evolutionary paths
Fitness Functions: GC content, length, hydrophobicity targets

🌲 Phylogenetic Simulation

Tree-Based Evolution: Simulate sequences on phylogenetic trees
Multiple Substitution Models: JC69, K80, HKY85, GTR
Molecular Clock: Uniform or variable evolutionary rates
Multiple Output Formats: FASTA, NEXUS, PHYLIP

Related MCP server: BioPython MCP Server

Installation

npm install
npm run build

Usage

With Claude Code

./start-claude.sh

Manual Configuration

Add to your Claude Code MCP configuration:

{
  "mcpServers": {
    "sequence-simulation": {
      "command": "node",
      "args": ["dist/server.js"],
      "cwd": "/path/to/mcp-sequence-simulation"
    }
  }
}

Available Tools

1. Generate DNA Sequence

Generate random DNA sequences with various models.

Parameters:

length (required): Sequence length
gcContent (optional): GC content ratio (0-1, default: 0.5)
count (optional): Number of sequences (default: 1)
model (optional): "random", "markov", or "codon-biased"
seed (optional): Random seed for reproducibility
outputFormat (optional): "fasta" or "plain"

Example:

{
  "length": 1000,
  "gcContent": 0.6,
  "count": 5,
  "model": "markov",
  "outputFormat": "fasta"
}

2. Generate Protein Sequence

Generate random protein sequences with various biases.

Parameters:

length (required): Sequence length
count (optional): Number of sequences (default: 1)
model (optional): "random", "hydrophobic-bias", or "disorder-prone"
composition (optional): Custom amino acid frequencies
seed (optional): Random seed
outputFormat (optional): "fasta" or "plain"

Example:

{
  "length": 200,
  "count": 3,
  "model": "hydrophobic-bias",
  "outputFormat": "fasta"
}

3. Simulate FASTQ File

Simulate FASTQ sequencing reads with realistic quality scores and error models.

Parameters:

referenceSequence (required): Reference DNA sequence to generate reads from
readLength (required): Length of each sequencing read (50-300 bp)
coverage (required): Target sequencing coverage depth (1-1000x)
readType (optional): "single-end" or "paired-end" (default: "single-end")
insertSize (optional): Mean insert size for paired-end reads (default: 300)
insertSizeStd (optional): Standard deviation of insert size (default: 50)
errorRate (optional): Base calling error rate 0-0.1 (default: 0.01)
qualityModel (optional): "illumina", "454", "ion-torrent", or "pacbio" (default: "illumina")
mutationRate (optional): Rate of true mutations 0-0.05 (default: 0.001)
seed (optional): Random seed for reproducibility
outputFormat (optional): "fastq" or "json" (default: "fastq")

Example:

{
  "referenceSequence": "ATCGATCGATCGATCGATCGATCGATCGATCGATCG",
  "readLength": 150,
  "coverage": 30,
  "readType": "paired-end",
  "errorRate": 0.01,
  "qualityModel": "illumina"
}

Citation: Based on Stephens et al. (2016) PLOS ONE 11(11): e0167047.

4. Mutate Sequence

Apply mutations to existing sequences.

Parameters:

sequence (required): Input sequence
sequenceType (required): "dna" or "protein"
substitutionRate (optional): Substitution rate (default: 0.01)
insertionRate (optional): Insertion rate (default: 0.001)
deletionRate (optional): Deletion rate (default: 0.001)
transitionBias (optional): Transition vs transversion bias for DNA (default: 2.0)
iterations (optional): Number of mutation rounds (default: 1)
seed (optional): Random seed
outputFormat (optional): "fasta" or "plain"

Example:

{
  "sequence": "ATGCGATCGATCG",
  "sequenceType": "dna",
  "substitutionRate": 0.02,
  "iterations": 5,
  "outputFormat": "fasta"
}

5. Evolve Sequence

Simulate sequence evolution over multiple generations.

