Sentiment Analysis

This example demonstrates how to wrap npm packages as tools for an LLM agent. We’ll use the popular sentiment package to give Claude the ability to perform quantitative sentiment analysis on text passages.

The Complete Example

import { open } from "thinkwell";
import * as fs from "fs/promises";
import Sentiment from "sentiment";

/**
 * A section of a document with its sentiment analysis.
 * @JSONSchema
 */
export interface DocumentSection {
  /** The section title */
  title: string;
  /** The sentiment score from the analysis tool */
  sentimentScore: number;
  /** A brief summary of the section */
  summary: string;
}

/**
 * Analysis of a document's sentiment and content.
 * @JSONSchema
 */
export interface DocumentAnalysis {
  /** The overall emotional tone of the document */
  overallTone: "positive" | "negative" | "mixed" | "neutral";
  /** Analysis of each section */
  sections: DocumentSection[];
  /** A recommendation based on the analysis */
  recommendation: string;
}

/**
 * A text passage to analyze.
 * @JSONSchema
 */
export interface TextPassage {
  /** The text passage to analyze */
  text: string;
}

// Initialize the sentiment analyzer (from the `sentiment` npm package)
const sentimentAnalyzer = new Sentiment();

async function main() {
  const agent = await open('claude');

  try {
    const feedback = await fs.readFile(
      new URL("feedback.txt", import.meta.url),
      "utf-8"
    );

    const analysis = await agent
      .think(DocumentAnalysis.Schema)
      .text(`
        Analyze the following customer feedback document.
        Use the sentiment analysis tool to measure the emotional tone of each section,
        then provide an overall analysis with recommendations.
      `)
      .quote(feedback, "feedback")

      // Custom tool: wraps the `sentiment` npm package as an MCP tool
      .tool(
        "analyze_sentiment",
        "Analyze the sentiment of a text passage.",
        TextPassage.Schema,
        async (passage) => {
          const result = sentimentAnalyzer.analyze(passage.text);
          return {
            score: result.score,
            comparative: result.comparative,
            positive: result.positive,
            negative: result.negative,
          };
        }
      )

      .run();

    console.log(`Overall Tone: ${analysis.overallTone}`);
    console.log(`Recommendation: ${analysis.recommendation}`);
  } finally {
    agent.close();
  }
}

main();

Run this example with:

npx thinkwell sentiment.ts

Using npm Packages as Tools

The key insight here is that any npm package can be wrapped as a tool. The sentiment package provides AFINN-based sentiment analysis, but the LLM doesn’t know how to use it directly. By wrapping it in a tool, we give the agent the ability to call this deterministic algorithm whenever it needs precise sentiment measurements.

import Sentiment from "sentiment";

const sentimentAnalyzer = new Sentiment();

// Later, in the agent call:
.tool(
  "analyze_sentiment",
  "Analyze the sentiment of a text passage.",
  TextPassage.Schema,
  async (passage) => {
    const result = sentimentAnalyzer.analyze(passage.text);
    return {
      score: result.score,
      comparative: result.comparative,
      positive: result.positive,
      negative: result.negative,
    };
  }
)

The tool receives a TextPassage object (validated against the schema) and returns the raw sentiment analysis result. The agent can then interpret these numbers in context.

Typed Tool Inputs with Schemas

Tools can accept structured input by providing a schema as the third argument. Here we define a TextPassage interface with the @JSONSchema decorator:

/**
 * A text passage to analyze.
 * @JSONSchema
 */
export interface TextPassage {
  /** The text passage to analyze */
  text: string;
}

When you pass TextPassage.Schema to the .tool() method, Thinkwell:

Exposes the schema to the LLM so it knows how to format tool calls
Validates incoming tool calls against the schema
Provides full TypeScript typing for the handler function’s passage parameter

This pattern ensures that tool inputs are always well-formed, and your handler code gets proper type checking.

Including Document Content with `.quote()`

The .quote() method adds content to the prompt in a clearly delimited format. This is ideal for including documents, user input, or any content that should be treated as data rather than instructions.

.quote(feedback, "feedback")

The second argument is an optional label that helps the LLM understand what the quoted content represents. In the prompt, this renders as a clearly marked block that the agent can reference. Without a label:

.quote(someContent)

With a label for clarity:

.quote(feedback, "customer-feedback")
.quote(policy, "company-policy-document")

Complex Schema Patterns

This example showcases several schema features working together:

Nested Types

The DocumentAnalysis schema contains an array of DocumentSection objects:

/**
 * A section of a document with its sentiment analysis.
 * @JSONSchema
 */
export interface DocumentSection {
  title: string;
  sentimentScore: number;
  summary: string;
}

/**
 * Analysis of a document's sentiment and content.
 * @JSONSchema
 */
export interface DocumentAnalysis {
  overallTone: "positive" | "negative" | "mixed" | "neutral";
  sections: DocumentSection[];
  recommendation: string;
}

Thinkwell automatically resolves references between schemas when generating the JSON Schema for the LLM.

String Literal Unions (Enums)

TypeScript string literal unions become enum constraints in the generated schema:

overallTone: "positive" | "negative" | "mixed" | "neutral";

This ensures the LLM can only return one of these four values, and TypeScript knows the type is constrained to these literals.

Descriptive JSDoc Comments

Every property should have a JSDoc comment explaining its purpose:

/** The sentiment score from the analysis tool */
sentimentScore: number;

These descriptions appear in the generated JSON Schema and help the LLM understand what each field should contain. Good descriptions lead to better, more consistent outputs.

Why Wrap Packages as Tools?

LLMs are powerful reasoners but they can struggle with precise calculations or algorithms that require exact execution. By wrapping deterministic packages as tools, you get the best of both worlds:

The LLM handles understanding context, breaking down the document into sections, and synthesizing a recommendation
The npm package handles the precise, algorithmic work of calculating sentiment scores

This pattern applies to many use cases: date parsing, math operations, data validation, API calls, database queries, and more. If there’s an npm package that does something well, you can make it available to your agent as a tool.