diff --git a/.codespell/ignore_words.txt b/.codespell/ignore_words.txt
index 04b4fcf..70174b7 100644
--- a/.codespell/ignore_words.txt
+++ b/.codespell/ignore_words.txt
@@ -6,3 +6,6 @@ mater
 
 ;; Frobenius norm used in np.linalg.norm
 fro
+
+;; "number of input arguments" used in diffpy.srfit.equation.literals.Operator
+nin
diff --git a/news/refinement.rst b/news/refinement.rst
new file mode 100644
index 0000000..052eb7f
--- /dev/null
+++ b/news/refinement.rst
@@ -0,0 +1,23 @@
+**Added:**
+
+* Add more flexible interface to utilize ``diffpy.srfit``.
+
+**Changed:**
+
+* <news item>
+
+**Deprecated:**
+
+* <news item>
+
+**Removed:**
+
+* <news item>
+
+**Fixed:**
+
+* <news item>
+
+**Security:**
+
+* <news item>
diff --git a/requirements/conda.txt b/requirements/conda.txt
index 61d392d..e395a4e 100644
--- a/requirements/conda.txt
+++ b/requirements/conda.txt
@@ -6,3 +6,4 @@ pyyaml
 diffpy.srfit
 diffpy.srreal
 diffpy.structure
+networkx
diff --git a/requirements/pip.txt b/requirements/pip.txt
index 24ce15a..ab1429a 100644
--- a/requirements/pip.txt
+++ b/requirements/pip.txt
@@ -1 +1,3 @@
 numpy
+inline-snapshot
+"mcp[cli]"
diff --git a/src/diffpy/__init__.py b/src/diffpy/__init__.py
deleted file mode 100644
index 2d7de20..0000000
--- a/src/diffpy/__init__.py
+++ /dev/null
@@ -1,14 +0,0 @@
-#!/usr/bin/env python
-##############################################################################
-#
-# (c) 2026 The Trustees of Columbia University in the City of New York.
-# All rights reserved.
-#
-# File coded by: Billinge Group members and community contributors.
-#
-# See GitHub contributions for a more detailed list of contributors.
-# https://github.com/diffpy/diffpy.apps/graphs/contributors
-#
-# See LICENSE.rst for license information.
-#
-##############################################################################
diff --git a/src/diffpy/apps/pdfadapter.py b/src/diffpy/apps/pdfadapter.py
index 5ea27ac..dc9b850 100644
--- a/src/diffpy/apps/pdfadapter.py
+++ b/src/diffpy/apps/pdfadapter.py
@@ -3,8 +3,6 @@
 from pathlib import Path
 
 import numpy
-from scipy.optimize import least_squares
-
 from diffpy.srfit.fitbase import (
     FitContribution,
     FitRecipe,
@@ -14,6 +12,7 @@
 from diffpy.srfit.pdf import PDFGenerator, PDFParser
 from diffpy.srfit.structure import constrainAsSpaceGroup
 from diffpy.structure.parsers import getParser
+from scipy.optimize import least_squares
 
 
 class PDFAdapter:
diff --git a/src/diffpy/apps/refinebase/__init__.py b/src/diffpy/apps/refinebase/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/src/diffpy/apps/refinebase/parametric_model.py b/src/diffpy/apps/refinebase/parametric_model.py
new file mode 100644
index 0000000..6b892b2
--- /dev/null
+++ b/src/diffpy/apps/refinebase/parametric_model.py
@@ -0,0 +1,141 @@
+from collections import OrderedDict
+
+import networkx as nx
+import numpy
+from diffpy.srfit.equation.literals import Operator, makeOperator
+from diffpy.srfit.fitbase import FitContribution, Profile
+from diffpy.srfit.pdf.pdfgenerator import PDFGenerator
+from diffpy.structure import Structure
+
+
+class ParametricModel:
+    # TODO: add constraints and restraints method
+    def __init__(self, name):
+        self.meta = OrderedDict()
+        self.name = name
+        self._contribution = FitContribution(name)
+        self._graph = nx.DiGraph()
+        # NOTE: all submodels will share the same profile
+        self._submodels = []
+        old_validate = self._contribution._validate
+        self._contribution._validate = lambda: self.validate(old_validate)
+
+    def validate(self, old_validate):
+        # must provide placeholders to pass the validation
+        if self._contribution.profile is None:
+            placeholder_profile = Profile()
+            placeholder_profile.setObservedProfile(
+                numpy.arange(100), numpy.arange(100)
+            )
+            self.set_profile(placeholder_profile)
+        if self._contribution._eq is None:
+            placeholder_equation = makeOperator(