Parameters:

sequence (required): Starting sequence
generations (required): Number of generations
populationSize (required): Population size
mutationRate (required): Mutation rate per generation
selectionPressure (optional): Selection strength (0-1)
fitnessFunction (optional): "gc-content", "length", "hydrophobic", or "custom"
targetValue (optional): Target value for fitness function
trackLineages (optional): Track individual lineages
seed (optional): Random seed
outputFormat (optional): "summary", "detailed", or "fasta"

Example:

{
  "sequence": "ATGCGATCGATCG",
  "generations": 100,
  "populationSize": 50,
  "mutationRate": 0.01,
  "selectionPressure": 0.3,
  "fitnessFunction": "gc-content",
  "targetValue": 0.5,
  "outputFormat": "detailed"
}

6. Simulate Phylogeny

Simulate sequence evolution on phylogenetic trees.

Parameters:

rootSequence (required): Ancestral sequence
treeStructure (optional): Newick format tree or "random"
numTaxa (optional): Number of taxa for random tree (default: 5)
mutationRate (optional): Mutation rate per branch length (default: 0.1)
branchLengthVariation (optional): Branch length variation (default: 0.2)
molecularClock (optional): Use molecular clock (default: true)
substitutionModel (optional): "JC69", "K80", "HKY85", or "GTR"
seed (optional): Random seed
outputFormat (optional): "fasta", "nexus", or "phylip"

Example:

{
  "rootSequence": "ATGCGATCGATCGATCG",
  "numTaxa": 8,
  "mutationRate": 0.05,
  "substitutionModel": "K80",
  "outputFormat": "nexus"
}

Output Formats

FASTA Format

Standard FASTA format with descriptive headers containing simulation parameters.

Statistics

All tools provide detailed statistics including:

Sequence composition analysis
Mutation counts and types
Evolutionary parameters
Phylogenetic tree statistics

Specialized Formats

NEXUS: For phylogenetic analysis software
PHYLIP: For phylogenetic analysis
JSON: Structured data with full simulation details

Use Cases

Research Applications

Molecular Evolution Studies: Simulate sequence evolution under different models
Phylogenetic Analysis: Generate test datasets with known evolutionary history
Algorithm Testing: Create benchmark datasets for bioinformatics tools
Educational Purposes: Demonstrate evolutionary principles

Bioinformatics Development

Algorithm Validation: Test sequence analysis tools with controlled data
Statistical Analysis: Generate null distributions for statistical tests
Performance Benchmarking: Create datasets of varying complexity
Method Comparison: Compare tools on simulated vs real data

Technical Details

Evolutionary Models

Jukes-Cantor (JC69): Equal substitution rates
Kimura 2-Parameter (K80): Transition/transversion bias
HKY85: Unequal base frequencies with transition bias
GTR: General time-reversible model

Sequence Generation

Markov Chains: Context-dependent nucleotide selection
Codon Usage Bias: Realistic protein-coding sequences
Amino Acid Properties: Hydrophobicity and disorder propensity

Mutation Models

Point Mutations: Single nucleotide/amino acid changes
Indels: Insertion and deletion events
Transition Bias: Realistic DNA mutation patterns

Dependencies

@modelcontextprotocol/sdk: MCP framework
zod: Schema validation
typescript: Type safety
Node.js: Runtime environment

Contributing

This server provides a comprehensive framework for sequence simulation. Extensions could include:

Additional substitution models
Recombination simulation
Population genetics models
Structural constraints
Codon usage tables for different organisms

License

See LICENSE file for details.

Available Tools

6 tools

evolve_sequenceC

Simulate evolution of sequences over multiple generations

ParametersJSON Schema

Name	Required	Description
`sequence`	Yes	Starting sequence
`generations`	Yes	Number of generations to simulate
`populationSize`	Yes	Population size for each generation
`mutationRate`	Yes	Mutation rate per generation (0-1)
`selectionPressure`	No	Selection pressure (0-1), 0 = no selection, 1 = strong selection
`fitnessFunction`	No	Fitness function: 'gc-content', 'length', 'hydrophobic', or 'custom'
`targetValue`	No	Target value for fitness function (e.g., target GC content)
`seed`	No	Random seed for reproducible results (optional)
`trackLineages`	No	Track individual lineages through generations
`outputFormat`	No	Output format: 'summary', 'detailed', or 'fasta'

TDQS

C2.9/5.0

Behavior2/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure but only states the simulation action without detailing traits like computational intensity, output format implications, or side effects. It misses opportunities to explain what 'evolve' entails beyond the basic parameters.

