Query Dataset

POST

mcp

query

Query Dataset

curl --request POST \
  --url https://api.fltr.com/v1/mcp/query \
  --header 'Authorization: <authorization>' \
  --header 'Content-Type: application/json' \
  --data '
{
  "query": "<string>",
  "dataset_id": "<string>",
  "limit": 123,
  "rerank": true,
  "filters": {}
}
'

import requests

url = "https://api.fltr.com/v1/mcp/query"

payload = {
    "query": "<string>",
    "dataset_id": "<string>",
    "limit": 123,
    "rerank": True,
    "filters": {}
}
headers = {
    "Authorization": "<authorization>",
    "Content-Type": "application/json"
}

response = requests.post(url, json=payload, headers=headers)

print(response.text)

const options = {
  method: 'POST',
  headers: {Authorization: '<authorization>', 'Content-Type': 'application/json'},
  body: JSON.stringify({
    query: '<string>',
    dataset_id: '<string>',
    limit: 123,
    rerank: true,
    filters: {}
  })
};

fetch('https://api.fltr.com/v1/mcp/query', options)
  .then(res => res.json())
  .then(res => console.log(res))
  .catch(err => console.error(err));

<?php

$curl = curl_init();

curl_setopt_array($curl, [
  CURLOPT_URL => "https://api.fltr.com/v1/mcp/query",
  CURLOPT_RETURNTRANSFER => true,
  CURLOPT_ENCODING => "",
  CURLOPT_MAXREDIRS => 10,
  CURLOPT_TIMEOUT => 30,
  CURLOPT_HTTP_VERSION => CURL_HTTP_VERSION_1_1,
  CURLOPT_CUSTOMREQUEST => "POST",
  CURLOPT_POSTFIELDS => json_encode([
    'query' => '<string>',
    'dataset_id' => '<string>',
    'limit' => 123,
    'rerank' => true,
    'filters' => [
        
    ]
  ]),
  CURLOPT_HTTPHEADER => [
    "Authorization: <authorization>",
    "Content-Type: application/json"
  ],
]);

$response = curl_exec($curl);
$err = curl_error($curl);

curl_close($curl);

if ($err) {
  echo "cURL Error #:" . $err;
} else {
  echo $response;
}

package main

import (
	"fmt"
	"strings"
	"net/http"
	"io"
)

func main() {

	url := "https://api.fltr.com/v1/mcp/query"

	payload := strings.NewReader("{\n  \"query\": \"<string>\",\n  \"dataset_id\": \"<string>\",\n  \"limit\": 123,\n  \"rerank\": true,\n  \"filters\": {}\n}")

	req, _ := http.NewRequest("POST", url, payload)

	req.Header.Add("Authorization", "<authorization>")
	req.Header.Add("Content-Type", "application/json")

	res, _ := http.DefaultClient.Do(req)

	defer res.Body.Close()
	body, _ := io.ReadAll(res.Body)

	fmt.Println(string(body))

}

HttpResponse<String> response = Unirest.post("https://api.fltr.com/v1/mcp/query")
  .header("Authorization", "<authorization>")
  .header("Content-Type", "application/json")
  .body("{\n  \"query\": \"<string>\",\n  \"dataset_id\": \"<string>\",\n  \"limit\": 123,\n  \"rerank\": true,\n  \"filters\": {}\n}")
  .asString();

require 'uri'
require 'net/http'

url = URI("https://api.fltr.com/v1/mcp/query")

http = Net::HTTP.new(url.host, url.port)
http.use_ssl = true

request = Net::HTTP::Post.new(url)
request["Authorization"] = '<authorization>'
request["Content-Type"] = 'application/json'
request.body = "{\n  \"query\": \"<string>\",\n  \"dataset_id\": \"<string>\",\n  \"limit\": 123,\n  \"rerank\": true,\n  \"filters\": {}\n}"

response = http.request(request)
puts response.read_body

{
  "results": [
    {}
  ],
  "results[].chunk_id": "<string>",
  "results[].content": "<string>",
  "results[].score": 123,
  "results[].metadata": {},
  "results[].document_id": "<string>",
  "query_time_ms": 123
}

Request

Performs semantic search combining vector similarity and keyword matching.