+                name=self.pdf_generator.name,
+                symbol="g",
+                operation=self.pdf_generator.operation,
+                nin=0,  # NOTE: make sure nin=0 and nout=1 is correct
+                nout=1,
+            )
+            self._contribution.setEquation("g", ns={"g": placeholder_equation})
+        old_validate()
+
+    def _construct_parameter_graph(self, parameterset, prefix=""):
+        parent_name = f"{prefix}{parameterset.name}"
+        self._graph.add_node(parent_name, parameter=parameterset)
+
+        for par in parameterset._iterManaged():
+            child_name = f"{parent_name}.{par.name}"
+            self._graph.add_node(child_name, parameter=par)
+            self._graph.add_edge(parent_name, child_name)
+            if hasattr(par, "_iterManaged"):
+                self._construct_parameter_graph(
+                    par,
+                    prefix=f"{parent_name}.",
+                )
+
+    def register_submodel(self, symbol, model):
+        """Register a parametric model to the current model."""
+        op = model.evaluate
+        if not isinstance(model.evaluate, Operator):
+            op = makeOperator(
+                name=model.name,
+                symbol=symbol,
+                operation=model.evaluate,
+                nin=0,  # NOTE: make sure nin=0 and nout=1 is correct
+                nout=1,
+            )
+        self._contribution._eqfactory.registerOperator(symbol, op)
+        # Allow iterPars to traverse the submodel's parameters
+        self._contribution.addParameterSet(model._contribution)
+        self._submodels.append(model)
+
+    @property
+    def parameters(self):
+        return {
+            par_node_id: self._graph.nodes[par_node_id]["parameter"]
+            for par_node_id in self._graph.nodes
+            if self._graph.out_degree(par_node_id) == 0
+        }
+
+    def set_profile(self, profile):
+        self._contribution.setProfile(profile)
+        if hasattr(self, "pdf_generator"):
+            self.pdf_generator.setProfile(profile)
+        for submodel in self._submodels:
+            submodel.set_profile(profile)
+
+    def set_equation(self, equation_str, ns={}):
+        self._contribution.setEquation(equation_str, ns=ns)
+
+    def get_equation(self):
+        return self._contribution.getEquation()
+
+    def add_parameter(self, parameter):
+        self._contribution.addParameter(parameter)
+
+    def remove_parameter(self, parameter):
+        self._contribution.removeParameter(parameter)
+
+    def add_parameter_set(self, parameterset):
+        self._contribution.addParameterSet(parameterset)
+
+    def remove_parameter_set(self, parameterset):
+        self._contribution.removeParameterSet(parameterset)
+
+    def prepare(self):
+        self._graph.clear()
+        self._construct_parameter_graph(self._contribution, prefix="")
+
+    def evaluate(self):
+        return self._contribution._eq()
+
+    def residual(self):
+        # TODO: Implement residual calculation for the model
+        pass
+
+
+class ParametricModelPDF(ParametricModel):
+    def __init__(self, name, structure: Structure, meta=None):
+        super().__init__(name=name)
+        self._initialize_pdf_generator(structure, meta)
+
+    def _initialize_pdf_generator(self, structure: Structure, meta: dict):
+        self.pdf_generator = PDFGenerator(self.name)
+        self.pdf_generator.setStructure(structure)
+        self._contribution.addParameterSet(self.pdf_generator)
+        self._contribution._RecipeContainer__managed = (
+            self.pdf_generator._RecipeContainer__managed
+        )
+        self._contribution._parameters = self.pdf_generator._parameters
+
+        if meta is not None:
+            self.processMetaData(meta)
+
+    def processMetaData(self, meta: dict = None):
+        self.pdf_generator.meta.update(meta)
+        self.pdf_generator.processMetaData()
+
+    def evaluate(self):
+        if self._contribution.profile is None:
+            raise ValueError("Profile is not set for the PDF model.")
