Simplified docstrings

.gitignore

@@ -58,6 +58,9 @@ coverage.xml
 .ruff_cache
 htmlcov
 
+# Ignore Claude Code
+.claude
+
 # Keep folders indicated by .gitkeep
 !/**/.gitkeep
 !/**/.gitignore

src/biocontext_kb/core/alphafold/_get_alphafold_info_by_protein_symbol.py

@@ -14,24 +14,16 @@
 
 @core_mcp.tool()
 def get_alphafold_info_by_protein_symbol(
-    protein_symbol: Annotated[str, Field(description="The name of the gene to search for (e.g., 'SYNPO')")],
+    protein_symbol: Annotated[str, Field(description="Gene/protein name (e.g., 'SYNPO')")],
     species: Annotated[
         str,
-        Field(description="The organism ID (e.g., '9606' for human)"),
+        Field(description="Taxonomy ID (e.g., '9606' for human)"),
     ] = "9606",
 ) -> dict:
-    """Query the AlphaFold database for the protein structure information using the protein name.
-
-    This function constructs a query URL to fetch data from the AlphaFold database based on the provided protein name.
-    The response contains links to the PDB and CIF files for the protein structure, as well as general information about
-    the protein.
-
-    Args:
-        protein_symbol (Annotated[str, Field, optional): The name of the protein to search for (e.g., "SYNPO").
-        species (str): The organism ID (e.g., "9606" for human). Default is "9606".
+    """Query AlphaFold database using protein name. First converts protein symbol to UniProt ID, then fetches structure predictions.
 
     Returns:
-        dict: Protein structure information or an error message.
+        dict: AlphaFold prediction data including PDB/CIF file URLs, confidence scores, and metadata or error message.
     """
     # Get the UniProt Id from the protein_symbol
     try:

src/biocontext_kb/core/alphafold/_get_alphafold_info_by_uniprot_id.py

@@ -6,15 +6,12 @@
 
 
 def get_alphafold_info_by_uniprot_id(
-    uniprot_id: Annotated[str, Field(description="The UniProt ID of the protein (e.g., 'P62258')")],
+    uniprot_id: Annotated[str, Field(description="UniProt protein ID (e.g., 'P62258')")],
 ) -> dict:
-    """Query the AlphaFold database for the protein structure information using the UniProt ID.
-
-    Args:
-        uniprot_id (str): The UniProt ID of the protein (e.g., "P62258").
+    """Query AlphaFold database for protein structure data.
 
     Returns:
-        dict: Protein structure information or an error message.
+        dict: AlphaFold prediction data including PDB/CIF file URLs, confidence scores, and metadata or error message.
     """
     # Ensure the UniProt ID is in uppercase
     uniprot_id = uniprot_id.upper()

src/biocontext_kb/core/antibodyregistry/_get_antibody_information.py

@@ -8,27 +8,12 @@
 
 @core_mcp.tool()
 def get_antibody_information(
-    ab_id: Annotated[str, Field(description="Antibody ID from the Antibody Registry (e.g., '3643095')")],
+    ab_id: Annotated[str, Field(description="Antibody Registry ID (e.g., '3643095')")],
 ) -> dict:
-    """Get detailed information for a specific antibody by its ID.
-
-    This function retrieves comprehensive information about a single antibody from the Antibody Registry
-    using its unique antibody ID (abId). The antibody ID is typically obtained from the results of
-    get_antibody_list() function, where each antibody entry contains an 'abId' field that can be used
-    with this function to get detailed information.
-
-    Note: Some information provided by the Antibody Registry is for non-commercial use only.
-    Users should refer to antibodyregistry.org for complete terms of use and licensing details.
-
-    Args:
-        ab_id (str): The unique antibody ID from the Antibody Registry. This is typically obtained
-                    from the 'abId' field in the results of get_antibody_list(), unless the ID
-                    is directly provided by the user.
+    """Get detailed antibody information by ID. Retrieves catalog number, vendor, clonality, epitope, applications, and more.
 