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

Conciseness5/5

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

The description is a single, efficient sentence that front-loads the core purpose without unnecessary words. It earns its place by succinctly conveying the tool's function, making it highly concise and well-structured.

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

Completeness2/5

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

Given the complexity of 10 parameters, no annotations, and no output schema, the description is incomplete. It fails to address behavioral aspects, output expectations, or usage context, leaving significant gaps for a tool with high parameter count and evolutionary simulation complexity.

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?

The schema description coverage is 100%, so the schema already documents all parameters thoroughly. The description adds no additional meaning beyond the schema, such as explaining interactions between parameters or simulation mechanics, resulting in a baseline score.

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

Purpose4/5

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

The description clearly states the action ('simulate evolution') and resource ('sequences over multiple generations'), making the purpose understandable. However, it doesn't explicitly differentiate from sibling tools like 'mutate_sequence' or 'simulate_phylogeny', which might involve similar evolutionary concepts.

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

Usage Guidelines2/5

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

No guidance is provided on when to use this tool versus alternatives. The description lacks context about scenarios where evolutionary simulation is appropriate compared to simpler mutation or generation tools, leaving the agent without usage direction.

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

generate_dna_sequenceC

Generate random DNA sequences with specified parameters

ParametersJSON Schema

Name	Required	Description
`length`	Yes	Length of the DNA sequence to generate
`gcContent`	No	GC content (0-1), default is 0.5
`count`	No	Number of sequences to generate, default is 1
`seed`	No	Random seed for reproducible results (optional)
`model`	No	Generation model: 'random', 'markov', or 'codon-biased'
`outputFormat`	No	Output format: 'fasta' or 'plain'

TDQS

C2.9/5.0

Behavior2/5

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

With no annotations provided, the description carries full burden but offers minimal behavioral context. It mentions 'random' generation but doesn't explain what makes sequences random, whether results are deterministic with seeds, performance characteristics, or output structure. For a generation tool with 6 parameters, this leaves significant behavioral gaps.

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

Conciseness5/5

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

The description is a single, efficient sentence that immediately communicates the core functionality. Every word earns its place with no redundancy or unnecessary elaboration. It's appropriately sized for a generation tool with well-documented parameters.

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

Completeness2/5

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

For a generation tool with 6 parameters, no annotations, and no output schema, the description is insufficiently complete. It doesn't explain what the output looks like (sequences, metadata), how parameters interact, or behavioral constraints. The high parameter count and lack of structured output information create significant gaps in understanding.

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 fully documents all parameters. The description adds no additional parameter semantics beyond implying parameters control the generation process. This meets the baseline for high schema coverage but doesn't enhance understanding of how parameters interact or affect results.

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

Purpose4/5

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

The description clearly states the verb 'generate' and resource 'random DNA sequences', making the purpose immediately understandable. It distinguishes from siblings like 'evolve_sequence' or 'mutate_sequence' by focusing on generation rather than modification. However, it doesn't explicitly differentiate from 'generate_protein_sequence' beyond the resource type.

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

Usage Guidelines2/5

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

The description provides no guidance on when to use this tool versus alternatives like 'generate_protein_sequence' for proteins or 'simulate_fastq_file' for file output. There's no mention of prerequisites, typical use cases, or scenarios where other tools might be more appropriate.

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

generate_protein_sequenceB

Generate random protein sequences with specified parameters

ParametersJSON Schema

Name	Required	Description
`length`	Yes	Length of the protein sequence to generate
`count`	No	Number of sequences to generate, default is 1
`seed`	No	Random seed for reproducible results (optional)
`model`	No	Generation model: 'random', 'hydrophobic-bias', or 'disorder-prone'
`composition`	No	Custom amino acid composition (frequencies should sum to 1)
`outputFormat`	No	Output format: 'fasta' or 'plain'

TDQS

B3.1/5.0

Behavior2/5

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

No annotations are provided, so the description carries full burden. It states 'generate random protein sequences' but doesn't disclose behavioral traits like whether this is a deterministic or stochastic process, computational requirements, rate limits, or what the output looks like (e.g., format details beyond schema). The mention of 'random' and 'reproducible' via seed hints at behavior, but lacks depth for a tool with 6 parameters.

