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agentoracle-mcp

Name: agentoracle-mcp
Author: TKCollective

by TKCollective

Overview Schema Source Related Servers Discussions Admin

JavaScript

Remote

agentoracle-mcp

Claude、Cursor、Windsurf、またはその他のMCPクライアントに、60秒で信頼性検証機能を追加しましょう。

AgentOracleは、エージェントが行動を起こす前に主張を検証します。クレームごとの信頼度スコア、ACT（実行）/VERIFY（検証）/REJECT（拒否）の推奨事項、および4つのソースによる検証が、1つのコマンドでMCPツールとして利用可能です。

インストール

npx agentoracle-mcp

以上です。APIキーも、アカウントも、ウォレットの設定も不要で開始できます。

Related MCP server: MCP Perplexity Pro

Claude Desktopへの追加

Claude Desktopの設定ファイルを開きます：

Mac: ~/Library/Application Support/Claude/claude_desktop_config.json Windows: %APPDATA%\Claude\claude_desktop_config.json

以下を追加します：

{
  "mcpServers": {
    "agentoracle": {
      "command": "npx",
      "args": ["-y", "agentoracle-mcp"]
    }
  }
}

Claude Desktopを再起動してください。これで信頼性検証がツールとして利用可能になります。

Cursorへの追加

Cursorの設定 → MCP → Add Serverを開きます：

{
  "agentoracle": {
    "command": "npx",
    "args": ["-y", "agentoracle-mcp"]
  }
}

Windsurfへの追加

Windsurfの設定 → MCP Servers → Addを開きます：

{
  "agentoracle": {
    "command": "npx",
    "args": ["-y", "agentoracle-mcp"]
  }
}

インストール後にできること

Claude、Cursor、またはその他のMCPクライアントに以下のように尋ねてみてください：

"レポートに含める前に、これらの主張を検証して"
"OpenAIが2026年にAnthropicを買収したというのは本当ですか？"
"AIエージェントフレームワークについてリサーチし、各主張の信頼度スコアを教えて"
"公開する前にこの段落をファクトチェックして"

AgentOracleは入力を個別の主張に分解し、それぞれを4つの独立したソースで実行し、すべての主張に対して信頼度スコアと判定を返します。

返される内容

{
  "overall_confidence": 0.91,
  "recommendation": "act",
  "claims": [
    {
      "claim": "LangGraph leads agent frameworks in 2026",
      "verdict": "supported",
      "confidence": 0.94,
      "evidence": "Confirmed across 4 independent sources"
    },
    {
      "claim": "OpenAI acquired Anthropic in early 2026",
      "verdict": "refuted",
      "confidence": 0.04,
      "correction": "Anthropic remains independent as of April 2026"
    }
  ]
}

検証の仕組み

すべての主張は、並行して4つのソースで実行されます：

Sonar — リアルタイムWebリサーチ
Sonar Pro — 詳細な多段階分析
Adversarial — 主張を積極的に反証しようと試みる
Gemma 4 — 主張の分解と信頼度の調整

コンセンサスによってスコアが構築されます。矛盾がある場合はリスクとしてフラグが立てられます。

スコア	推奨	意味
> 0.8	`act`	主張が検証済み — 実行へ
0.5–0.8	`verify`	不確実 — レビューが必要
< 0.5	`reject`	矛盾あり — 破棄

料金

エンドポイント	価格	内容
`/preview`	無料	20リクエスト/時、支払い不要
`/evaluate`	$0.01/主張	クレームごとの完全な検証
`/research`	$0.02/クエリ	リアルタイムリサーチ + 検証

支払いはx402プロトコル経由で行われます — Base上のUSDC、SKALE（ガス代無料）、またはStellar。サブスクリプションや最低料金はありません。

無料の /preview エンドポイントは、ウォレットや設定なしで動作します。支払う前にぜひお試しください。

インストールせずに今すぐ試す

curl -X POST https://agentoracle.co/preview \
  -H "Content-Type: application/json" \
  -d '{"query": "OpenAI acquired Anthropic in 2026"}'

Available Tools

4 tools

check-healthA

Check if the AgentOracle API is online and get service status including uptime, model info, and pricing.

