Prototype · Preview: every server in the official registry, synced every 30 minutes. Reviews and accounts come in a later step.
PopNx MCPMCP servers for people who use AI · explained in plain words

BioMASS ODE Model Builder

by marcoruscversion 2.5.2i★ –iRuns on your computer
Registry nameio.github.marcorusc/BioMASS

Install

Overview

Description from the maker (original English)

Construct, inspect, visualize, and simulate evidence-backed ODE models with BioMASS

PopNx MCP’s three pillars

These three are never merged into one score: opinions, measurements and popularity are different things.

What you can do with it

We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.

  • Connection: Runs on your computer
  • Transport: stdio
  • Category: Other

Install

Pick the AI app you use, then copy the settings.

Before you install

  • Basic checks: 1 of 1 passed
  • Runs on your computer with your user’s permissions (files, network, apps).
  • The registry lists no key to fill in.

You need

  • PyPIPython’s uv installed (for the uvx command)

Which app do you use?

Pick your app above. We remember it, so next time the button says “Install in …”.

Open Claude Desktop → Settings → Developer → Edit Config, and paste this into claude_desktop_config.json

{
  "mcpServers": {
    "biomass": {
      "command": "uvx",
      "args": [
        "mcp-biomodelling-servers"
      ]
    }
  }
}

Run this in your terminal

claude mcp add biomass -- uvx mcp-biomodelling-servers

Open in Cursor

Or copy the settings yourself

Paste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json

{
  "mcpServers": {
    "biomass": {
      "command": "uvx",
      "args": [
        "mcp-biomodelling-servers"
      ]
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

Open in VS Code

Or copy the settings yourself

Paste into .vscode/mcp.json in your project

{
  "servers": {
    "biomass": {
      "type": "stdio",
      "command": "uvx",
      "args": [
        "mcp-biomodelling-servers"
      ]
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

This one runs on your computer, and ChatGPT can only connect to servers on the internet. Pick another app.

Your choice stays on this device in a cookie; no account needed.

These settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.

What it can do

Runs on your computer; the tool list appears in your AI app after you install it.

Basic checksi

We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 7, 2026

  • ✓ Pass

    Maker matches the repo

    The registry name was verified with the GitHub account marcorusc, which owns the repo

  • … Not checked yet

    Still maintained

    We have not read this repo from GitHub yet

  • … Not checked yet

    Has a licence

    We have not read this repo from GitHub yet

  • – Not applicable

    Endpoint responds

    This one runs on your computer; there is no internet endpoint

  • – Not applicable

    Sign-in with OAuth

    Runs on your computer and uses local settings instead

  • … Not checked yet

    No hidden instructions in tool descriptions

    Runs on your computer; we can only read tool lists from internet endpoints

Compared with others in Otheri

  • More stars than 0% of the other 32655 servers in this category
  • Published to the registry more recently than 78% of the other 32655 servers in this category
  • Basic checks equal to or better than 100% of the other 32655 servers in this category

Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.

Common questions

Is BioMASS ODE Model Builder safe?

It passed 1 of 1 applicable basic checks. We have not read the code. Before adding a key or token, see what access the server asks for and use a key with only the permissions you need.

What keys or codes does it need?

The registry does not list a required key, though some servers ask you to sign in on first use.

Does it run on my computer or in the cloud?

It runs on your computer. Your AI app starts it when needed, and data does not pass through the maker’s servers (unless it calls an online service itself).

Is it still maintained?

The GitHub repo last changed –, and the latest version (2.5.2) reached the registry 10 days ago.

Which apps can use it?

Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Python (uv) on your computer.

What can I use instead?

In the same category: Spec Driven Development, Remote Desktop Commander, SuperCollider

Alternatives

Servers in the same category from other makers, sorted by stars.

Reading a card:★ GitHub starsiThree pillars: reviews · checks · usageiUpdated = latest version published to the registryi

Spec Driven Development

by formulahendry

MCP Server that facilitates spec-driven development workflows, not just Vibe Coding.

Runs on your computer★ 440MITUpdated 14 months ago
Install

Remote Desktop Commander

by desktop-commander

Remote Desktop Commander: use files, terminal and processes on your own computers from any AI app.

Runs in the cloud★ 211Updated 2 days ago
Install

Make sound with SuperCollider: synths, drums, chords, melodies and soundscapes over OSC.

Runs on your computerUpdated today
Install

README from the maker

Open the README

Copied from the maker’s repo as written. PopNx MCP has not reviewed or endorsed it. HTML, images and scripts are removed. Links lead off this site.

