search-records.ts•2.08 kB
import {McpServer} from '@modelcontextprotocol/sdk/server/mcp.js';
import {Pinecone} from '@pinecone-database/pinecone';
import {z} from 'zod';
import {RERANK_MODEL_SCHEMA} from './common/rerank-model.js';
import {SEARCH_QUERY_SCHEMA} from './common/search-query.js';
const INSTRUCTIONS = 'Search an index for records that are similar to the query text';
const RERANK_SCHEMA = z
.object({
model: RERANK_MODEL_SCHEMA,
topN: z
.number()
.optional()
.describe(
`The number of results to return after reranking. Must be less than or
equal to the value of "query.topK".`,
),
rankFields: z.array(z.string()).describe(
`The fields to rerank on. This should include the field name specified
in the index's "fieldMap". The "bge-reranker-v2-m3" and
"pinecone-rerank-v0" models support only a single rerank field.
"cohere-rerank-3.5" supports multiple rerank fields.`,
),
query: z
.string()
.optional()
.describe(
`An optional query to rerank documents against. If not specified, the
same query will be used for both the initial search and the reranking.`,
),
})
.optional()
.describe(
`Reranking can help determine which of the returned records are most
relevant. When reranking, use a "query" with a "topK" that returns more
results than you need; then use "rerank" to select the most relevant
"topN" results.`,
);
const SCHEMA = {
name: z.string().describe('The index to search.'),
namespace: z.string().describe('The namespace to search.'),
query: SEARCH_QUERY_SCHEMA,
rerank: RERANK_SCHEMA,
};
export function addSearchRecordsTool(server: McpServer, pc: Pinecone) {
server.tool('search-records', INSTRUCTIONS, SCHEMA, async ({name, namespace, query, rerank}) => {
const ns = pc.index(name).namespace(namespace);
const results = await ns.searchRecords({query, rerank});
return {
content: [
{
type: 'text',
text: JSON.stringify(results, null, 2),
},
],
};
});
}