+        return self.pdf_generator.operation()
diff --git a/src/diffpy/apps/refinebase/refinement_server.py b/src/diffpy/apps/refinebase/refinement_server.py
new file mode 100644
index 0000000..70437f5
--- /dev/null
+++ b/src/diffpy/apps/refinebase/refinement_server.py
@@ -0,0 +1,248 @@
+import uuid
+from typing import Annotated
+
+from mcp.server import MCPServer
+
+from diffpy.apps.refinebase.refinement_session import RefinementSession
+
+session = RefinementSession()
+mcp = MCPServer("diffpy.apps")
+
+
+@mcp.tool()
+async def add_pdf_profile(
+    profile_path: Annotated[str, "Path to the PDF profile file"],
+    profile_name: Annotated[str, "Unique name for the profile"] = uuid.uuid4(),
+) -> str:
+    """Add a pdf profile to the refinement session."""
+    from diffpy.apps.refinebase.util import (
+        get_pdf_profile,
+    )
+
+    profile = get_pdf_profile(profile_path)
+    session.add_profile(profile, profile_name=profile_name)
+    return f"Profile {profile_name} added successfully."
+
+
+@mcp.tool()
+async def add_dat_profile(
+    profile_path: Annotated[str, "Path to the .dat profile file"],
+    profile_name: Annotated[str, "Unique name for the profile"] = uuid.uuid4(),
+) -> str:
+    """Add a profile to the refinement session.
+
+    The profile should satisfy that
+    xarray, yarray = np.loadtxt(profile_path)"""
+    from diffpy.apps.refinebase.util import (
+        get_dat_profile,
+    )
+
+    profile = get_dat_profile(profile_path)
+    session.add_profile(profile, profile_name=profile_name)
+    return f"Profile {profile_name} added successfully."
+
+
+@mcp.tool()
+def add_text_profile(
+    xarray: Annotated[list, "X-values of the profile"],
+    yarray: Annotated[list, "Y-values of the profile"],
+    dx: Annotated[list, "Uncertainties in the x-values"] = None,
+    dy: Annotated[list, "Uncertainties in the y-values"] = None,
+    profile_name: Annotated[str, "Unique name for the profile"] = uuid.uuid4(),
+) -> str:
+    """Add a profile to the refinement session.
+
+    The profile is constructed from the provided xarray and yarray data."""
+    from diffpy.apps.refinebase.util import (
+        get_text_profile,
+    )
+
+    profile = get_text_profile(xarray, yarray, dx=dx, dy=dy)
+    session.add_profile(profile, profile_name=profile_name)
+    return f"Profile {profile_name} added successfully."
+
+
+@mcp.tool()
+async def add_pdf_model(
+    structure_path: Annotated[str, "Path to the structure file"],
+    model_name: Annotated[str, "Name of the parametric model"] = uuid.uuid4(),
+) -> str:
+    """Add a PDF parametric model to the refinement session."""
+    from diffpy.apps.refinebase.util import get_pdf_model
+
+    pdf_model = get_pdf_model(structure_path=structure_path, name=model_name)
+    pdf_model.prepare()
+    session.add_model(pdf_model)
+    return f"Model {pdf_model.name} added successfully."