BioMASS ODE Model Builder

A stateful MCP server for evidence-backed ODE construction using BioMASS 0.14 and Text2Model. It supports model inspection, graph visualization, export, and bounded exploratory simulation. The calling agent reads literature and proposes mechanisms. Calibration and sensitivity analysis are outside v1.

Installation

python -m pip install mcp-biomodelling-servers

mcp-biomass-server

Graph rendering is part of the standard installation: PyGraphviz wheels bundle Graphviz, so no system Graphviz or dot program is needed. The former [biomass-graph] extra is kept as an empty alias for existing install commands. On a platform without a PyGraphviz wheel, pip builds it from source, which needs a C compiler and Graphviz development headers.

Example MCP client configuration:

{

"servers": { "biomass": { "type": "stdio", "command": "uvx", "args": ["--from", "mcp-biomodelling-servers", "mcp-biomass-server"] } } }

From a source checkout, install with python -m pip install '.[dev]' and run python -m BioMASS.server. The repository's wheel path remapping does not support editable installs.

NeKo workflow

  1. Curate a network in NeKo, then call its export_biomass_handoff with the biological context. The export preserves nodes, stable edge IDs, references, and available mechanism/context columns. It does not require connectivity.
  2. create_session, then import_neko_handoff with the returned manifest path. Import verifies artifact integrity and stores a durable copy of the network and provenance inside the authoring snapshot. If BioMASS cannot read NeKo's files (a host that sandboxes each server), pass handoff_bundle, the object returned by NeKo export_handoff_bundle, instead of the path.
  3. Read papers with the calling agent's literature tools. set_evidence stores source identifiers, supporting passages or summaries, locations, biological context, limitations, and supporting/contradicting/context-only stances.
  4. build_reactions adds or edits reactions; set_reactions replaces the complete list. Each record has a stable ID, Text2Model statement, originating edges, evidence IDs, and a supported/assumed/unreviewed status. Scientific annotations are optional and supplied by the agent; new records default to unreviewed. These links may be many-to-many; uncovered edges remain in the coverage report.
  5. configure_model sets observables, time span, conditions, numerical defaults, units, and quantity provenance. It replaces the complete configuration.
  6. Inspect, validate, generate, visualize, optionally simulate, and export.

Reaction example (IDs must correspond to imported edges and stored evidence):

{

"reaction_id": "binding", "statement": "E + S <--> ES | kf=0.003, kr=0.001 | E=100, S=50, ES=0", "status": "supported", "evidence_ids": ["binding_paper"], "edge_ids": ["edge_returned_by_import"] }

Use share_parameters_with to refer to an earlier reaction ID. The renderer resolves that ID to the correct Text2Model line number. Numeric sharing in reaction records is rejected. Complete replacement with set_reactions clears numerical overrides and conditions because generated parameter names depend on line numbers; reapply those settings after inspecting the revised model.

For conversational construction, call create_session, then build_reactions with templates or raw statements. inspect_reactions returns stable IDs and generated symbols. Preview a batch using expected_version, then apply with preview=false. Incremental edits retain compatible numerical settings. Read model editing or docs://biomass/model_editing for details. The agent chooses kinetics and any scientific annotations; the server validates syntax, references, dependencies, and execution.

Standalone text workflow

import_text preserves a complete Text2Model document verbatim, including comments, directives, and numeric line references. Replacing a document also replaces its line-evidence mapping and clears the old configuration. Evidence can be stored before import, then linked by line number. In document mode, put observables, simulation conditions, and time span in the text itself; configure_model can override numeric defaults and record units/provenance.

import_text_file reads an existing UTF-8 file into the session with its source path and hash. build_reactions can expand or edit it while preserving untouched lines and parameter references. The original file is never modified. Removals leave comment lines so later line numbers remain stable; dependent observables and conditions must be repaired explicitly in the same batch.

examples/enzyme.txt is a small executable enzyme model. It specifies all three parameters, all four initial values (including zeros), and the time span, so it runs without opting into placeholder values.

{"scenario": {"name": "enzyme"}, "session_id": "returned-session-id"}

Pass that object to run_simulation after generate_model. For explicitly hypothetical runs, set scenario.allow_placeholders to true. A supplied or assumed value is distinguished from a Text2Model placeholder. Literature-derived quantities require evidence IDs. Unspecified units remain explicit null values.

