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tianmu

Perplexica MCP Server

by tianmu

search_web

Conduct AI-powered web searches. Submit a query to receive an answer accompanied by source citations.

Instructions

Search the web using Perplexica's AI-powered search engine.

Args: query: The search query or question chat_provider: Chat model provider (optional, uses env config if not provided) chat_model: Specific chat model to use (optional, uses env config if not provided) embedding_provider: Embedding model provider (optional, uses env config if not provided) embedding_model: Specific embedding model to use (optional, uses env config if not provided) optimization_mode: Speed vs quality tradeoff (optional, uses env config if not provided) output_format: Output format - "formatted" for human-readable text or "json" for raw JSON

Returns: Formatted text with AI response and sources, or JSON if output_format="json"

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYes
chat_providerNo
chat_modelNo
embedding_providerNo
embedding_modelNo
optimization_modeNo
output_formatNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior3/5

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

The description explains the output format (formatted text or JSON) and that it uses an AI-powered engine. No mention of rate limits, authentication, or safety (e.g., read-only). With no annotations, more detail would be beneficial.

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

Conciseness3/5

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

The description is well-structured with Args and Returns sections, but it is verbose, repeating 'optional, uses env config if not provided' for multiple parameters. Could be more concise.

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 parameter count and sibling tools, the description adequately explains parameters and return values. However, it lacks context on when to use this tool over similar search tools (e.g., search_academic), leaving the agent to infer.

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

Parameters5/5

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

Schema coverage is 0%, but the description provides detailed explanations for each parameter, clarifying defaults behavior ('uses env config if not provided') and the purpose of output_format. This adds significant value beyond the schema.

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 it searches the web using an AI-powered engine. However, it does not differentiate from sibling search tools like search_academic, search_reddit, or search_youtube, missing a chance to clarify scope.

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 on when to use this tool vs alternatives such as search_academic or search_reddit. The description implies general web search but lacks explicit context or exclusions.

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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