     Returns:
-        dict: Detailed antibody information including catalog number, vendor, clonality, epitope,
-              applications, target species, isotype, source organism, citations, and other metadata,
-              or error message if the request fails.
+        dict: Antibody details including abId, catalog numbers, vendor, clonality, epitope, applications, target species, isotype, citations or error message.
     """
     ab_id = ab_id.strip()
     if not ab_id:

src/biocontext_kb/core/antibodyregistry/_get_antibody_list.py

@@ -8,25 +8,12 @@
 
 @core_mcp.tool()
 def get_antibody_list(
-    search: Annotated[
-        str, Field(description="Search term for antibodies (e.g., gene symbol, protein name, UniProt ID)")
-    ],
+    search: Annotated[str, Field(description="Gene symbol, protein name, or UniProt ID (e.g., 'TRPC6')")],
 ) -> dict:
-    """Query the Antibody Registry for available antibodies.
-
-    This function searches the Antibody Registry database for antibodies matching the search term.
-    Common search parameters include gene symbols (e.g., 'TRPC6'), protein names, UniProt IDs,
-    or other relevant identifiers.
-
-    Note: Some information provided by the Antibody Registry is for non-commercial use only.
-    Users should refer to antibodyregistry.org for complete terms of use and licensing details.
-
-    Args:
-        search (str): Search term for antibodies. Can be a gene symbol, protein name, UniProt ID, or similar identifier.
+    """Search Antibody Registry for antibodies. Returns catalog numbers, vendors, clonality, applications, and metadata.
 
     Returns:
-        dict: Antibody search results including catalog numbers, vendor information, clonality,
-              applications, and other antibody metadata, or error message if the request fails.
+        dict: Search results containing list of antibodies with catalog numbers, vendors, clonality, applications, metadata or error message.
     """
     search = search.strip()
     if not search:

src/biocontext_kb/core/biorxiv/_get_preprint_details.py

@@ -11,20 +11,13 @@
 
 @core_mcp.tool()
 def get_biorxiv_preprint_details(
-    doi: Annotated[str, Field(description="DOI of the preprint (e.g., '10.1101/2020.09.09.20191205')")],
-    server: Annotated[str, Field(description="Server to search: 'biorxiv' or 'medrxiv'")] = "biorxiv",
+    doi: Annotated[str, Field(description="Preprint DOI (e.g., '10.1101/2020.09.09.20191205')")],
+    server: Annotated[str, Field(description="'biorxiv' or 'medrxiv'")] = "biorxiv",
 ) -> Dict[str, Any]:
-    """Get detailed information about a specific preprint by DOI.
-
-    This tool retrieves detailed metadata for a single preprint from bioRxiv or medRxiv
-    using its DOI identifier.
-
-    Args:
-        doi (str): DOI of the preprint (e.g., '10.1101/2020.09.09.20191205').
-        server (str): Server to search - 'biorxiv' or 'medrxiv' (default: 'biorxiv').
+    """Get detailed preprint metadata by DOI. Retrieves title, authors, abstract, date, version, category, license, and publication status.
 
     Returns:
-        dict: Detailed preprint information or error message
+        dict: Preprint metadata including doi, title, authors, abstract, date, version, category, license, publication status or error message.
     """
     # Validate server
     if server.lower() not in ["biorxiv", "medrxiv"]:

