nsforge-mcp
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@followed by the MCP server name and your instructions, e.g., "@nsforge-mcpderive body fat-adjusted volume of distribution"
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Here is a step-by-step guide with screenshots.
π₯ Neurosymbolic Forge (NSForge)
Turn concepts into verifiable, traceable formulas. NSForge is an MCP server that forges new formulas through deterministic, provenance-tracked derivation β the AI orchestrates, tools reify.
π English | ηΉι«δΈζ
π‘ Why NSForge?
LLMs are great at understanding and planning, but doing symbolic math by hand makes them hallucinate, contradict themselves, and produce unverifiable results. NSForge draws a clean line:
The LLM does⦠| NSForge does⦠|
Understand the question | Precise symbolic computation |
Plan the derivation | Track every step's provenance |
Explain the result | Verify (dimensions, boundary, equivalence) |
β | Store the formula, generate code |
North star: every symbol, equation, value, and line of code in a result has a tool call as its birth certificate. The amount the AI computes by hand approaches zero.
NSForge is NOT a formula database β it's a derivation factory. Formulas are inputs (from SymPyβMCP, Wikidata, BioModels, you); the operators (compose Β· substitute Β· solve Β· verify Β· prove) are the product.
Related MCP server: Desmos MCP Server
πͺ The Reification Ladder
The core idea: climb from a fuzzy concept to executable, provenance-bound code, one deterministic rung at a time.
flowchart LR
C["π CONCEPT<br/>a goal"] --> S["π€ SYMBOL<br/>typed + units"]
S --> D["π§© DERIVATION<br/>composed"]
D --> V{"β
verify"}
V -->|"fails"| D
V -->|"passes"| A["βοΈ ALGORITHM<br/>code"]
A -. "emitted only if ledger complete" .-> L[["π provenance ledger"]]
C -.-> L
S -.-> L
D -.-> Lπ Deep dive: Reification-ladder direction Β· General-formula-exploration roadmap
π Ecosystem: don't reinvent the wheel
NSForge works with other MCP servers, not against them.
flowchart TB
subgraph SY["π’ sympy-mcp Β· 32 tools"]
direction LR
SY1["Base formulas: F=ma, PV=nRT, Arrhenius"]
SY2["Constants Β· ODE / PDE / matrices"]
end
subgraph NS["π¨ nsforge-mcp Β· 91 tools β YOU ARE HERE"]
direction LR
NS1["Derivation framework<br/>compose Β· verify Β· code"]
NS2["Provenance repository"]
NS3["Formula search<br/>Wikidata Β· BioModels Β· SciPy"]
end
subgraph US["π― usolver-mcp Β· optional"]
US1["Z3 Β· OR-Tools Β· CVXPY Β· HiGHS"]
end
SY -->|"base formulas"| NS
NS -->|"prepared model"| US
NS -->|"stores CREATED formulas"| REPO[("formulas/derivations")]β Belongs in NSForge | β Use another tool |
Temperature-corrected drug elimination | Basic physics formulas β sympy-mcp |
Body-fat-adjusted volume of distribution | Physical constants β sympy-mcp |
Renal-function dose adjustments | Clinical scores β medical-calc-mcp |
Custom composite PK/PD models | Textbook formulas β references |
π¦ Installation
Requirements: Python 3.12+ and uv (recommended).
uv add nsforge-mcp # or: pip install nsforge-mcpgit clone https://github.com/u9401066/nsforge-mcp.git
cd nsforge-mcp
uv sync --all-extras
uv run python -c "import nsforge; print(nsforge.__version__)"Configure as an MCP server
{
"mcpServers": {
"nsforge": { "command": "uvx", "args": ["nsforge-mcp"] }
}
}π¬ How it works β SymPy-MCP first
The golden rule: compute & verify with SymPy-MCP, then record with NSForge (provenance + human insight at every step).
flowchart LR
A["π€ LLM<br/>understand + plan"] --> B["π’ SymPy-MCP<br/>compute + verify"]
B --> C["π¨ NSForge<br/>record step + provenance"]
C --> D{"more<br/>steps?"}
D -->|"yes"| B
D -->|"no"| E["β
complete<br/>stored formula + code"]Task | Tool | Why |
Math computation | SymPy-MCP | Full ODE / PDE / matrix support |
Formula display |
| User confirms each step |
Knowledge storage | NSForge | Provenance, searchable |
Dimension check | NSForge | Physical-unit verification |
π§ Autonomous task orchestration (L2 / L3)
Hand NSForge a declarative Derivation Task Spec (DTS) and it runs the whole ladder for you. task_explore turns a single answer into a space of verified answers: it runs the base derivation plus every alternative, then ranks the survivors.
