JMT x402 MCP Server

social_sentiment

Analyze social media sentiment across multiple platforms for any topic using a large language model.

Instructions

Cross-platform social sentiment analysis with LLM. Price: $0.05/call via x402 (USDC on Base mainnet).

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "topic": "<topic>"
    },
    "name": "social_sentiment"
  }
}

Input Schema

Name	Required	Description	Default
`topic`	Yes	Topic to analyze

Schema Changelog

Changes observed during successful MCP inspections.

First observedv1.0.0Jul 13, 2026

TDQS

C2.9/5.0

Behavior2/5

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

With no annotations, the description must carry the full burden of behavioral disclosure. It mentions a price and payment method, which is useful, but does not state that the tool is read-only or idempotent, nor does it describe any side effects, rate limits, or authentication needs. The agent is left uncertain about safety.

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 a single concise sentence that front-loads the purpose. Every word earns its place, though the pricing detail, while useful, is secondary to the function. It is not overly verbose but could include more context without being long.

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

Completeness2/5

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

Given the tool's simplicity (one parameter, no output schema), the description is minimally adequate but incomplete. It does not explain what the output looks like (e.g., sentiment scores, labels), which platforms are covered, or how to interpret results. The agent lacks enough information to reliably use the tool.

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 input schema has 100% coverage: the single 'topic' parameter is described as 'Topic to analyze'. The description adds no additional meaning beyond the schema. Baseline score of 3 is appropriate.

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 cross-platform social sentiment analysis using an LLM. The verb 'analysis' is implied, and 'social sentiment' distinguishes it from many sibling tools like stock_quote or news_briefing. However, it does not explicitly differentiate from similar tools like crypto_research or market_pulse, so it's not a 5.

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?

No guidance is provided on when to use this tool versus alternatives. There is no mention of prerequisites, when it is appropriate, or when to avoid it. The description only states what it does and the price, offering no decision support for the agent.

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