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DaInfernalCoder

MCP-researcher Server

by DaInfernalCoder
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Perplexity MCP サーバー

Perplexityの専用AIモデルを搭載したインテリジェントなリサーチアシスタント。クエリの複雑さを自動検出し、最適な結果を得るために最適なモデルにリクエストをルーティングします。公式サーバーとは異なり、あらゆるタスクに対応する検索機能を備えています。

ツール

クイック ノート: Deep Research ツールは、実装の違いにより、cline などの一部のツールではタイムアウトしますが、cursor などの他のツールではタイムアウトしません。ただし、reason ツールがそれを補います。

1. 検索（Sonar Pro）

簡単なクエリや基本情報の検索に最適なクイック検索。簡潔で直接的な回答が必要な、率直な質問に最適です。

const result = await use_mcp_tool({
  server_name: "perplexity",
  tool_name: "search",
  arguments: {
    query: "What is the capital of France?",
    force_model: false // Optional: force using this model even if query seems complex
  }
});

2. Reason（ソナー・リーゾニング・プロ）

詳細な分析を必要とする複雑で多段階のタスクを処理します。説明、比較、問題解決に最適です。

const result = await use_mcp_tool({
  server_name: "perplexity",
  tool_name: "reason",
  arguments: {
    query: "Compare and contrast REST and GraphQL APIs, explaining their pros and cons",
    force_model: false // Optional: force using this model even if query seems simple
  }
});

3. ディープリサーチ（ソナーディープリサーチ）

包括的な調査を実施し、詳細なレポートを作成します。複雑なトピックの詳細な分析に最適です。

const result = await use_mcp_tool({
  server_name: "perplexity",
  tool_name: "deep_research",
  arguments: {
    query: "The impact of quantum computing on cryptography",
    focus_areas: [
      "Post-quantum cryptographic algorithms",
      "Timeline for quantum threats",
      "Practical mitigation strategies"
    ],
    force_model: false // Optional: force using this model even if query seems simple
  }
});

Related MCP server: github-manager MCP Server

インテリジェントなモデル選択

サーバーはクエリの複雑さを自動的に分析し、リクエストを最も適切なモデルにルーティングします。

  1. シンプルなクエリ→ Sonar Pro

    • 基本情報の検索

    • 率直な質問

    • 簡単な事実

  2. 複雑なクエリ→ Sonar Reasoning Pro

    • どのように/なぜの質問

    • 比較

    • ステップバイステップの説明

    • 問題解決タスク

  3. リサーチクエリ→ Sonar Deep Research

    • 詳細な分析

    • 包括的な研究

    • 詳細な調査

    • 多面的なトピック

任意のツールの引数でforce_model: trueを使用して自動選択をオーバーライドできます。

設定

  1. 前提条件

  2. MCP設定を構成する

MCP 設定ファイルに追加します (場所はプラットフォームによって異なります)。

{
  "mcpServers": {
    "perplexity": {
      "command": "node",
      "args": ["/path/to/perplexity-server/build/index.js"],
      "env": {
        "PERPLEXITY_API_KEY": "YOUR_API_KEY_HERE"
      },
      "disabled": false,
      "autoApprove": []
    }
  }
}

または、NPX を使用すると、ローカルにインストールする必要がなくなります (macOS に推奨)。

{
  "mcpServers": {
    "perplexity": {
      "command": "npx",
      "args": [
        "-y",
        "perplexity-mcp"
      ],
      "env": {
        "PERPLEXITY_API_KEY": "your_api_key"
      }
    }
  }
}

Available Tools

3 tools
deep_researchB

Conducts in-depth analysis and generates detailed reports using Perplexity's Sonar Deep Research model. Best for comprehensive research topics.

ParametersJSON Schema
NameRequiredDescriptionDefault
queryYesThe research topic or question to investigate in depth. IMPORTANT: Be extremely specific and include all relevant details: - Include exact error messages, logs, and stack traces if applicable - Provide exact terminology, function names, API names, version numbers - Include relevant code snippets showing the problem or context - Specify platform, OS, framework versions, and environment details - Mention any attempted solutions or workarounds - Provide context about what you're trying to achieve - Include relevant data structures, configurations, or inputs - Specify the scope, constraints, or specific requirements The more specific details you include, the more accurate and helpful the answer will be. If you don't have enough specific information, prompt the user to provide it before using this tool.
focus_areasNoOptional: Specific aspects or areas to focus on
force_modelNoOptional: Force using this model even if query seems simple

TDQS

B3.2/5.0
Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions the tool uses 'Perplexity's Sonar Deep Research model' and is for 'comprehensive research topics,' but it lacks details on behavioral traits such as response format, potential rate limits, authentication needs, or whether it's a read-only or mutative operation. The description doesn't contradict annotations, but it's insufficient for a tool with no annotation coverage.

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 appropriately sized and front-loaded, consisting of two concise sentences that directly state the tool's purpose and usage context. There's no wasted text, and it efficiently conveys key information without unnecessary elaboration.