+
+
+@mcp.tool()
+async def add_equation_model(
+    equation: Annotated[str, "Equation for the parametric model"],
+    model_name: Annotated[str, "Name of the parametric model"] = uuid.uuid4(),
+) -> str:
+    """Add an equation-based parametric model to the refinement session."""
+    from diffpy.apps.refinebase.parametric_model import ParametricModel
+
+    equation_model = ParametricModel(name=model_name)
+    equation_model.set_equation(equation)
+    equation_model.prepare()
+    session.add_model(equation_model)
+    return f"Model {equation_model.name} added successfully."
+
+
+@mcp.tool()
+async def list_profiles() -> list[str]:
+    """List all profiles in the refinement session."""
+    return [str(profile_id) for profile_id in session.profiles.keys()]
+
+
+@mcp.tool()
+async def list_models() -> list[str]:
+    """List all models in the refinement session."""
+    return [str(model_id) for model_id in session.models.keys()]
+
+
+@mcp.tool()
+async def set_model_equation(
+    model_name: Annotated[str, "Name of the parametric model"],
+    equation: Annotated[str, "Equation to set for the parametric model"],
+) -> str:
+    """Set or change the equation for a specific parametric model."""
+    if model_name not in session.models:
+        raise ValueError(f"Model with ID {model_name} does not exist.")
+
+    model = session.models[model_name]
+    model.set_equation(equation)
+    model.prepare()  # Re-prepare the model after changing the equation
+
+    return f"Equation for model {model_name} set successfully."
+
+
+@mcp.tool()
+async def combine_models(
+    parent_model_name: Annotated[str, "Name of the parent parametric model"],
+    child_model_name: Annotated[str, "Name of the child parametric model"],
+    symbol: Annotated[
+        str, "Symbol to use for child model in the parent model's equation"
+    ],
+) -> str:
+    """
+    Combine two parametric models by registering the child to the parent model.
+    """
+    if parent_model_name not in session.models:
+        raise ValueError(
+            f"Parent model with ID {parent_model_name} does not exist."
+        )
+
+    if child_model_name not in session.models:
+        raise ValueError(
+            f"Child model with ID {child_model_name} does not exist."
+        )
+
+    parent_model = session.models[parent_model_name]
+    child_model = session.models[child_model_name]
+
+    parent_model.register_submodel(symbol, child_model)
+    parent_model.prepare()
+
+    return (
+        f"Models {parent_model_name} and "
+        f"{child_model_name} combined successfully."
+    )
+
+
+@mcp.tool()
+async def set_model_param_value(
+    param_name: Annotated[str, "Name of the parameter to set"],
+    value: Annotated[float, "Value to set for the parameter"],
+) -> str:
+    """
+    Set the value of a specific parameter in a parametric model.
+    """
+    from diffpy.apps.refinebase.util import get_variable
+
+    variable = get_variable(session.models, param_name)
+    variable.setValue(value)
+
+    return f"Parameter '{param_name}' is set to {value}."
+
+
+@mcp.tool()
+async def list_model_parameters(
+    model_name: Annotated[str, "Name of the parametric model"],
+) -> str:
+    """
+    List all parameters of a specific parametric model.
+    """
+    if model_name not in session.models:
+        raise ValueError(f"Model with ID {model_name} does not exist.")
+
+    model = session.models[model_name]
+    parameters = {
+        node_id: par.value for node_id, par in model.parameters.items()
+    }
+    return f"Parameters for model '{model_name}': {parameters}"
+
+
+@mcp.tool()
+async def get_model_value(
+    model_name: Annotated[str, "Name of the parametric model"],
+) -> str:
+    """
+    Get the value of a specific parametric model.
+    """
+    if model_name not in session.models:
+        raise ValueError(f"Model with ID {model_name} does not exist.")
