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UpendraNath

Tavily Web Search MCP Server

by UpendraNath
OverviewSchemaRelated ServersScoreDiscussions
Python
Remote

web_search

Search the web for information using natural language queries to find relevant data and answers through the Tavily API.

Instructions

Search the web for information about the given query

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Implementation Reference

  • server.py:15-19 (handler)
    The web_search tool handler: registered via @mcp.tool(), accepts a query string, performs web search using TavilyClient.get_search_context(), and returns the search results.
    @mcp.tool()
def web_search(query: str) -> str:
    """Search the web for information about the given query"""
    search_results = client.get_search_context(query=query)
    return search_results

Tool Definition Quality

B3.1/5.0
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 searches the web but doesn't add context beyond that—such as rate limits, authentication needs, result format, or potential side effects. This is a significant gap for a tool that likely interacts with external resources.

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 front-loads the core action ('Search the web') and purpose. There is no wasted language, making it appropriately sized and easy to parse for an AI agent.

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's complexity (web search with external interaction), no annotations, and an output schema (which reduces need to explain return values), the description is minimally adequate. It covers the basic purpose but lacks behavioral context and usage guidelines, making it incomplete for optimal agent 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 input schema has 0% description coverage, so the description must compensate. It mentions 'the given query,' which aligns with the single parameter 'query,' adding some meaning. However, it doesn't provide details on query syntax, length limits, or examples, leaving gaps in parameter understanding.

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 tool's purpose with a specific verb ('Search') and resource ('the web'), and it specifies the action is for information about a given query. However, it doesn't differentiate from sibling tools like 'organize_and_categorize' or 'load_bookmark_data', which might involve web-related functions, so it lacks sibling distinction.

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?

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention any context, exclusions, or comparisons to sibling tools such as 'load_bookmark_data' for retrieving saved web data or 'organize_and_categorize' for processing web content, leaving the agent without usage direction.

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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