Scenario overrides replace generated defaults before condition assignments. Each condition starts from fresh defaults. The actual parameters and initial values used for each solve, including steady-state preparation, are recorded. Parameter-sharing constraints remain effective; override their source parameter. Simulation produces complete species and observable CSV tables, a trajectory preview (up to 20 species), and a numerical scenario report.

Graph visualization

  • visualize_model(format="png"): default static graph and MCP image content.
  • visualize_model(format="svg"): scalable static graph.
  • visualize_model(format="html"): interactive graph; no browser is opened.
  • export_model_graph: DOT for external graph software.

Static layouts are dot (default), neato, fdp, circo, and twopi. HTML optionally exposes physics/layout controls. Layout selection applies to static formats. Interactive HTML references the vis-network JavaScript and stylesheet CDN used by BioMASS’s supported PyVis version; opening it requires network access. Graph artifacts are linked to an immutable model revision and are included in that revision's exported bundle.

BioMASS projects reactants and modifiers onto products, combining repeated species connections. It does not distinguish reactants from modifiers, or activating from inhibiting modifiers. Therefore this visualization is not a complete reaction graph or a signed causal network. Unresolved NeKo edges remain in coverage reports. An interactive graph is not a simulation animation. See the upstream graph tutorial.

Tools and resources

The server exposes 22 tools:

FamilyTools
Sessionscreate_session, list_sessions, close_session, restore_session
Authoringimport_neko_handoff, import_text, import_text_file, set_evidence, set_reactions, build_reactions, configure_model
Inspectioninspect_model, inspect_reactions, validate_model
Generationgenerate_model
Visualizationvisualize_model, export_model_graph
Simulationrun_simulation
Artifactsexport_model_bundle, list_generated_files, list_artifact_sessions, clean_generated_files

Read docs://biomass/agent_manual or request biomass_workflow_prompt for agent instructions. Session resources expose /model, /evidence, /coverage, /files, and /revision/{revision} under biomass://session/{session_id}.

Before writing reactions, agents should read these offline MCP resources:

ResourceReference
docs://biomass/reaction_syntaxSupported syntax and generated kinetics
docs://biomass/authoring_examplesTested tool argument examples
docs://biomass/network_to_reactionsEvidence-to-mechanism authoring guide
docs://biomass/model_editingConversational construction, templates, and file editing

These references ship with the server and are linked from its initialization instructions, agent manual, workflow prompt, and authoring tool descriptions. They document the supported BioMASS 0.14 subset. A separate skill is not required. The syntax examples are converted in tests; the complete example workflows are also generated and simulated. Signed edges do not automatically determine mechanisms, kinetic laws, or one reaction per edge.

Revisions, validation, and limits

Each session has locked authoring state and a durable JSON snapshot. Workers use separate directories and fresh interpreters. Generated revisions have integrity inventories; authoring edits clear the current revision pointer but retain previous revisions. Explicitly select an old revision to inspect its original model and evidence. Failed jobs retain only diagnostic logs.

Syntax validity, generation success, evidence coverage, and numerical execution are separate results. None establishes biological validity. Validation with check_generation=true uses a disposable worker and publishes no revision.

Accepted expressions contain finite constants, arithmetic, and model-symbol references (p[name], u[name], init[name] where applicable). Python calls, attributes, imports, and arbitrary Python model packages are unsupported. Identifiers start with a letter and use letters, digits, and single underscores. Numerical reaction values must be finite and nonnegative.

Workers default to 60 seconds, configurable from 1 to 300 seconds. v1 limits: 1 MiB / 2000 text lines, 500 species / 1000 reactions, 20 conditions, and 10001 integer time samples. Timeouts terminate the worker and its child processes on POSIX systems. These processes isolate runtime state; they are not an OS sandbox.

Exports include generated Python, original text, numerical configuration, evidence, assumptions, coverage, version metadata, graphs, and saved runs. Extract the ZIP and run python run_simulation.py to list saved scenarios, then python run_simulation.py simulate_ to reproduce one. The script uses the recorded actual numerical conditions. requirements.txt pins BioMASS 0.14.0. Closing a session preserves artifacts; cleanup preserves authoring and lineage while removing generated revisions and runs.

Artifacts and restorable session snapshots now use $MCP_BIOMODELLING_ARTIFACT_ROOT/BioMASS/ or the shared user-cache default documented in the root README. Legacy package-local directories remain listed with legacy=true and their full location; they are not migrated, deleted, or automatically restored.

Ask PopNx