flowchart TD
DTS["π Derivation Task Spec"] --> BASE["base derivation"]
DTS --> ALT1["alternative 1"]
DTS --> ALT2["alternative 2"]
BASE --> V["verify Β· acceptance oracles Β· provenance"]
ALT1 --> V
ALT2 --> V
V --> RANK["π ranked candidates<br/>verified Β· oracles passed Β· simpler"]task_planβ reify a DTS into an ordered, provenance-tagged plantask_runβ run the ladder end-to-end (optional hardtimeout_s)task_exploreβ branching search returning all verified candidates
ποΈ Step-by-step control
Navigate and edit a derivation like a version-controlled document. Expressions are immutable (that keeps verification honest) β to change a result, rollback to a valid state and re-derive.
stateDiagram-v2
direction LR
[*] --> deriving
deriving --> deriving: get_step / update_step / insert_note
deriving --> earlier: rollback
earlier --> deriving: re-derive a new path
deriving --> [*]: complete + savederivation_get_step Β· derivation_update_step Β· derivation_rollback Β· derivation_insert_note Β· derivation_delete_step β see the tool reference.
π οΈ Tools at a glance β 91 tools Β· 82 loaded by default
Module | # | What it does |
π₯ Derivation engine | 31 | Stateful sessions: compose, step, track, store |
π’ Calculation | 12 | Limits, series, sums, inequalities, probability |
π£ Advanced algebra & transforms | 14 | expand/factor/apartβ¦ + Laplace / Fourier |
β Verification | 6 | Equality, derivative, integral, dimensions |
π Formula search | 6 | Wikidata, BioModels, SciPy constants |
π» Code generation | 4 | Python, LaTeX, report, SymPy script |
π Expression | 3 | Parse, validate, extract symbols |
π§ Task orchestration | 3 |
|
π§ Suggester | 1 | Retrieval-augmented next-step ranking |
π΅ Music (opt-in) | 9 | Symbolic tones β waveform, spectrum, WAV β set |
π§© Runtime self-description | 2 |
|
π Full list with every tool: Tool Reference Β· machine-readable
capabilities.json
β Verification harness
One command is the ground truth. python scripts/check.py runs 10 gates β a green run is the definition of "done".
lint Β· format Β· type Β· import Β· manifest Β· test Β· bench Β· generic Β· provenance Β· diffbench β known derivations reproduce correctly
generic β unseen, randomly-composed formulas derive correctly (proves NSForge is a derivation calculus, not a hand-built library)
provenance β every benchmark derivation carries a complete tool-provenance ledger (no hand-derived leaks)
python scripts/check.py # all gates
python scripts/check.py --json # machine-readable (for agents)π Derivation repository
Derived formulas are stored with full provenance β LaTeX, SymPy form, the base formulas combined, the steps taken, verification status, and clinical/physical context.
Derivation | Domain | Description |
PK | First-order elimination + Arrhenius | |
PK/PD | Henderson-Hasselbalch + Emax | |
PK | Saturable kinetics + temperature | |
PBPK | Vd adjustment for body composition |
π Worked example:
examples/npo_antibiotic_analysis.py
π§ Agent skills
NSForge ships 19 pre-built skills that teach agents how to use the tools β 6 NSForge workflows (nsforge-derivation-workflow, nsforge-formula-search, nsforge-verification-suite, β¦) plus 13 general development skills.
π NSForge Skills Guide
π Optional: NSForge β USolver
NSForge derives the domain-smart formula; USolver finds the math-optimal values.
flowchart LR
N["π¨ NSForge<br/>derive modified formula"] --> P["derivation_prepare_for_optimization"]
P --> U["π― USolver<br/>Z3 Β· OR-Tools Β· CVXPY"]
U --> R["optimal parameters"]π Skill:
nsforge-usolver-collab
ποΈ Architecture & development
DDD with a pure domain core and a replaceable MCP layer (nsforge core has no MCP dependency).
uv sync --all-extras # set up
uv run pytest # tests
python scripts/check.py # full harness (10 gates)
uv run nsforge-mcp # start the serverπ Architecture Β· Contributing Β· NSForge vs SymPy-MCP
πΊοΈ Roadmap
Reification-ladder phases 1β6 are live (engine β benchmarks β suggester β self-correction β provenance β explore mode). Remaining: Lean4 formal verification (optional) and multi-agent infrastructure.
π License
NSForge β Forge new formulas through verified derivation Β· Where Neural meets Symbolic
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