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

Conciseness5/5

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

The description is a single, efficient sentence that front-loads the core purpose without unnecessary words. Every part ('generate random protein sequences with specified parameters') contributes directly to understanding the tool's function.

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

Completeness3/5

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

Given 6 parameters with full schema coverage but no annotations or output schema, the description is adequate but incomplete. It covers the 'what' (generate protein sequences) but lacks context on behavior, output format implications, or integration with sibling tools, leaving gaps for an AI agent to navigate usage effectively.

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%, providing detailed descriptions for all 6 parameters. The description adds minimal value beyond the schema, only implying that parameters customize the generation. No additional semantic context or examples are given, so it meets the baseline for high schema coverage.

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

Purpose4/5

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

The description clearly states the verb ('generate') and resource ('protein sequences'), specifying they are 'random' with 'specified parameters'. It distinguishes from siblings like 'generate_dna_sequence' by specifying protein sequences, but doesn't explicitly differentiate from 'evolve_sequence' or 'mutate_sequence' in terms of generation vs. modification.

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

Usage Guidelines2/5

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

The description provides no guidance on when to use this tool versus alternatives like 'evolve_sequence' or 'mutate_sequence'. It mentions 'specified parameters' but doesn't clarify use cases, prerequisites, or exclusions, leaving the agent to infer usage from the tool name alone.

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

mutate_sequenceC

Apply mutations to DNA or protein sequences

ParametersJSON Schema

Name	Required	Description
`sequence`	Yes	Input sequence to mutate
`sequenceType`	Yes	Type of sequence: 'dna' or 'protein'
`substitutionRate`	No	Substitution mutation rate (0-1), default 0.01
`insertionRate`	No	Insertion mutation rate (0-1), default 0.001
`deletionRate`	No	Deletion mutation rate (0-1), default 0.001
`transitionBias`	No	Transition vs transversion bias for DNA (default 2.0)
`iterations`	No	Number of mutation iterations, default 1
`seed`	No	Random seed for reproducible results (optional)
`outputFormat`	No	Output format: 'fasta' or 'plain'

TDQS

C2.9/5.0

Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool 'Apply mutations' but does not explain what this entails beyond the basic action. It lacks details on permissions, side effects, rate limits, or output behavior, which are critical for a mutation tool. The description is minimal and does not compensate for the absence of annotations.

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

Conciseness5/5

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

The description is extremely concise and front-loaded, consisting of a single sentence: 'Apply mutations to DNA or protein sequences.' It efficiently conveys the core purpose without unnecessary words or complexity, making it easy for an agent to parse quickly.

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

Completeness2/5

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

Given the complexity of a mutation tool with 9 parameters and no annotations or output schema, the description is incomplete. It does not address behavioral aspects like safety, permissions, or output format implications, nor does it provide usage context. For a tool that modifies sequences, more detail is needed to guide the agent effectively, making this inadequate.

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%, meaning all parameters are documented in the input schema. The description does not add any additional meaning or context beyond what the schema provides, such as explaining how mutations are applied or the implications of parameters. With high schema coverage, the baseline score of 3 is appropriate, as the description does not enhance parameter understanding.

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

Purpose4/5

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

The description clearly states the tool's purpose: 'Apply mutations to DNA or protein sequences.' It specifies the verb ('apply mutations') and the resource ('DNA or protein sequences'), making the function unambiguous. However, it does not explicitly differentiate from sibling tools like 'evolve_sequence' or 'generate_dna_sequence,' which might have overlapping or related functions, preventing a perfect score.

