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fastmcp-me

Perplexity Advanced MCP

by fastmcp-me

ask_perplexity

Consult Perplexity to search the internet and answer queries, with file attachments and support for simple or complex query types.

Instructions

Perplexity is fundamentally an LLM that can search the internet, gather information, and answer users' queries.

    For example, let's suppose we want to find out the latest version of Python.
    1. You would search on Google.
    2. Then read the top two or three results directly to verify.

    Perplexity does that work for you.

    To answer a user's query, Perplexity searches, opens the top search results, finds information on those websites, and then provides the answer.

    Perplexity can be used with two types of queries: simple and complex. Choosing the right query type to fulfill the user's request is most important.

    SIMPLE Query:
    - Cheap and fast (on average, 10x cheaper and 3x faster than complex queries).
    - Suitable for straightforward questions such as "What is the latest version of Python?"
    - Pricing: $1/M input tokens, $1/M output tokens.

    COMPLEX Query:
    - Slower and more expensive (on average, 10x more expensive and 3x slower).
    - Suitable for tasks requiring multiple steps of reasoning or deep analysis, such as "Analyze the attached code to examine the current status of a specific library and create a migration plan."
    - Pricing: $1/M input tokens, $5/M output tokens.

    Instructions:
    - When reviewing the user's request, if you find anything unexpected, uncertain, or questionable, do not hesitate to use the "ask_perplexity" tool to consult Perplexity.
    - Since Perplexity is also an LLM, prompt engineering techniques are paramount.
    - Remember the basics of prompt engineering, such as providing clear instructions, sufficient context, and examples.
    - Include as much context and relevant files as possible to smoothly fulfill the user's request.
    - IMPORTANT: When adding files as attachments, you MUST use absolute paths (e.g., '/absolute/path/to/file.py'). Relative paths will not work.

    Note: All queries must be in English for optimal results.
    

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYesThe query to search for
query_typeYesType of query to determine model selection
attachment_pathsYesAn optional list of absolute file paths to attach as context for the search query
Behavior4/5

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

With no annotations, the description carries full burden. It explains the search process (searches, opens top results, finds info, provides answer), pricing/speed trade-offs, and prompt engineering requirements. However, it does not explicitly state that the tool is read-only or idempotent, though implied.

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?

Description is overly verbose, including examples and pricing details that could be streamlined. While front-loaded with purpose, it contains redundant elaboration (e.g., step-by-step hypothetical) that increases length without proportional benefit.

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

Completeness5/5

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

For a tool with three required parameters and no output schema, the description is highly comprehensive. It covers all parameter semantics, usage guidelines, behavioral traits, and even prompt engineering tips, making it fully complete for an agent to invoke correctly.

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

Parameters4/5

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

Schema coverage is 100%, so baseline 3. Description adds significant value: explains query_type enum choices with detailed use cases, and mandates absolute paths for attachment_paths. Also notes English-only for query, which is not in schema.

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

Purpose5/5

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

Description clearly states that Perplexity is an LLM that searches the internet to answer queries, with explicit examples (e.g., latest Python version) and differentiation between simple and complex queries. The verb 'ask' plus resource 'perplexity' is specific and unambiguous.

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

Usage Guidelines5/5

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

Provides explicit when-to-use guidance: use when uncertain or questionable, and instructions for choosing query type (simple for straightforward, complex for multi-step analysis). Also mandates English queries and absolute paths for attachments, leaving no ambiguity.

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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