+
+    model = session.models[model_name]
+    value = model.evaluate()
+
+    return f"Value for model '{model_name}': {value}"
+
+
+@mcp.tool()
+async def refine(
+    profile_names: Annotated[
+        list[str], "List of profile IDs to use in the refinement"
+    ],
+    model_names: Annotated[
+        list[str], "List of model IDs to use in the refinement"
+    ],
+    variable_names: Annotated[list[str], "List of variable names to refine"],
+    weights: Annotated[list[float], "List of weights for each profile"] = None,
+    initial_values: Annotated[
+        list[float], "List of initial values for each variable"
+    ] = None,
+) -> str:
+    """
+    Perform a refinement using the specified profiles, models, and variables.
+    """
+    # Retrieve profiles and models from session using provided IDs
+    from diffpy.apps.refinebase.util import get_variable
+
+    profile_objs = [session.profiles[pid] for pid in profile_names]
+    model_objs = [session.models[mid] for mid in model_names]
+    variable_objs = [
+        get_variable(session.models, var_name) for var_name in variable_names
+    ]
+
+    # Perform refinement
+    session.solve(
+        profile_objs,
+        model_objs,
+        variable_objs,
+        weights=weights,
+        initial_values=initial_values,
+    )
+
+    # Collect refined variable values
+    refined_values = {var.name: var.value for var in variable_objs}
+
+    return (
+        f"Refinement completed successfully. Refined values: {refined_values}"
+    )
+
+
+if __name__ == "__main__":
+    mcp.run()
diff --git a/src/diffpy/apps/refinebase/refinement_session.py b/src/diffpy/apps/refinebase/refinement_session.py
new file mode 100644
index 0000000..585201d
--- /dev/null
+++ b/src/diffpy/apps/refinebase/refinement_session.py
@@ -0,0 +1,61 @@
+import uuid
+from collections import OrderedDict
+
+import numpy
+from diffpy.srfit.fitbase import (
+    FitRecipe,
+    Profile,
+)
+from scipy.optimize import leastsq
+
+from diffpy.apps.refinebase.parametric_model import (
+    ParametricModel,
+)
+
+
+class RefinementSession:
+    def __init__(self):
+        self.recipes = OrderedDict()
+        self.profiles = OrderedDict()
+        self.models = OrderedDict()
+
+    def add_profile(self, profile: Profile, profile_name: str = None):
+        if profile in self.profiles.values():
+            raise ValueError("Profile already exists in the session.")
+        if profile_name is None:
+            profile_name = str(uuid.uuid4())
+        self.profiles[profile_name] = profile
+
+    def add_model(self, model: ParametricModel):
+        if model in self.models.values():
+            raise ValueError("Model already exists in the session.")
+        self.models[model.name] = model
+
+    def solve(
+        self,
+        profiles,
+        models,
+        variables,
+        id=uuid.uuid4(),
+        weights=None,
+        initial_values=None,
+    ):
+        recipe = FitRecipe()
+        self.recipes[id] = recipe
+        if weights is None:
+            weights = numpy.ones(len(profiles)) / len(profiles)
+        for i in range(len(models)):
+            models[i].set_profile(profiles[i])
+            recipe.addContribution(models[i]._contribution, weight=weights[i])
+        # Add variables
+        if initial_values is not None:
+            for var, val in zip(variables, initial_values):
+                var.value = val
+        for var in variables:
+            recipe.addVar(var)
+        # Refine the recipe
+        recipe.fix("all")
