Josh Easter

import { createServer, tool } from 'mcp-framework';
import fs from 'node:fs';
import path from 'node:path';
import { cosineSimilarity, embed } from './pocembed';

// Load precomputed vectors created by the training script
const vectors = JSON.parse(
  fs.readFileSync(path.join(process.cwd(), 'vectors.json'), 'utf8')
) as Array<{
  id: string;
  text: string;
  vector: number[];
  source: string;
}>;

const searchDesignDocs = tool({
  name: 'search_design_docs',
  description: 'Semantic search over design system docs and guidelines',
  inputSchema: {
    type: 'object',
    required: ['query'],
    properties: {
      query: { type: 'string' },
      k: { type: 'number', default: 5 }
    },
  },
  handler: async ({ query, k = 5 }) => {
    const q = await embed(query);
    const scored = vectors
      .map((d) => ({ ...d, score: cosineSimilarity(q, d.vector) }))
      .sort((a, b) => b.score - a.score)
      .slice(0, k);

    return {
      results: scored.map((s) => ({
        source: s.source,
        score: Number(s.score.toFixed(3)),
        text: s.text
      })),
    };
  },
});

const server = createServer({
  name: 'company-design-system-mcp-poc',
  version: '0.0.1',
  tools: [searchDesignDocs],
});

server.start(8080);

// scripts/poc-train.ts
import fs from 'node:fs';
import path from 'node:path';
import matter from 'gray-matter';
import { embed } from './pocembed';

const docsDir = path.join(process.cwd(), 'docs/design-system');

function chunk(text: string, size = 800, overlap = 120) {
  const chunks: string[] = [];
  for (let i = 0; i < text.length; i += size - overlap) {
    chunks.push(text.slice(i, i + size));
  }
  return chunks;
}

async function main() {
  const files = fs.readdirSync(docsDir)
    .filter((f) => f.endsWith('.md') || f.endsWith('.mdx'));
  const out: any[] = [];

  for (const file of files) {
    const full = path.join(docsDir, file);
    const raw = fs.readFileSync(full, 'utf8');
    const { content } = matter(raw);

    for (const text of chunk(content)) {
      const vector = await embed(text);
      out.push({
        id: `${file}-${out.length}`,
        source: file,
        text,
        vector
      });
    }
  }

  fs.writeFileSync(
    path.join(process.cwd(), 'vectors.json'),
    JSON.stringify(out)
  );
}

main();