src/biocontext_kb/core/biorxiv/_get_recent_biorxiv_preprints.py

@@ -12,39 +12,25 @@
 
 @core_mcp.tool()
 def get_recent_biorxiv_preprints(
-    server: Annotated[str, Field(description="Server to search: 'biorxiv' or 'medrxiv'")] = "biorxiv",
-    start_date: Annotated[Optional[str], Field(description="Start date in YYYY-MM-DD format")] = None,
-    end_date: Annotated[Optional[str], Field(description="End date in YYYY-MM-DD format")] = None,
+    server: Annotated[str, Field(description="'biorxiv' or 'medrxiv'")] = "biorxiv",
+    start_date: Annotated[Optional[str], Field(description="Start date (YYYY-MM-DD)")] = None,
+    end_date: Annotated[Optional[str], Field(description="End date (YYYY-MM-DD)")] = None,
     days: Annotated[
-        Optional[int], Field(description="Number of recent days to search (alternative to date range)", ge=1, le=365)
+        Optional[int], Field(description="Search last N days (1-365, alternative to date range)", ge=1, le=365)
     ] = None,
     recent_count: Annotated[
-        Optional[int], Field(description="Number of most recent preprints (alternative to date range)", ge=1, le=1000)
+        Optional[int], Field(description="Most recent N preprints (1-1000, alternative to date range)", ge=1, le=1000)
     ] = None,
     category: Annotated[
-        Optional[str], Field(description="Subject category filter (e.g., 'cell biology', 'neuroscience')")
+        Optional[str], Field(description="Filter by subject (e.g., 'cell biology', 'neuroscience')")
     ] = None,
-    cursor: Annotated[int, Field(description="Starting position for pagination", ge=0)] = 0,
-    max_results: Annotated[int, Field(description="Maximum number of results to return", ge=1, le=500)] = 100,
+    cursor: Annotated[int, Field(description="Pagination: starting position", ge=0)] = 0,
+    max_results: Annotated[int, Field(description="Max results per page (1-500)", ge=1, le=500)] = 100,
 ) -> Dict[str, Any]:
-    """Get recent preprints from bioRxiv or medRxiv.
-
-    This tool searches the bioRxiv and medRxiv preprint servers for research papers.
-    You can search by date range, recent posts, or most recent papers.
-    Results are paginated with up to 100 papers per API call.
-
-    Args:
-        server (str): Server to search - 'biorxiv' or 'medrxiv' (default: 'biorxiv').
-        start_date (str, optional): Start date in YYYY-MM-DD format.
-        end_date (str, optional): End date in YYYY-MM-DD format.
-        days (int, optional): Number of recent days to search (1-365).
-        recent_count (int, optional): Number of most recent preprints (1-1000).
-        category (str, optional): Subject category filter (e.g., 'cell biology', 'neuroscience').
-        cursor (int): Starting position for pagination (default: 0).
-        max_results (int): Maximum number of results to return (default: 100, max: 500).
+    """Search bioRxiv/medRxiv preprints by date range or recent count. Specify one search method: date range, days, or recent_count.
 
     Returns:
-        dict: Preprint search results or error message
+        dict: Search results with server, search_params, total_returned, papers list (each with title, authors, abstract, metadata), pagination info or error message.
     """
     # Validate server
     if server.lower() not in ["biorxiv", "medrxiv"]:

src/biocontext_kb/core/clinicaltrials/_get_recruiting_studies_by_location.py

@@ -8,38 +8,18 @@
 
 @core_mcp.tool()
 def get_recruiting_studies_by_location(
-    location_country: Annotated[
-        str, Field(description="Country name (e.g., 'United States', 'Germany', 'United Kingdom')")
-    ],
-    location_state: Annotated[
-        Optional[str], Field(description="State or province (e.g., 'California', 'New York')")
-    ] = None,
-    location_city: Annotated[Optional[str], Field(description="City name (e.g., 'Los Angeles', 'Boston')")] = None,
-    condition: Annotated[
-        Optional[str], Field(description="Medical condition to filter by (e.g., 'cancer', 'diabetes')")
-    ] = None,
-    study_type: Annotated[
-        Optional[str], Field(description="Type of study: 'INTERVENTIONAL', 'OBSERVATIONAL', 'ALL'")
-    ] = "ALL",
-    age_range: Annotated[Optional[str], Field(description="Age group: 'CHILD', 'ADULT', 'OLDER_ADULT', 'ALL'")] = "ALL",
-    page_size: Annotated[int, Field(description="Number of results to return", ge=1, le=1000)] = 50,
+    location_country: Annotated[str, Field(description="Country name (e.g., 'United States', 'Germany')")],
+    location_state: Annotated[Optional[str], Field(description="State/province (e.g., 'California')")] = None,
+    location_city: Annotated[Optional[str], Field(description="City name")] = None,
+    condition: Annotated[Optional[str], Field(description="Medical condition filter (e.g., 'cancer')")] = None,
+    study_type: Annotated[Optional[str], Field(description="'INTERVENTIONAL', 'OBSERVATIONAL', or 'ALL'")] = "ALL",
+    age_range: Annotated[Optional[str], Field(description="'CHILD', 'ADULT', 'OLDER_ADULT', or 'ALL'")] = "ALL",
+    page_size: Annotated[int, Field(description="Results per page (1-1000)", ge=1, le=1000)] = 50,
 ) -> Union[Dict[str, Any], dict]:
-    """Find recruiting clinical trials in a specific geographic location.
-
-    This function helps patients and healthcare providers find clinical trials
-    that are currently recruiting participants in their area.
-
-    Args:
-        location_country (str): Country name where studies are conducted.
-        location_state (str, optional): State or province name.
-        location_city (str, optional): City name.
-        condition (str, optional): Medical condition to filter by.
-        study_type (str, optional): Type of study filter (default: "ALL").
-        age_range (str, optional): Age group filter (default: "ALL").
-        page_size (int): Number of results to return (default: 50, max: 1000).
+    """Find recruiting clinical trials by geographic location. Returns paginated results with summary breakdowns.
 
