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mountainmystic

Hedera Toolbox

by mountainmystic
OverviewSchemaRelated ServersScoreDiscussions
JavaScript
Remote

hcs_understand

Analyze Hedera Consensus Service topics to detect anomalies, identify trends, extract entities, or assess risks using historical data patterns.

Instructions

Deep pattern analysis of an HCS topic - anomaly detection, trend analysis, entity extraction, or risk assessment. Costs 0.50 HBAR.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
api_keyYesYour HederaIntel API key
analysis_typeYesType of analysis to perform
topic_idNoHedera topic ID. Defaults to the HederaIntel platform topic.
lookback_daysNoDays of history to analyze (default 7, max 30)

Implementation Reference

  • src/modules/hcs/tools.js:91-104 (handler)
    The handler for hcs_understand that processes the topic messages using deepAnalyze.
    if (name === "hcs_understand") {
  const payment = chargeForTool("hcs_understand", args.api_key);
  const topicId = args.topic_id || PLATFORM_TOPIC;
  const messages = await getTopicMessages(topicId, 100);
  const analysis = await deepAnalyze(messages, args.analysis_type);
  return {
    topic_id: topicId,
    analysis_type: args.analysis_type,
    messages_analyzed: messages.length,
    ...analysis,
    payment,
    timestamp: new Date().toISOString(),
  };
}
  • src/modules/hcs/tools.js:36-52 (schema)
    The MCP schema definition for hcs_understand tool.
      name: "hcs_understand",
  description: "Deep pattern analysis of an HCS topic - anomaly detection, trend analysis, entity extraction, or risk assessment. Costs 1.0 HBAR.",
  inputSchema: {
    type: "object",
    properties: {
      topic_id: { type: "string", description: "Hedera topic ID. Defaults to the HederaIntel platform topic." },
      analysis_type: {
        type: "string",
        enum: ["anomaly_detection", "trend_analysis", "entity_extraction", "risk_assessment"],
        description: "Type of analysis to perform",
      },
      lookback_days: { type: "number", description: "Days of history to analyze (default 7, max 30)" },
      api_key: { type: "string", description: "Your HederaIntel API key" },
    },
    required: ["analysis_type", "api_key"],
  },
},

Tool Definition Quality

B3.2/5.0
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 usefully adds cost information ('Costs 0.50 HBAR'), which is valuable operational context. However, it doesn't describe what the analysis returns, potential rate limits, authentication requirements beyond the api_key parameter, or error conditions. The description provides some behavioral context but leaves significant gaps.

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 concise with two clear sentences. The first sentence states the purpose and scope, while the second provides important cost information. There's no wasted language, though it could be slightly more structured with clearer separation of concepts.

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?

For a 4-parameter analysis tool with no annotations and no output schema, the description provides basic purpose and cost information but lacks critical details about what the analysis returns, error handling, or performance characteristics. The 100% schema coverage helps with parameters, but the overall context for using this tool remains incomplete given its analytical nature and lack of output documentation.

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 documents all parameters thoroughly. The description doesn't add any meaningful parameter semantics beyond what's in the schema - it mentions analysis types but these are already in the enum, and it doesn't explain parameter interactions or provide usage examples. This meets the baseline for high schema coverage.

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 performs 'Deep pattern analysis of an HCS topic' with specific analysis types listed (anomaly detection, trend analysis, entity extraction, risk assessment), providing a clear verb+resource combination. However, it doesn't explicitly differentiate from sibling HCS tools like hcs_audit_trail, hcs_monitor, hcs_query, hcs_verify_record, or hcs_write_record, which prevents a perfect score.

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?

The description provides no guidance on when to use this tool versus alternatives. While it mentions the analysis types, it doesn't specify scenarios where this tool is preferred over other HCS tools or when it should be avoided. There's no mention of prerequisites or alternative tools for similar tasks.

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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