ParametersJSON Schema

Name	Required	Description	Default
No parameters

TDQS

A3.6/5.0

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 describes what the tool returns (uptime, model info, pricing) which is helpful context. However, it doesn't disclose important behavioral aspects like whether this is a read-only operation, potential rate limits, authentication requirements, or error conditions. The description adds some value but leaves significant behavioral 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 perfectly concise - a single sentence that communicates the complete purpose and return information without any wasted words. It's front-loaded with the primary function ('check if the AgentOracle API is online') followed by the specific data returned. Every word earns its place.

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 simplicity (0 parameters, no output schema, no annotations), the description is reasonably complete for a health check tool. It explains what information is returned. However, without annotations or output schema, it should ideally provide more detail about the response format, error handling, or when this tool should be preferred over similar status-checking operations. The description is adequate but could be more comprehensive.

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?

The tool has zero parameters with 100% schema description coverage, so the baseline is 4. The description appropriately doesn't waste space discussing parameters that don't exist, which is efficient and correct. No additional parameter semantics are needed or provided.

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 with specific verbs ('check if... is online', 'get service status') and resources ('AgentOracle API'). It distinguishes itself from sibling tools like 'deep-research' and 'research' by focusing on health/status rather than research operations. However, it doesn't explicitly differentiate from 'get-manifest' which might also provide system information.

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 implies usage context - when you need to verify API availability and obtain service metrics. However, it doesn't provide explicit guidance on when to use this versus alternatives like 'get-manifest', nor does it specify prerequisites or exclusions. The usage context is reasonably clear but lacks explicit comparison with sibling tools.

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

deep-researchA

Perform comprehensive deep research on any topic using AgentOracle Sonar Pro. Returns expert-level analysis with 10-15 detailed facts, in-depth analysis paragraph, cited sources, and confidence score. Costs $0.10 USDC per query via x402 on Base mainnet. Use for complex topics requiring thorough analysis.

ParametersJSON Schema

Name	Required	Description	Default
`query`	Yes	Natural language research question for deep analysis. Examples: 'Comprehensive analysis of x402 protocol adoption and market impact', 'Detailed comparison of AI agent frameworks in 2026 with pros and cons'

TDQS

A4.4/5.0

Behavior4/5

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

With no annotations provided, the description carries full burden and does well by disclosing key behavioral traits: the specific return format (10-15 facts, analysis paragraph, sources, confidence score), payment requirement ($0.10 USDC via x402), and that it's for complex topics. It doesn't mention rate limits or error conditions, keeping it from a perfect score.

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 perfectly concise and front-loaded: the first sentence states the core purpose, subsequent sentences add crucial details (output format, cost, usage context). Every sentence earns its place with no wasted words.

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?

For a single-parameter tool with no annotations and no output schema, the description provides excellent context about behavior, outputs, and usage. It falls short of perfect because it doesn't explicitly describe error cases or response structure details, though the output format is well-specified.

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 100%, so the schema already fully documents the single 'query' parameter. The description adds no additional parameter semantics beyond what's in the schema, maintaining the baseline score of 3 for adequate but not enhanced coverage.

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 ('perform comprehensive deep research') and resource ('any topic using AgentOracle Sonar Pro'). It distinguishes from the sibling 'research' tool by emphasizing 'comprehensive' analysis with specific outputs (10-15 facts, analysis paragraph, sources, confidence score).

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?

Explicit guidance is provided on when to use this tool ('for complex topics requiring thorough analysis') and when not to use it (implied: simpler topics should use the sibling 'research' tool). The cost disclosure ($0.10 per query) also helps determine appropriate usage contexts.

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

get-manifestA

Get the AgentOracle x402 payment manifest. Returns payment requirements, supported networks, pricing, and endpoint details for programmatic integration.

ParametersJSON Schema

Name	Required	Description	Default
No parameters

TDQS

A4/5.0

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. It discloses the return content (payment requirements, networks, pricing, endpoint details) but doesn't mention behavioral aspects like rate limits, authentication requirements, or whether this is a cached/static response versus dynamic data.

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?

Two efficient sentences that front-load the core purpose and follow with specific return details. Every word earns its place with zero redundancy or unnecessary elaboration.

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?