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

Usage Guidelines2/5

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

The description provides no guidance on when to use this tool versus alternatives. It does not mention sibling tools, context for application, or any exclusions. Usage is implied only by the tool's name and description, leaving the agent to infer when this is appropriate without explicit direction.

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

simulate_fastq_fileB

Simulate FASTQ sequencing reads with realistic quality scores and error models based on NEAT methodology. Implementation inspired by: Stephens et al. (2016) 'Simulating Next-Generation Sequencing Datasets from Empirical Mutation and Sequencing Models.' PLOS ONE 11(11): e0167047. https://doi.org/10.1371/journal.pone.0167047

ParametersJSON Schema

Name	Required	Description
`referenceSequence`	Yes	Reference DNA sequence to generate reads from
`readLength`	Yes	Length of each sequencing read
`coverage`	Yes	Target sequencing coverage depth
`readType`	No	Type of reads to generate
`insertSize`	No	Mean insert size for paired-end reads (ignored for single-end)
`insertSizeStd`	No	Standard deviation of insert size for paired-end reads
`errorRate`	No	Base calling error rate (0-1)
`qualityModel`	No	Quality score model to use
`mutationRate`	No	Rate of true mutations to introduce (0-1)
`seed`	No	Random seed for reproducible results (optional)
`outputFormat`	No	Output format for the reads

TDQS

B3.1/5.0

Behavior2/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. While it mentions 'realistic quality scores and error models' and cites the NEAT methodology, it doesn't describe key behavioral aspects: what the output looks like (FASTQ format details), whether the simulation is deterministic or stochastic, computational requirements, or any limitations of the implementation. For an 11-parameter simulation tool with no annotations, this is insufficient.

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

Conciseness4/5

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

The description is appropriately concise - two sentences that efficiently convey the core functionality and academic foundation. The first sentence clearly states the purpose, and the second provides citation without unnecessary elaboration. However, it could be slightly more front-loaded by explicitly mentioning the output format earlier.

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

Completeness2/5

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

For a complex simulation tool with 11 parameters and no output schema, the description is incomplete. It doesn't explain what the tool returns (FASTQ file content, structure, or JSON format details), doesn't mention performance characteristics or limitations, and provides no examples of typical use cases. The absence of annotations exacerbates these gaps, making the description inadequate for guiding effective tool use.

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?

The schema description coverage is 100%, so all parameters are documented in the schema. The description doesn't add any parameter-specific information beyond what's already in the schema descriptions. It provides general context about the NEAT methodology but no additional details about individual parameters. This meets the baseline expectation when schema coverage is complete.

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 tool's purpose: 'Simulate FASTQ sequencing reads with realistic quality scores and error models based on NEAT methodology.' It specifies the verb ('simulate'), resource ('FASTQ sequencing reads'), and methodology, distinguishing it from sibling tools like 'evolve_sequence' or 'generate_dna_sequence' which don't mention FASTQ format or quality scores.

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

Usage Guidelines2/5

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

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention sibling tools or suggest scenarios where this simulation approach is preferred over other sequence generation tools. The citation adds academic context but doesn't offer practical usage advice.

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

simulate_phylogenyC

Simulate phylogenetic tree and sequence evolution

ParametersJSON Schema

Name	Required	Description
`rootSequence`	Yes	Root sequence for phylogenetic simulation
`treeStructure`	No	Newick format tree or 'random' for random tree
`numTaxa`	No	Number of taxa for random tree generation
`mutationRate`	No	Mutation rate per branch length unit
`branchLengthVariation`	No	Variation in branch lengths (0-1)
`molecularClock`	No	Use molecular clock (uniform rates)
`substitutionModel`	No	Substitution model: 'JC69', 'K80', 'HKY85', or 'GTR'
`seed`	No	Random seed for reproducible results (optional)
`outputFormat`	No	Output format: 'fasta', 'nexus', or 'phylip'

TDQS

C2.9/5.0

Behavior2/5

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

With no annotations provided, the description carries full burden for behavioral disclosure. While 'simulate' implies generation rather than modification of existing data, it doesn't address critical behavioral aspects: whether this is a read-only operation, what computational resources it requires, whether it's deterministic with the seed parameter, or what the output looks like beyond format options.