Headers

string

required

Bearer token for authentication

Body

string

required

Search query or question (max 1000 characters)

string

required

Dataset to search

integer

default:5

Number of results to return (max: 50)

boolean

default:false

Enable Cohere reranking for better quality

object

Filter by metadata fields

Response

array

Array of search results

string

Unique chunk identifier

string

Text content of the chunk

number

Relevance score (0-1, higher is better)

object

Document metadata

string

Parent document ID

integer

Query execution time in milliseconds

Examples

Basic Query

cURL

curl -X POST https://api.fltr.com/v1/mcp/query \
  -H "Authorization: Bearer fltr_sk_abc123..." \
  -H "Content-Type: application/json" \
  -d '{
    "query": "How do I authenticate with FLTR?",
    "dataset_id": "ds_abc123",
    "limit": 3
  }'

Python

response = requests.post(
    "https://api.fltr.com/v1/mcp/query",
    headers={
        "Authorization": "Bearer fltr_sk_abc123...",
        "Content-Type": "application/json"
    },
    json={
        "query": "How do I authenticate with FLTR?",
        "dataset_id": "ds_abc123",
        "limit": 3
    }
)

results = response.json()
for result in results['results']:
    print(f"[{result['score']:.2f}] {result['metadata']['title']}")
    print(result['content'][:200])

JavaScript

const response = await fetch("https://api.fltr.com/v1/mcp/query", {
  method: "POST",
  headers: {
    "Authorization": "Bearer fltr_sk_abc123...",
    "Content-Type": "application/json"
  },
  body: JSON.stringify({
    query: "How do I authenticate with FLTR?",
    dataset_id: "ds_abc123",
    limit: 3
  })
});

const data = await response.json();
data.results.forEach(result => {
  console.log(`[${result.score}] ${result.metadata.title}`);
});

With Reranking

{
  "query": "authentication methods",
  "dataset_id": "ds_abc123",
  "limit": 10,
  "rerank": true
}

With Filters

{
  "query": "API documentation",
  "dataset_id": "ds_abc123",
  "limit": 5,
  "filters": {
    "category": "tutorial",
    "published": true
  }
}

Response

{
  "results": [
    {
      "chunk_id": "ch_xyz789",
      "content": "FLTR supports three authentication methods: API keys for services, OAuth 2.1 for MCP clients, and session tokens for web apps...",
      "score": 0.89,
      "metadata": {
        "title": "Authentication Guide",
        "category": "security",
        "url": "https://docs.fltr.com/auth"
      },
      "document_id": "doc_abc123"
    },
    {
      "chunk_id": "ch_abc456",
      "content": "To authenticate API requests, include your API key in the Authorization header: Bearer fltr_sk_...",
      "score": 0.82,
      "metadata": {
        "title": "API Keys",
        "category": "security"
      },
      "document_id": "doc_def456"
    }
  ],
  "query_time_ms": 45
}

Search Algorithm

FLTR uses hybrid search combining:

Vector Search - Semantic similarity using embeddings
Keyword Search - BM25 for exact matches
Fusion - RRF (Reciprocal Rank Fusion) to combine results

Optional Cohere reranking provides additional quality improvement.

Scoring

Scores range from 0 to 1:

0.9-1.0 - Excellent match
0.7-0.9 - Good match
0.5-0.7 - Moderate match
Below 0.5 - Weak match

Performance

Average latency: 50-200ms
With reranking: +100-300ms
Timeout: 10 seconds

Limits

Max query length: 1,000 characters
Max results: 50 per request
Filters: 10 fields maximum

Tips

Use natural language questions
Include context in your query
Enable reranking for better quality
Filter by metadata to narrow results
Request 3-5 results for most use cases

