Spec Driven Development
MCP Server that facilitates spec-driven development workflows, not just Vibe Coding.
Beschreibung vom Entwickler (englisches Original)
Construct, inspect, visualize, and simulate evidence-backed ODE models with BioMASS
Diese drei werden nie zu einer Punktzahl zusammengefasst: Meinungen, Messungen und Beliebtheit sind verschiedene Dinge.
Für diesen Server haben wir noch keine Zusammenfassung in einfacher Sprache geschrieben. Unten stehen die Fakten, die wir aus der Registry gelesen haben.
Wähle die KI-App, die du nutzt, und kopiere die Einstellungen.
Du brauchst
Welche App nutzt du?
Wähle oben deine App. Wir merken sie uns, damit der Button beim nächsten Mal „In … installieren“ anzeigt.
Öffne Claude Desktop → Settings → Developer → Edit Config und füge dies in claude_desktop_config.json ein
{
"mcpServers": {
"biomass": {
"command": "uvx",
"args": [
"mcp-biomodelling-servers"
]
}
}
}Führe das in deinem Terminal aus
claude mcp add biomass -- uvx mcp-biomodelling-serversIn .cursor/mcp.json in deinem Projekt oder in ~/.cursor/mcp.json einfügen
{
"mcpServers": {
"biomass": {
"command": "uvx",
"args": [
"mcp-biomodelling-servers"
]
}
}
}Dein Browser fragt, ob er die App öffnen darf, dann zeigt die App die Einstellungen und bittet dich um Bestätigung. Schlüsselwerte bleiben leer, damit du sie selbst einträgst.
In .vscode/mcp.json in deinem Projekt einfügen
{
"servers": {
"biomass": {
"type": "stdio",
"command": "uvx",
"args": [
"mcp-biomodelling-servers"
]
}
}
}Dein Browser fragt, ob er die App öffnen darf, dann zeigt die App die Einstellungen und bittet dich um Bestätigung. Schlüsselwerte bleiben leer, damit du sie selbst einträgst.
Dieser Server läuft auf deinem Computer, und ChatGPT kann sich nur mit Servern im Internet verbinden. Wähle eine andere App.
Deine Auswahl bleibt in einem Cookie auf diesem Gerät; kein Konto nötig.
Diese Einstellungen werden aus dem Registry-Eintrag des Entwicklers erzeugt. Wenn in der README etwas anderes steht, folge der README.
Läuft auf deinem Computer; die Tool-Liste erscheint nach der Installation in deiner KI-App.
Wir prüfen Informationen, die jeder sehen kann; den Code haben wir nicht gelesen. Checks, die nicht zutreffen, werden nicht mitgezählt. · Geprüft am 7. Nov. 2026
Der Registry-Name wurde mit dem GitHub-Konto marcorusc bestätigt, dem das Repo gehört
Dieses Repo haben wir noch nicht von GitHub gelesen
Dieses Repo haben wir noch nicht von GitHub gelesen
Dieser Server läuft auf deinem Computer; es gibt keinen Endpunkt im Internet
Läuft auf deinem Computer und nutzt stattdessen lokale Einstellungen
Läuft auf deinem Computer; Tool-Listen können wir nur von Endpunkten im Internet lesen
Verglichen mit allen aktiven Servern dieser Kategorie in der Registry. Sterne zählen nur, wenn dem Herausgeber das Repo gehört.
Er hat 1 von 1 zutreffenden Basischecks bestanden. Den Code haben wir nicht gelesen. Bevor du einen Schlüssel oder Token hinzufügst, sieh nach, welche Zugriffe der Server verlangt, und nutze einen Schlüssel, der nur die Berechtigungen hat, die du brauchst.
Die Registry nennt keinen erforderlichen Schlüssel, manche Server bitten dich aber bei der ersten Nutzung, dich anzumelden.
Er läuft auf deinem Computer. Deine KI-App startet ihn bei Bedarf, und die Daten laufen nicht über die Server des Entwicklers (außer der Server ruft selbst einen Onlinedienst auf).
Das GitHub-Repo wurde zuletzt – geändert, und die neueste Version (2.5.2) kam vor 10 Tagen in die Registry.
Apps, die MCP unterstützen, etwa Claude Desktop, Claude Code, Cursor und VS Code. Außerdem brauchst du Python (uv) auf deinem Computer.
In derselben Kategorie: Spec Driven Development, Remote Desktop Commander, Detailing Manager
Server derselben Kategorie von anderen Entwicklern, sortiert nach Sternen.
So liest du eine Karte:★ GitHub-SterneiDrei Säulen: Bewertungen · Checks · NutzungiAktualisiert = neueste Version in der Registry veröffentlichti
MCP Server that facilitates spec-driven development workflows, not just Vibe Coding.
Remote Desktop Commander: use files, terminal and processes on your own computers from any AI app.
Connect your auto detailing studio's tasks, cars, clients and revenue to Claude or ChatGPT.
Unverändert aus dem Repo des Entwicklers übernommen. PopNx MCP hat den Inhalt weder geprüft noch empfiehlt es ihn. HTML, Bilder und Skripte wurden entfernt. Links führen von dieser Website weg.
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.
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.
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.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.set_evidence stores source identifiers, supporting passages or summaries, locations, biological context, limitations, and supporting/contradicting/context-only stances.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.configure_model sets observables, time span, conditions, numerical defaults, units, and quantity provenance. It replaces the complete configuration.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.
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.
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.
The server exposes 22 tools:
| Family | Tools |
|---|---|
| Sessions | create_session, list_sessions, close_session, restore_session |
| Authoring | import_neko_handoff, import_text, import_text_file, set_evidence, set_reactions, build_reactions, configure_model |
| Inspection | inspect_model, inspect_reactions, validate_model |
| Generation | generate_model |
| Visualization | visualize_model, export_model_graph |
| Simulation | run_simulation |
| Artifacts | export_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:
| Resource | Reference |
|---|---|
docs://biomass/reaction_syntax | Supported syntax and generated kinetics |
docs://biomass/authoring_examples | Tested tool argument examples |
docs://biomass/network_to_reactions | Evidence-to-mechanism authoring guide |
docs://biomass/model_editing | Conversational 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.
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.