+        recipe.residual()
+        for i in range(len(variables)):
+            recipe.free(variables[i].name)
+            leastsq(recipe.residual, recipe.getValues())
diff --git a/src/diffpy/apps/refinebase/util.py b/src/diffpy/apps/refinebase/util.py
new file mode 100644
index 0000000..2198a92
--- /dev/null
+++ b/src/diffpy/apps/refinebase/util.py
@@ -0,0 +1,58 @@
+def get_pdf_profile(profile_path: str):
+    from diffpy.srfit.fitbase import Profile
+    from diffpy.srfit.pdf import PDFParser
+
+    profile = Profile()
+    parser = PDFParser()
+    parser.parseFile(profile_path)
+    profile.loadParsedData(parser)
+    return profile
+
+
+def get_dat_profile(profile_path: str):
+    from diffpy.srfit.fitbase import Profile
+
+    profile = Profile()
+    profile.loadtxt(profile_path)
+
+    return profile
+
+
+def get_text_profile(xarray, yarray, dx=None, dy=None):
+    from diffpy.srfit.fitbase import Profile
+
+    profile = Profile()
+    profile.setObservedProfile(xarray, yarray, dx=dx, dy=dy)
+
+    return profile
+
+
+def get_pdf_model(structure_path: str, name="pdf"):
+    from diffpy.structure import Structure
+
+    from diffpy.apps.refinebase.parametric_model import ParametricModelPDF
+
+    stru = Structure()
+    stru.read(structure_path)
+    pdf_model = ParametricModelPDF(name, structure=stru)
+    return pdf_model
+
+
+def get_variable(models_dict, variable_name):
+    objs = variable_name.split(".")
+    if objs[0] not in models_dict:
+        raise ValueError(f"Model '{objs[0]}' not found in the session.")
+    if variable_name not in models_dict[objs[0]].parameters:
+        raise ValueError(
+            f"Variable '{variable_name}' not found in the model '{objs[0]}'."
+        )
+
+    return models_dict[objs[0]].parameters[variable_name]
+
+
+# if __name__ == "__main__":
+# import numpy as np
+# xarray = np.linspace(-2*np.pi, 2*np.pi, 400)
+# yarray = np.sin(xarray) + 0.05*np.random.normal(size=len(xarray))
+# X = np.stack((xarray,yarray), axis=1)
+# np.savetxt("sine.dat",X)
diff --git a/tests/conftest.py b/tests/conftest.py
index e3b6313..d43dcdf 100644
--- a/tests/conftest.py
+++ b/tests/conftest.py
@@ -17,3 +17,17 @@ def user_filesystem(tmp_path):
         json.dump(home_config_data, f)
 
     yield tmp_path
+
+
+@pytest.fixture
+def nested_sine_model():
+    from diffpy.apps.refinebase.parametric_model import ParametricModel
+
+    model = ParametricModel("main")
+    submodel = ParametricModel("sub")
+    submodel.set_equation("a*x")
+    model.register_submodel("u", submodel)
+    model.set_equation("A*sin(u)")
+    submodel.prepare()
+    model.prepare()
+    return model, submodel
diff --git a/tests/data/sine.dat b/tests/data/sine.dat
new file mode 100644
index 0000000..5c4e92c
--- /dev/null
+++ b/tests/data/sine.dat
@@ -0,0 +1,400 @@
+-6.283185307179586232e+00 5.333472993497439391e-02
+-6.251690643985703844e+00 4.730056680876025543e-02
+-6.220195980791820567e+00 6.674570663149917427e-02
+-6.188701317597938178e+00 8.163496149342541752e-02
+-6.157206654404055790e+00 5.765535646179940910e-02
+-6.125711991210173402e+00 1.362339072376442528e-01
+-6.094217328016290125e+00 1.220400021248826683e-01
+-6.062722664822407737e+00 2.071501486063853370e-01
+-6.031228001628525348e+00 2.171193621378014194e-01
+-5.999733338434642960e+00 2.591809174618869283e-01
+-5.968238675240759683e+00 2.440214685406498218e-01
+-5.936744012046877295e+00 3.844927167452896200e-01
+-5.905249348852994906e+00 3.332980129599112384e-01
+-5.873754685659111630e+00 3.730055499203158154e-01