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 (3 parameters, no annotations, no output schema), the description is moderately complete. It covers the purpose and hints at usage but lacks details on behavioral traits, output format, and differentiation from siblings. The high schema coverage helps, but for a research tool with no output schema, more context on what to expect from results would be beneficial.

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 all parameters thoroughly. The description adds no additional meaning beyond what the schema provides—it doesn't explain parameter interactions or usage nuances. With high schema coverage, the baseline score is 3, as the description doesn't compensate but also doesn't detract.

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: 'Conducts in-depth analysis and generates detailed reports using Perplexity's Sonar Deep Research model.' It specifies the verb ('conducts analysis and generates reports'), resource ('Perplexity's Sonar Deep Research model'), and scope ('comprehensive research topics'). However, it doesn't explicitly differentiate from sibling tools 'reason' and 'search' beyond mentioning it's 'best for comprehensive research topics,' which is somewhat vague.

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

Usage Guidelines3/5

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

The description provides some guidance: 'Best for comprehensive research topics.' This implies usage for complex or broad inquiries, but it doesn't explicitly state when to use this tool versus alternatives like 'reason' or 'search,' nor does it mention any exclusions or prerequisites. The input schema hints at usage context by emphasizing specificity, but this isn't part of the description itself.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

reasonA

Handles complex, multi-step tasks using Perplexity's Sonar Reasoning Pro model. Best for explanations, comparisons, and problem-solving.

ParametersJSON Schema
NameRequiredDescriptionDefault
queryYesThe complex query or task to reason about. IMPORTANT: Be extremely specific and include all relevant details: - Include exact error messages, logs, and stack traces if applicable - Provide exact terminology, function names, API names, version numbers - Include relevant code snippets showing the problem or context - Specify platform, OS, framework versions, and environment details - Mention any attempted solutions or workarounds - Provide context about what you're trying to achieve - Include relevant data structures, configurations, or inputs The more specific details you include, the more accurate and helpful the answer will be. If you don't have enough specific information, prompt the user to provide it before using this tool.
force_modelNoOptional: Force using this model even if query seems simple/research-oriented

TDQS

A3.8/5.0
Behavior3/5

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 mentions using 'Perplexity's Sonar Reasoning Pro model' and hints at complexity handling, but lacks details on performance traits (e.g., latency, rate limits), error handling, or output format. It adds some context but falls short of fully describing behavioral aspects for a tool with no annotations.

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 extremely concise and front-loaded, consisting of two sentences that directly state the tool's purpose and best-use cases. Every sentence earns its place by providing essential information without waste, making it easy to parse and understand quickly.

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 has no annotations and no output schema, the description is incomplete. It covers purpose and usage well but lacks details on behavioral traits, output format, or error handling. For a tool with 2 parameters and no structured metadata, the description should do more to compensate, leaving gaps in contextual understanding.

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 description coverage is 100%, so the schema already documents both parameters thoroughly. The description does not add specific parameter semantics beyond what's in the schema, but since coverage is high, the baseline is 3. It earns a 4 because the description implicitly reinforces the importance of the 'query' parameter by emphasizing complex tasks, adding slight contextual value without redundancy.

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 'handles complex, multi-step tasks' using a specific reasoning model, with examples of use cases (explanations, comparisons, problem-solving). It distinguishes from 'deep_research' and 'search' by emphasizing reasoning over research or simple search, though not explicitly naming alternatives. The purpose is specific but could be more explicit about sibling differentiation.

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

Usage Guidelines4/5

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

The description provides clear context for when to use this tool ('Best for explanations, comparisons, and problem-solving'), implying it's suited for complex reasoning tasks. However, it does not explicitly state when not to use it or name alternatives like 'deep_research' or 'search', missing explicit exclusions or comparisons. The guidance is strong but not fully comprehensive.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. 3 tool updatesv1.0.0
    • First observeddeep_research
    • First observedreason
    • First observedsearch
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TDQS

A3.6/5.0
Disambiguation5/5

Each tool has a clearly distinct purpose: deep_research for comprehensive analysis, reason for multi-step reasoning tasks, and search for basic queries. The descriptions explicitly differentiate their use cases, eliminating any ambiguity or overlap in functionality.

Naming Consistency5/5

All tool names follow a consistent snake_case pattern with clear, descriptive verbs (deep_research, reason, search). There are no deviations in naming style, making the set predictable and easy to understand.

Tool Count3/5

With only 3 tools, the set feels thin for a research server, potentially limiting coverage of common research workflows like summarization, citation management, or data extraction. However, the tools are well-scoped to core research tasks, avoiding bloat.

Completeness3/5

The tools cover basic research functions (deep analysis, reasoning, quick search), but there are notable gaps such as no tools for summarizing, filtering results, or handling citations. This may cause agents to work around missing operations in complex research scenarios.

Maintenance

ActivityInactive
ResponsivenessSyncing

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