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

Conciseness5/5

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

The description is extremely concise at just 5 words: 'Simulate phylogenetic tree and sequence evolution'. It's front-loaded with the core functionality and contains zero wasted words or redundant information. Every word earns its place in communicating the tool's purpose.

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

Completeness2/5

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

For a complex tool with 9 parameters, no annotations, and no output schema, the description is inadequate. It doesn't explain what the tool returns (sequences? trees? both?), doesn't provide behavioral context about computational requirements or limitations, and doesn't help users understand when to choose this over sibling tools. The 100% schema coverage helps, but the description itself lacks completeness for such a sophisticated simulation tool.

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?

The schema description coverage is 100%, so the schema already documents all parameters thoroughly. The description adds no additional parameter semantics beyond what's in the schema. The baseline of 3 is appropriate since the schema does the heavy lifting, though the description doesn't compensate with any higher-level explanation of parameter relationships or typical usage patterns.

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

Purpose4/5

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

The description clearly states the tool's purpose: 'Simulate phylogenetic tree and sequence evolution'. This specifies both the tree simulation and sequence evolution components, making it more specific than just 'simulate phylogeny'. However, it doesn't explicitly differentiate from sibling tools like 'evolve_sequence' or 'mutate_sequence', which might have overlapping functionality.

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

Usage Guidelines2/5

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

The description provides no guidance on when to use this tool versus alternatives. With sibling tools like 'evolve_sequence', 'generate_dna_sequence', and 'mutate_sequence', there's no indication of how this tool differs in application or which scenarios warrant its use over those alternatives.

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. Dates show when Glama detected each change.

6 tool updatesv0.1.0Feb 22, 2026
- First observedevolve_sequence
- First observedgenerate_dna_sequence
- First observedgenerate_protein_sequence
- First observedmutate_sequence
- First observedsimulate_fastq_file
- First observedsimulate_phylogeny

Install Server

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TDQS

A3.5/5.0

Disambiguation5/5

Each tool has a clearly distinct purpose with no ambiguity. Tools target specific simulation tasks: sequence generation (DNA/protein), evolution (evolve/mutate), phylogeny simulation, and FASTQ file generation. The descriptions clearly differentiate their scopes, preventing misselection.

Naming Consistency5/5

All tools follow a consistent verb_noun pattern (e.g., evolve_sequence, generate_dna_sequence, simulate_fastq_file). The naming convention is uniform throughout, using snake_case and clear action-object pairs, making the tool set predictable and readable.

Tool Count5/5

With 6 tools, the count is well-scoped for a sequence simulation server. Each tool earns its place by covering key aspects: sequence generation, mutation, evolution, phylogeny, and FASTQ simulation. This provides a focused yet comprehensive surface without being overwhelming.

Completeness4/5

The tool set covers core sequence simulation workflows effectively, including generation, mutation, evolution, phylogeny, and sequencing data. A minor gap is the lack of tools for analyzing or visualizing simulated data, but agents can work around this as the surface supports essential simulation tasks.

Maintenance

ActivityInactive

ResponsivenessSyncing

Resources

GitHub Repository

Need Help?

Reddit Discussion

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MCP Sequence Simulation Server

Features

🧬 DNA Sequence Generation

📊 FASTQ Sequencing Simulation

🧭 Protein Sequence Generation

🔬 Sequence Mutation

🌳 Evolutionary Simulation

🌲 Phylogenetic Simulation

Installation

Usage

With Claude Code

Manual Configuration

Available Tools

1. Generate DNA Sequence

2. Generate Protein Sequence

3. Simulate FASTQ File

4. Mutate Sequence

5. Evolve Sequence

6. Simulate Phylogeny

Output Formats

FASTA Format

Statistics

Specialized Formats

Use Cases

Research Applications

Bioinformatics Development

Technical Details

Evolutionary Models

Sequence Generation

Mutation Models

Dependencies

Contributing

License

Available Tools

Tool Schema Changelog

Maintenance

Resources

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bio-mcp-evo2official

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