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UpendraNath

Tavily Web Search MCP Server

by UpendraNath

load_bookmark_data

Import browser bookmarks and history from data files to enable personalized web search results and context-aware browsing assistance.

Instructions

Load bookmarks and history data from files

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
data_folderNodata

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Implementation Reference

  • server.py:27-37 (handler)
    The handler function for the 'load_bookmark_data' MCP tool. It creates a BookmarkOrganizer instance and calls its load_data() method to load bookmarks and history, returning a success message with item count.
    @mcp.tool()
    def load_bookmark_data(data_folder: str = "data") -> str:
        """Load bookmarks and history data from files"""
        try:
            from bookmark_organizer import BookmarkOrganizer
            organizer = BookmarkOrganizer(data_folder)
            organizer.load_data()
            return f"Loaded {len(organizer.all_items)} total items from {data_folder}"
        except Exception as e:
            return f"Error loading data: {str(e)}"
  • Supporting helper method in BookmarkOrganizer class that implements the core data loading logic by parsing specific bookmark HTML and browser history CSV files.
    def load_data(self):
        """Load all bookmark and history data"""
        print("Loading data from files...")
        
        # Load bookmarks
        bookmark_file = self.data_folder / "bookmarks_10_26_25.html"
        if bookmark_file.exists():
            bookmarks = self.parser.parse_netscape_html(str(bookmark_file))
            self.all_items.extend(bookmarks)
            print(f"Loaded {len(bookmarks)} bookmarks")
        
        # Load history
        history_file = self.data_folder / "BrowserHistory_10_26_25.csv"
        if history_file.exists():
            history = self.parser.parse_csv_history(str(history_file))
            self.all_items.extend(history)
            print(f"Loaded {len(history)} history entries")
        
        print(f"Total items loaded: {len(self.all_items)}")
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden of behavioral disclosure. It states the tool loads data but doesn't cover critical aspects like whether this is a read-only operation, potential side effects, error handling, or performance implications, which are essential for safe use.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, efficient sentence that directly states the tool's function without unnecessary words. It's front-loaded and appropriately sized for its purpose, earning a perfect score for conciseness.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool has an output schema (which handles return values) and only one parameter, the description is minimally adequate. However, with no annotations and low schema coverage, it lacks details on behavior and parameter usage, making it incomplete for fully informed use.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The description mentions loading data 'from files', which loosely relates to the 'data_folder' parameter, but with 0% schema description coverage, it doesn't add meaningful details like supported file types or folder structure. Since there's only one parameter, the baseline is higher, but the description doesn't fully compensate for the lack of schema documentation.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the action ('Load') and the resource ('bookmarks and history data from files'), making the purpose understandable. However, it doesn't differentiate from sibling tools like 'organize_and_categorize' or 'web_search', which prevents a perfect score.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance is provided on when to use this tool versus alternatives. The description lacks context about prerequisites, such as file formats or locations, and doesn't mention any sibling tools for comparison, leaving usage unclear.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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