For a zero-parameter read-only tool with no output schema, the description provides good context about what information is returned. However, it could be more complete by mentioning whether this is static configuration data or requires authentication, given the lack of annotations.

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?

The tool has zero parameters with 100% schema coverage, so the baseline is 4. The description appropriately doesn't waste space discussing nonexistent parameters and focuses on what the tool returns instead.

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 specific action ('Get') and resource ('AgentOracle x402 payment manifest'), and distinguishes it from sibling tools by specifying it returns payment integration details rather than health checks or research functions.

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 implies usage context (programmatic integration needs) but doesn't explicitly state when to use this tool versus alternatives like check-health or research tools. No explicit exclusions or comparisons are provided.

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

researchA

Perform real-time research on any topic using AgentOracle. Returns structured results with summary, key facts, cited sources, and confidence score. Costs $0.02 USDC per query via x402 on Base mainnet.

ParametersJSON Schema

Name	Required	Description	Default
`query`	Yes	Natural language research question. Examples: 'What are the latest AI chip architectures?', 'Compare React vs Vue in 2026', 'Latest funding rounds in autonomous agents space'

TDQS

A4.2/5.0

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 behavioral traits: the tool performs real-time research, returns structured results with specific components (summary, key facts, cited sources, confidence score), and has a cost of $0.02 USDC per query via x402 on Base mainnet. This covers important operational aspects beyond basic functionality.

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 efficiently structured in two sentences that each earn their place: the first explains the core functionality and output format, the second discloses the cost mechanism. There's zero wasted text and it's appropriately front-loaded with the most important information.

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?

For a single-parameter tool with no annotations and no output schema, the description provides substantial context about behavior, output format, and cost. It could be slightly more complete by explicitly mentioning the 'deep-research' sibling tool alternative, but overall it's quite comprehensive given the tool's complexity level.

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% with comprehensive documentation of the single 'query' parameter including examples. The description doesn't add any parameter-specific information beyond what's already in the schema, so it meets the baseline expectation when the schema does the heavy lifting.

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 specific action ('perform real-time research'), resource ('any topic using AgentOracle'), and distinguishes from siblings by specifying the structured result format and cost mechanism. It goes beyond just restating the name 'research' to explain what the tool actually does.

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 about when to use this tool ('perform real-time research on any topic') and mentions the cost per query, which helps users understand usage implications. However, it doesn't explicitly differentiate when to use this versus the 'deep-research' sibling tool, which appears to be a related alternative.

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.

4 tool updatesv0.1.0Mar 23, 2026
- First observedcheck-health
- First observeddeep-research
- First observedget-manifest
- First observedresearch

Deploy Server

One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

TDQS

A4.2/5.0

Scored 2026-04-13 01:35 across 4 tools

Disambiguation5/5

Each tool has a clearly distinct purpose: check-health verifies API status, deep-research provides comprehensive analysis, get-manifest retrieves payment details, and research offers basic real-time research. There is no overlap in functionality, and the descriptions clearly differentiate their scopes and use cases.

Naming Consistency5/5

All tool names follow a consistent kebab-case pattern with clear verb-noun combinations: check-health, deep-research, get-manifest, and research. The naming is predictable and readable, with no deviations in style or convention.

Tool Count5/5

With 4 tools, the set is well-scoped for the AgentOracle MCP server's purpose, covering API health checks, payment integration, and two tiers of research functionality. Each tool earns its place without redundancy, making the count appropriate for the domain.

Completeness4/5

The tool surface covers core functionalities: API status, payment details, and research operations with basic and deep tiers. A minor gap exists in lacking explicit CRUD operations for managing research queries or payment settings, but agents can work around this given the server's focus on querying and integration.

Maintenance

ActivityInactive

ResponsivenessUnresponsive

Resources

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Appeared in Searches

Official Consensus.app MCP server for AI-powered research and academic search

agentoracle-mcp

インストール

Claude Desktopへの追加

Cursorへの追加

Windsurfへの追加

インストール後にできること

返される内容

検証の仕組み

料金

インストールせずに今すぐ試す

関連情報

Available Tools

Tool Schema Changelog

Maintenance

Resources

Related MCP Connectors

Related MCP Servers

Appeared in Searches