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lv042

Perplexity Web-Search MCP

by lv042

web_search

Retrieve real-time web answers from Perplexity Sonar API. Specify query, model, recency filter, city, or country for targeted results.

Instructions

Search the web using Perplexity Sonar API for real-time information.

Args:
    query: The search query or question to ask Perplexity
    model: The Perplexity model to use. Options: "sonar", "sonar-pro", 
           "sonar-deep-research", "sonar-reasoning", "sonar-reasoning-pro"
           (default: "sonar")
    recency_filter: Time filter for results (e.g., 'week', 'day')
    city: City name for location-based search
    country: Two letter ISO country code

Returns:
    str: The search results and answer from Perplexity API

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYes
modelNosonar
recency_filterNo
cityNo
countryNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior2/5

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

With no annotations, the description carries full responsibility for behavioral disclosure. It states 'real-time information' but does not disclose rate limits, response format details, error handling, or any side effects. The tool is likely read-only but this is not confirmed.

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

Conciseness4/5

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

The description is concise and front-loaded with the main action. Parameters are listed clearly with brief explanations. No wasted words. However, it mixes parameter descriptions with return type in a non-standard format (Args/Returns), which is acceptable but slightly less structured than ideal.

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 5 parameters (1 required) and an output schema (string), the description covers all params minimally. It lacks usage examples, failure behaviors, or differentiation from sibling. For a simple search tool, it is adequate but not comprehensive. The lack of annotations increases the need for more context.

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?

Schema description coverage is 0%, so the description must compensate. It briefly explains each parameter: query, model (with enum options), recency_filter (example 'week'), city, country. For model, it list options already in schema but adds default value. Other parameters have minimal explanation; recency_filter and city/country lack examples beyond 'week' and 'day'. Adds some value but could be more detailed.

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: 'Search the web using Perplexity Sonar API for real-time information.' It uses specific verbs and resources. However, it does not differentiate from the sibling tool 'web_search_academic', leaving ambiguity about which to use for general vs. academic queries.

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 explicit guidance on when to use this tool versus alternatives. It does not mention when to choose web_search over web_search_academic or any prerequisites. Usage context is only implied by the tool's name and general purpose.

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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