     Returns:
-        dict: Recruiting studies in the specified location or error message
+        dict: Studies list with summary containing search location, total studies, study type/phase/condition breakdowns, recruiting locations or error message.
     """
     if not location_country:
         return {"error": "Location country must be provided"}

src/biocontext_kb/core/clinicaltrials/_get_studies_by_condition.py

@@ -8,40 +8,23 @@
 
 @core_mcp.tool()
 def get_studies_by_condition(
-    condition: Annotated[
-        str, Field(description="Medical condition or disease name (e.g., 'breast cancer', 'diabetes', 'alzheimer')")
-    ],
+    condition: Annotated[str, Field(description="Medical condition/disease (e.g., 'cancer', 'diabetes')")],
     status: Annotated[
         Optional[str],
-        Field(description="Study status filter: 'RECRUITING', 'ACTIVE_NOT_RECRUITING', 'COMPLETED', 'ALL'"),
+        Field(description="'RECRUITING', 'ACTIVE_NOT_RECRUITING', 'COMPLETED', or 'ALL'"),
     ] = "ALL",
-    study_type: Annotated[
-        Optional[str], Field(description="Type of study: 'INTERVENTIONAL', 'OBSERVATIONAL', 'ALL'")
-    ] = "ALL",
-    location_country: Annotated[
-        Optional[str], Field(description="Country filter (e.g., 'United States', 'Germany')")
-    ] = None,
-    page_size: Annotated[int, Field(description="Number of results to return", ge=1, le=1000)] = 50,
+    study_type: Annotated[Optional[str], Field(description="'INTERVENTIONAL', 'OBSERVATIONAL', or 'ALL'")] = "ALL",
+    location_country: Annotated[Optional[str], Field(description="Country filter (e.g., 'United States')")] = None,
+    page_size: Annotated[int, Field(description="Results per page (1-1000)", ge=1, le=1000)] = 50,
     sort: Annotated[
         str,
-        Field(description="Sort order: 'LastUpdatePostDate:desc', 'StudyFirstPostDate:desc', 'EnrollmentCount:desc'"),
+        Field(description="'LastUpdatePostDate:desc', 'StudyFirstPostDate:desc', or 'EnrollmentCount:desc'"),
     ] = "LastUpdatePostDate:desc",
 ) -> Union[Dict[str, Any], dict]:
-    """Search for clinical trials by medical condition with simplified parameters.
-
-    This function provides a focused search for clinical trials related to a specific
-    medical condition, with common filters that biomedical researchers typically use.
-
-    Args:
-        condition (str): Medical condition or disease name to search for.
-        status (str, optional): Study status filter (default: "ALL").
-        study_type (str, optional): Type of study filter (default: "ALL").
-        location_country (str, optional): Country where studies are conducted.
-        page_size (int): Number of results to return (default: 50, max: 1000).
-        sort (str): Sort order for results (default: most recently updated).
+    """Search trials by condition with summary statistics. Returns paginated results with breakdowns by status, study type, and phase.
 
