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rezashahnazar

Perplexity MCP Server

by rezashahnazar
OverviewSchemaRelated ServersScoreDiscussions
TypeScript
Remote

Server Quality Checklist

67%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v1.0.0

  • Disambiguation5/5

    With only one tool, there is no possibility of ambiguity or overlap between tools. The tool's purpose is clearly defined as performing AI-powered web searches with citations, leaving no room for confusion or misselection.

    Naming Consistency5/5

    The single tool name 'perplexity_search_chat' follows a clear and consistent pattern. Since there is only one tool, naming consistency is inherently perfect with no deviations or mixed conventions to evaluate.

    Tool Count2/5

    A single tool is too few for most server purposes, as it limits functionality and flexibility. While this tool covers web search comprehensively, the server's scope feels thin, lacking complementary operations like filtering, saving searches, or managing history that would enhance its utility.

    Completeness3/5

    The tool provides a robust search function with real-time web access and citations, covering the core need for up-to-date information. However, there are notable gaps: no ability to refine searches, save results, or interact with search history, which are common features in search-oriented interfaces, limiting agent workflows.

  • Average 4.4/5 across 1 of 1 tools scored.

    See the Tool Scores section below for per-tool breakdowns.

    • No community issues in the last 6 months
    • 0 commits in the last 12 weeks
    • No stable releases found
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI status not available

  • This repository is licensed under Apache 2.0.

  • This repository includes a README.md file.

  • No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.

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How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

  • Behavior4/5

    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 effectively describes key traits: the tool performs searches with real-time web access, includes citations to sources, and is optimized for current or authoritative information. However, it lacks details on rate limits, authentication needs, or error handling, which are minor gaps.

    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 appropriately sized and front-loaded, starting with the core functionality and followed by usage guidelines. Every sentence adds value without redundancy, making it efficient 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.

    Completeness4/5

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

    Given the tool's complexity (search with AI and web integration) and lack of annotations or output schema, the description is mostly complete. It covers purpose, usage, and behavioral aspects well, but could benefit from mentioning response format or potential limitations to fully compensate for the missing structured data.

    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 schema description coverage is 100%, so the input schema already documents the 'content' parameter as a string for the user query. The description does not add any additional meaning or context about parameters beyond what the schema provides, such as formatting examples or constraints, resulting in a baseline score.

    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?

    The description clearly states the tool's purpose with specific verbs ('Ask questions', 'get AI-powered answers') and resources ('Perplexity AI', 'real-time web search'). It explicitly distinguishes what the tool does by mentioning its unique features like citations and web-sourced information, even without siblings for comparison.

    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?

    The description provides explicit guidance on when to use this tool, listing specific scenarios such as 'current information, facts, research, news' and 'any query that benefits from up-to-date web sources'. It includes a 'Best for' section with detailed examples like current events and technical documentation lookups, offering clear context for usage.

    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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  • Confirm that the MCP server is working as expected.
  • Confirm that there are no obvious security issues.
  • Evaluate tool definition quality.

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