+-5.842260022465229241e+00 4.607970012517633718e-01
+-5.810765359271346853e+00 4.532285005410850998e-01
+-5.779270696077464464e+00 5.173537937900670736e-01
+-5.747776032883581188e+00 5.883332618147742998e-01
+-5.716281369689698799e+00 5.913440547536299441e-01
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diff --git a/tests/helper.py b/tests/helper.py
index b4b1a4f..f557162 100644
--- a/tests/helper.py
+++ b/tests/helper.py
@@ -1,5 +1,4 @@
 import numpy as np
-
 from diffpy.srfit.fitbase import FitContribution, FitRecipe, Profile
 from diffpy.srfit.pdf import PDFGenerator, PDFParser
 from diffpy.srfit.structure import constrainAsSpaceGroup
diff --git a/tests/test_parametric_model.py b/tests/test_parametric_model.py
new file mode 100644
index 0000000..ee1335a
--- /dev/null
+++ b/tests/test_parametric_model.py
@@ -0,0 +1,48 @@
+import numpy
+import pytest
+
+
+def test_parametric_model_graph(nested_sine_model):
+    model, submodel = nested_sine_model
+    # C1: Create a nested parametric model
+    # Expect the graph to be constructed correctly
+    expected_parameters = ["main.A", "main.sub.a", "main.sub.x"]
+    actual_parameters = list(model.parameters.keys())
+    assert set(expected_parameters) == set(actual_parameters)
+    expected_nodes = ["main", "main.A", "main.sub", "main.sub.a", "main.sub.x"]
+    actual_nodes = list(model._graph.nodes)
+    assert set(expected_nodes).issubset(set(actual_nodes))
+    expected_edges = [
+        ("main", "main.A"),
+        ("main", "main.sub"),
+        ("main.sub", "main.sub.a"),
+        ("main.sub", "main.sub.x"),
+    ]
+    actual_edges = list(model._graph.edges)
+    assert set(expected_edges) == set(actual_edges)
+
+
+def test_parametric_model_parameter_access(nested_sine_model):
+    # C1: Create a nested sine parametric model
+    # Expect to models to share the same parameter obj
+    model, submodel = nested_sine_model
+    assert model.parameters["main.sub.a"] is submodel.parameters["sub.a"]
+
+
+@pytest.mark.parametrize(
+    "A, a, x, expected",
+    [
+        (1.0, 1.0, numpy.pi / 2, 1.0),
+        (2.0, 2.0, numpy.pi / 4, 2.0),
+        (1.0, 1.0, numpy.pi, 0.0),
+    ],
+)
+def test_parametric_model_evaluation(nested_sine_model, A, a, x, expected):
+    # C1: Create a nested sine parametric model
+    # Expect the model to evaluate correctly
+    model, submodel = nested_sine_model
+    model.parameters["main.A"].value = A
+    submodel.parameters["sub.a"].value = a
+    submodel.parameters["sub.x"].value = x
+    actual = model.evaluate()
+    assert numpy.isclose(actual, expected, rtol=1e-6)
diff --git a/tests/test_refinement_server.py b/tests/test_refinement_server.py
new file mode 100644
index 0000000..e803f7e
--- /dev/null
+++ b/tests/test_refinement_server.py
@@ -0,0 +1,58 @@
+import numpy
+import pytest
+from mcp import Client
+
+from diffpy.apps.refinebase.refinement_server import mcp
+from diffpy.apps.refinebase.util import get_variable
+
+
+@pytest.fixture
+def anyio_backend():
+    return "asyncio"
+
+
+@pytest.mark.anyio
+async def test_refine_sine():
+    # C1: Set up the MCP client and do a sine refinement
+    #   Expect all objs are created and refined successfully
+    from diffpy.apps.refinebase.refinement_server import session
+
+    async with Client(mcp, raise_exceptions=True) as mcp_client:
+        await mcp_client.call_tool(
+            "add_dat_profile",
+            {
+                "profile_name": "sine_profile",
+                "profile_path": "tests/data/sine.dat",
+            },
+        )
