diff --git a/spatialagent/tool/databases.py b/spatialagent/tool/databases.py
index 377f835..98321e4 100644
--- a/spatialagent/tool/databases.py
+++ b/spatialagent/tool/databases.py
@@ -161,14 +161,21 @@ def search_panglao(
 
 @tool
 def search_czi_datasets(
-    query: Annotated[str, Field(description="Query describing tissue, condition, and organism (e.g., 'liver, breast cancer, Homo sapiens')")],
+    query: Annotated[str, Field(description="Query describing the condition or context (e.g., 'breast cancer', 'normal development'). Do NOT include organism or tissue here — use the dedicated parameters instead.")],
     n_datasets: Annotated[int, Field(description="Number of top datasets to return")] = 1,
-    organism: Annotated[str, Field(description="Filter by organism (e.g., 'Mus musculus', 'Homo sapiens'). Optional.")] = None,
-    tissue: Annotated[str, Field(description="Filter by tissue keyword (e.g., 'lung', 'brain'). Matches against tissue and tissue_general columns. Optional.")] = None,
+    organism: Annotated[str, Field(description="Filter by organism (e.g., 'Mus musculus', 'Homo sapiens'). Always pass this when the organism is known.")] = None,
+    tissue: Annotated[str, Field(description="Filter by tissue keyword (e.g., 'lung', 'brain'). Matches against tissue and tissue_general columns. Always pass this when the tissue is known.")] = None,
 ) -> str:
     """Search CZI CELLxGENE Census for reference single-cell datasets.
 
-    Input is a string containing: tissue, condition, and organism.
+    When the query mentions an organism or tissue, pass them as the dedicated
+    ``organism`` and ``tissue`` parameters so they are used as hard pre-filters
+    before embedding-based ranking. The ``query`` should contain only the
+    condition or context (e.g., 'breast cancer', 'normal development').
+
+    If strict filtering returns fewer than ``n_datasets`` results, the function
+    progressively relaxes filters (drop tissue first, then organism) and
+    includes a warning in the output.
 
     Returns:
         str: Formatted string with dataset info. Example output:
@@ -206,22 +213,55 @@ def search_czi_datasets(
 
     df = pd.read_csv(metadata_path)
 
-    # Pre-filter by organism and tissue before embedding search
-    if organism:
-        mask = df["organism"] == organism
-        df_filtered = df[mask]
-        if len(df_filtered) >= n_datasets:
-            df = df_filtered
-
-    if tissue:
-        tissue_lower = tissue.lower()
-        mask = (
-            df["tissue"].str.lower().str.contains(tissue_lower, na=False) |
-            df["tissue_general"].str.lower().str.contains(tissue_lower, na=False)
-        )
-        df_filtered = df[mask]
-        if len(df_filtered) >= n_datasets:
-            df = df_filtered
+    # Pre-filter by organism and tissue with controlled relaxation
+    filter_warnings = []
+    applied_organism = None
+    applied_tissue = None
+
+    if organism or tissue:
+        # Try strict filter: both organism and tissue
+        df_strict = df
+        if organism:
+            df_strict = df_strict[df_strict["organism"] == organism]
+        if tissue:
+            tissue_lower = tissue.lower()
+            df_strict = df_strict[
+                df_strict["tissue"].str.lower().str.contains(tissue_lower, na=False)
+                | df_strict["tissue_general"].str.lower().str.contains(tissue_lower, na=False)
+            ]
+
+        if len(df_strict) >= n_datasets:
+            df = df_strict
+            applied_organism = organism
+            applied_tissue = tissue
+        elif organism and tissue:
+            # Relax: drop tissue, keep organism only
+            df_org_only = df[df["organism"] == organism]
+            if len(df_org_only) >= n_datasets:
+                df = df_org_only
+                applied_organism = organism
+                filter_warnings.append(
+                    f"Note: Only {len(df_strict)} datasets matched "
+                    f"organism='{organism}' AND tissue='{tissue}' "
+                    f"(fewer than {n_datasets} requested). "
+                    f"Relaxed to organism='{organism}' only "
+                    f"({len(df_org_only)} datasets)."
+                )
+            else:
+                filter_warnings.append(
+                    f"Note: Only {len(df_org_only)} datasets matched "
+                    f"organism='{organism}' (fewer than {n_datasets} "
+                    f"requested). Using all {len(df)} datasets."
+                )
+        else:
+            # Single filter with too few results
+            filter_name = "organism" if organism else "tissue"
+            filter_val = organism if organism else tissue
+            filter_warnings.append(
+                f"Note: Only {len(df_strict)} datasets matched "
+                f"{filter_name}='{filter_val}' (fewer than {n_datasets} "
+                f"requested). Using all {len(df)} datasets."
+            )
 
     df = df.reset_index(drop=True)
 
@@ -239,12 +279,12 @@ def search_czi_datasets(
     query_embedding = _embed_with_retry(llm_embed_query, [query])
 
     # Check for cached description embeddings (use effective_model for cache key)
-    # Include filter params in database identifier to avoid cache collisions
+    # Include actually-applied filters in identifier to avoid cache collisions
     db_id = "czi_census"
-    if organism:
-        db_id += f"_{organism.replace(' ', '_')}"
-    if tissue:
-        db_id += f"_{tissue.lower()}"
+    if applied_organism:
+        db_id += f"_{applied_organism.replace(' ', '_')}"
+    if applied_tissue:
+        db_id += f"_{applied_tissue.lower()}"
     cache_key = _get_cache_key(db_id, effective_model, len(descriptions))
     desc_embeddings = _load_cached_embeddings(cache_key)
 
@@ -276,6 +316,8 @@ def search_czi_datasets(
         results.append(result_str)
 
     output = "\n".join(results)
+    if filter_warnings:
+        output = "\n".join(filter_warnings) + "\n\n" + output
     return output