     Returns:
-        dict: Study search results with summary statistics or error message
+        dict: Studies list with summary containing condition searched, total studies, status/study type/phase breakdowns or error message.
     """
     if not condition:
         return {"error": "Medical condition must be provided"}

src/biocontext_kb/core/clinicaltrials/_get_studies_by_intervention.py

@@ -10,45 +10,29 @@
 def get_studies_by_intervention(
     intervention: Annotated[
         str,
-        Field(description="Drug, therapy, or treatment name (e.g., 'aspirin', 'pembrolizumab', 'radiation therapy')"),
+        Field(description="Drug/therapy name (e.g., 'aspirin', 'pembrolizumab', 'radiation')"),
     ],
-    condition: Annotated[
-        Optional[str], Field(description="Medical condition to filter by (e.g., 'cancer', 'diabetes')")
-    ] = None,
+    condition: Annotated[Optional[str], Field(description="Medical condition filter (e.g., 'cancer')")] = None,
     phase: Annotated[
-        Optional[str], Field(description="Clinical trial phase: 'PHASE1', 'PHASE2', 'PHASE3', 'PHASE4', 'EARLY_PHASE1'")
+        Optional[str], Field(description="'PHASE1', 'PHASE2', 'PHASE3', 'PHASE4', or 'EARLY_PHASE1'")
     ] = None,
     status: Annotated[
-        Optional[str], Field(description="Study status: 'RECRUITING', 'ACTIVE_NOT_RECRUITING', 'COMPLETED', 'ALL'")
+        Optional[str], Field(description="'RECRUITING', 'ACTIVE_NOT_RECRUITING', 'COMPLETED', or 'ALL'")
     ] = "ALL",
     intervention_type: Annotated[
         Optional[str],
-        Field(
-            description="Type of intervention: 'DRUG', 'BIOLOGICAL', 'DEVICE', 'PROCEDURE', 'RADIATION', 'BEHAVIORAL', 'ALL'"
-        ),
+        Field(description="'DRUG', 'BIOLOGICAL', 'DEVICE', 'PROCEDURE', 'RADIATION', 'BEHAVIORAL', or 'ALL'"),
     ] = "ALL",
-    page_size: Annotated[int, Field(description="Number of results to return", ge=1, le=1000)] = 50,
+    page_size: Annotated[int, Field(description="Results per page (1-1000)", ge=1, le=1000)] = 50,
     sort: Annotated[
         str,
-        Field(description="Sort order: 'LastUpdatePostDate:desc', 'StudyFirstPostDate:desc', 'EnrollmentCount:desc'"),
+        Field(description="'LastUpdatePostDate:desc', 'StudyFirstPostDate:desc', or 'EnrollmentCount:desc'"),
     ] = "LastUpdatePostDate:desc",
 ) -> Union[Dict[str, Any], dict]:
-    """Search for clinical trials by drug or intervention name.
-
-    This function helps biomedical researchers find clinical trials testing specific
-    drugs, therapies, or treatments, with optional filters for condition and phase.
-
-    Args:
-        intervention (str): Drug, therapy, or treatment name to search for.
-        condition (str, optional): Medical condition to filter by.
-        phase (str, optional): Clinical trial phase to filter by.
-        status (str, optional): Study status filter (default: "ALL").
-        intervention_type (str, optional): Type of intervention filter (default: "ALL").
-        page_size (int): Number of results to return (default: 50, max: 1000).
-        sort (str): Sort order for results (default: most recently updated).
+    """Search trials by intervention with condition and phase filters. Returns paginated results with breakdowns.
 
     Returns:
-        dict: Study search results with summary statistics or error message
+        dict: Studies list with summary containing intervention searched, total studies, status/phase breakdowns, top conditions/sponsors or error message.
     """
     if not intervention:
         return {"error": "Intervention name must be provided"}