Query Dataset

curl --request POST \
  --url https://api.fltr.com/v1/mcp/query \
  --header 'Authorization: <authorization>' \
  --header 'Content-Type: application/json' \
  --data '
{
  "query": "<string>",
  "dataset_id": "<string>",
  "limit": 123,
  "rerank": true,
  "filters": {}
}
'

import requests

url = "https://api.fltr.com/v1/mcp/query"

payload = {
    "query": "<string>",
    "dataset_id": "<string>",
    "limit": 123,
    "rerank": True,
    "filters": {}
}
headers = {
    "Authorization": "<authorization>",
    "Content-Type": "application/json"
}

response = requests.post(url, json=payload, headers=headers)

print(response.text)

const options = {
  method: 'POST',
  headers: {Authorization: '<authorization>', 'Content-Type': 'application/json'},
  body: JSON.stringify({
    query: '<string>',
    dataset_id: '<string>',
    limit: 123,
    rerank: true,
    filters: {}
  })
};

fetch('https://api.fltr.com/v1/mcp/query', options)
  .then(res => res.json())
  .then(res => console.log(res))
  .catch(err => console.error(err));

<?php

$curl = curl_init();

curl_setopt_array($curl, [
  CURLOPT_URL => "https://api.fltr.com/v1/mcp/query",
  CURLOPT_RETURNTRANSFER => true,
  CURLOPT_ENCODING => "",
  CURLOPT_MAXREDIRS => 10,
  CURLOPT_TIMEOUT => 30,
  CURLOPT_HTTP_VERSION => CURL_HTTP_VERSION_1_1,
  CURLOPT_CUSTOMREQUEST => "POST",
  CURLOPT_POSTFIELDS => json_encode([
    'query' => '<string>',
    'dataset_id' => '<string>',
    'limit' => 123,
    'rerank' => true,
    'filters' => [
        
    ]
  ]),
  CURLOPT_HTTPHEADER => [
    "Authorization: <authorization>",
    "Content-Type: application/json"
  ],
]);

$response = curl_exec($curl);
$err = curl_error($curl);

curl_close($curl);

if ($err) {
  echo "cURL Error #:" . $err;
} else {
  echo $response;
}

package main

import (
	"fmt"
	"strings"
	"net/http"
	"io"
)

func main() {

	url := "https://api.fltr.com/v1/mcp/query"

	payload := strings.NewReader("{\n  \"query\": \"<string>\",\n  \"dataset_id\": \"<string>\",\n  \"limit\": 123,\n  \"rerank\": true,\n  \"filters\": {}\n}")

	req, _ := http.NewRequest("POST", url, payload)

	req.Header.Add("Authorization", "<authorization>")
	req.Header.Add("Content-Type", "application/json")

	res, _ := http.DefaultClient.Do(req)

	defer res.Body.Close()
	body, _ := io.ReadAll(res.Body)

	fmt.Println(string(body))

}

HttpResponse<String> response = Unirest.post("https://api.fltr.com/v1/mcp/query")
  .header("Authorization", "<authorization>")
  .header("Content-Type", "application/json")
  .body("{\n  \"query\": \"<string>\",\n  \"dataset_id\": \"<string>\",\n  \"limit\": 123,\n  \"rerank\": true,\n  \"filters\": {}\n}")
  .asString();

require 'uri'
require 'net/http'

url = URI("https://api.fltr.com/v1/mcp/query")

http = Net::HTTP.new(url.host, url.port)
http.use_ssl = true

request = Net::HTTP::Post.new(url)
request["Authorization"] = '<authorization>'
request["Content-Type"] = 'application/json'
request.body = "{\n  \"query\": \"<string>\",\n  \"dataset_id\": \"<string>\",\n  \"limit\": 123,\n  \"rerank\": true,\n  \"filters\": {}\n}"

response = http.request(request)
puts response.read_body

{
  "results": [
    {}
  ],
  "results[].chunk_id": "<string>",
  "results[].content": "<string>",
  "results[].score": 123,
  "results[].metadata": {},
  "results[].document_id": "<string>",
  "query_time_ms": 123
}

Overview

Datasets

Documents

MCP Endpoints

Webhooks

Request

Headers

Body

Response

Examples

Basic Query

With Reranking

With Filters

Response

Search Algorithm

Scoring

Performance

Limits

Tips

​Request

​Headers

​Body

​Response

​Examples

​Basic Query

​With Reranking

​With Filters

​Response

​Search Algorithm

​Scoring

​Performance

​Limits

​Tips

Request

Headers

Body

Response

Examples

Basic Query

With Reranking

With Filters

Response

Search Algorithm

Scoring

Performance

Limits

Tips