+        assert "sine_profile" in session.profiles
+        await mcp_client.call_tool(
+            "add_equation_model",
+            {
+                "model_name": "sine_model",
+                "equation": "A*sin(x)",
+            },
+        )
+        assert "sine_model" in session.models
+        await mcp_client.call_tool(
+            "set_model_param_value",
+            {
+                "param_name": "sine_model.A",
+                "value": 0.8,
+            },
+        )
+        variable_A = get_variable(session.models, "sine_model.A")
+        expected_value = 0.8
+        actual_value = variable_A.value
+        assert actual_value == expected_value
+        await mcp_client.call_tool(
+            "refine",
+            {
+                "profile_names": ["sine_profile"],
+                "model_names": ["sine_model"],
+                "variable_names": ["sine_model.A"],
+            },
+        )
+        expected_value = 1.0  # The expected value of A after refinement
+        actual_value = variable_A.value
+        assert numpy.isclose(actual_value, expected_value, rtol=0.2)
diff --git a/tests/test_refinement_session.py b/tests/test_refinement_session.py
new file mode 100644
index 0000000..e73e652
--- /dev/null
+++ b/tests/test_refinement_session.py
@@ -0,0 +1,75 @@
+import numpy
+from diffpy.srfit.fitbase import (
+    Profile,
+)
+from diffpy.srfit.pdf import PDFParser
+from diffpy.structure import Structure
+
+from diffpy.apps.refinebase.parametric_model import (
+    ParametricModel,
+    ParametricModelPDF,
+)
+from diffpy.apps.refinebase.refinement_session import RefinementSession
+
+
+def test_refine_sine():
+    # C1: Refinement session without additional calculator or functions
+    session = RefinementSession()
+    xobs = numpy.linspace(-numpy.pi, numpy.pi, 100)
+    yobs = numpy.sin(xobs) + 1e-2 * numpy.random.normal(size=xobs.shape)
+    sine_profile = Profile()
+    sine_profile.setObservedProfile(xobs, yobs)
+    sine_model = ParametricModel("sine_model")
+    sine_model.set_equation("A*sin(a*x)")
+    sine_model.prepare()
+    session.solve(
+        profiles=[sine_profile],
+        models=[sine_model],
+        variables=[
+            sine_model.parameters["sine_model.A"],
+            sine_model.parameters["sine_model.a"],
+        ],
+        initial_values=[0.5, 0.5],
+    )
+    assert numpy.isclose(
+        sine_model.parameters["sine_model.A"].value,
+        1.0,
+        rtol=1e-2,
+    )
+    assert numpy.isclose(
+        sine_model.parameters["sine_model.a"].value,
+        1.0,
+        rtol=1e-2,
+    )
+
+
+def test_refine_ni():
+    # C1: Refinement session with one PDFCalculator
+    profile_path = "tests/data/Ni.gr"
+    profile = Profile()
+    parser = PDFParser()
+    parser.parseFile(profile_path)
+    profile.loadParsedData(parser)
+    profile.setCalculationRange(xmax=20)
+    stru = Structure()
+    structure_path = "tests/data/Ni.cif"
+    stru.read(structure_path)
+
+    pdf_model = ParametricModelPDF("pdf", structure=stru, meta=profile.meta)
+    pdf_model.prepare()
+    ni_model = ParametricModel("ni_model")
+    ni_model.register_submodel("g", pdf_model)
+    ni_model.set_equation("s*g")
+    ni_model.prepare()
+
+    ni_model.parameters["ni_model.s"].value = 1.0
+
+    session = RefinementSession()
+    session.solve(
+        profiles=[profile],
+        models=[ni_model],
+        variables=[
+            ni_model.parameters["ni_model.s"],
+            pdf_model.parameters["pdf.phase.lattice.a"],
+        ],
+    )