Add timeframe profile tests and prefer Julia MCP for testing

- Add attach_timeframe_profile! to API consistency tests (TulipaData and TulipaAsset)
- Fix missing set_partition! import in TulipaAsset API test
- Add Stage 4 to export test verifying timeframe profile CSV output
- Add test for timeframe profiles across multiple milestone years
- Mark Julia MCP as preferred test runner in CLAUDE.md

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Co-authored-by: Claude Code (claude-sonnet-4-6) <noreply@anthropic.com>

Author: abelsiqueira

CLAUDE.md

@@ -296,9 +296,11 @@ end
 
 **When testing new tests, use filtering to run only the relevant files.**
 
-#### Running Tests via [Julia MCP](https://github.com/aplavin/julia-mcp)
+#### Running Tests via [Julia MCP](https://github.com/aplavin/julia-mcp) (Preferred)
 
-When using the Julia MCP (`julia_eval`), point `env_path` at `test/` — it has its own `Project.toml` with the package registered as a path source, so no extra setup or `TestEnv` is needed. Use `@run_package_tests` directly (the CLI runner's `runtests.jl` relies on `ARGS` and won't work in a REPL):
+**When the Julia MCP (`julia_eval`) is available, always prefer it over the CLI runner for testing.** It keeps a warm Julia session, avoiding recompilation overhead on each run.
+
+Point `env_path` at `test/` — it has its own `Project.toml` with the package registered as a path source, so no extra setup or `TestEnv` is needed. Use `@run_package_tests` directly (the CLI runner's `runtests.jl` relies on `ARGS` and won't work in a REPL):
 
 ```julia
 # Run all tests

test/test-api-consistency.jl

@@ -15,6 +15,13 @@
     @test attach_milestone_data!(tulipa, "producer1", 2030) === tulipa
     @test attach_both_years_data!(tulipa, "producer1", 2030, 2030) === tulipa
     @test attach_profile!(tulipa, "producer1", :availability, 2030, rand(24)) === tulipa
+    @test attach_timeframe_profile!(
+        tulipa,
+        "producer1",
+        :max_storage_level,
+        2030,
+        rand(10),
+    ) === tulipa
 
     # attach_* functions (flow variants)
     @test attach_commission_data!(tulipa, "producer1", "consumer1", 2030) === tulipa
@@ -34,14 +41,17 @@ end
         attach_commission_data!,
         attach_milestone_data!,
         attach_both_years_data!,
-        attach_profile!
+        attach_profile!,
+        attach_timeframe_profile!,
+        set_partition!
 
     asset = TulipaAsset("test", :producer)
 
     @test attach_commission_data!(asset, 2030, capacity = 1.0) === asset
     @test attach_milestone_data!(asset, 2030, investable = true) === asset
     @test attach_both_years_data!(asset, 2030, 2030, initial_units = 1) === asset
     @test attach_profile!(asset, :availability, 2030, rand(24)) === asset
+    @test attach_timeframe_profile!(asset, :max_storage_level, 2030, rand(10)) === asset
     @test set_partition!(asset, 2030, 3, 4) === asset
     @test set_partition!(asset, 2030, 4, :explicit, "12;12") === asset
 end

test/test-output.jl

@@ -195,4 +195,23 @@ end
 
     data = create_and_get_data(tulipa, TEM.schema)
     test_that_tables_are_equivalent(data, manual_data)
+
+    ## Stage 4 - attach timeframe profile
+
+    tf_profile = collect(0.1:0.1:1.0)
+    attach_timeframe_profile!(tulipa, "ccgt", :max_storage_level, 2030, tf_profile)
+
+    push!(
+        manual_data[:assets_timeframe_profiles],
+        ("ccgt", 2030, 1, "max_storage_level", "ccgt-max_storage_level-2030-1"),
+    )
+    for (i, x) in enumerate(tf_profile)
+        push!(
+            manual_data[:profiles_timeframe],
+            ("ccgt-max_storage_level-2030-1", 2030, i, x),
+        )
+    end
+
+    data = create_and_get_data(tulipa, TEM.schema)
+    test_that_tables_are_equivalent(data, manual_data)
 end

test/test-timeframe-profiles.jl

@@ -104,3 +104,40 @@ end
     @test all(scenario1_rows.value .≈ 0.9)
     @test all(scenario2_rows.value .≈ 0.8)
 end
+
+@testitem "Timeframe profiles across multiple milestone years" tags =
+    [:unit, :fast, :schema] setup = [CommonSetup, CreateConnectionSetup, TestSchema] begin
+    using DuckDB: DuckDB
+    using DataFrames: DataFrame
+
+    tulipa = TulipaData()
+    add_asset!(tulipa, "storage", :storage)
+    attach_profile!(tulipa, "storage", :availability, 2030, ones(24))
+    attach_profile!(tulipa, "storage", :availability, 2040, ones(48))
+    attach_timeframe_profile!(tulipa, "storage", :max_storage_level, 2030, 0.9 .* ones(5))
+    attach_timeframe_profile!(tulipa, "storage", :max_storage_level, 2040, 0.7 .* ones(8))
+
+    connection = create_connection(tulipa, TestSchema.schema)
+
+    atp_df =
+        DuckDB.query(
+            connection,
+            "SELECT * FROM assets_timeframe_profiles ORDER BY milestone_year",
+        ) |> DataFrame
+    @test size(atp_df, 1) == 2
+    @test atp_df.milestone_year == [2030, 2040]
+    @test all(atp_df.asset .== "storage")
+
+    tf_df =
+        DuckDB.query(
+            connection,
+            "SELECT * FROM profiles_timeframe ORDER BY milestone_year, period",
+        ) |> DataFrame
+    @test size(tf_df, 1) == 13  # 5 periods (2030) + 8 periods (2040)
+    rows_2030 = filter(r -> r.milestone_year == 2030, tf_df)
+    rows_2040 = filter(r -> r.milestone_year == 2040, tf_df)
+    @test size(rows_2030, 1) == 5
+    @test size(rows_2040, 1) == 8
+    @test all(rows_2030.value .≈ 0.9)
+    @test all(rows_2040.value .≈ 0.7)
+end