| Domain | Use case unlocked by MCP tool | Previous friction |
|---|---|---|
| Commerce / payments | - Add an item to a Shopify cart and hand back a checkout URL in one turn — "Add the Allbirds Men’s Tree Dasher 2 in size 10" → cart link - Generate a Stripe payment link | Function calling meant you had to write a custom cart_add or create_payment_link wrapper and host your own relay server. |
| Dev-ops & code quality | - Ask Sentry for the latest error in a particular file, then open a GitHub issue with a suggested fix in the same conversation | Chaining two different third-party APIs inside one assistive loop involved webhook glue and state juggling. |
| Messaging / notifications | - Grab the morning’s top soccer headlines via web-search and have Twilio text the summary to a phone number in a single call | Required stitching two tool calls in your backend and batching the final SMS payload yourself. |
curl https://api.openai.com/v1/responses -i \
-H "Content-Type: application/json" \
-H "Authorization: Bearer $OPENAI_API_KEY" \
-d '{
"model": "gpt-4.1",
"tools": [
{
"type": "mcp",
"server_label": "gitmcp",
"server_url": "https://gitmcp.io/openai/tiktoken",
"allowed_tools": ["search_tiktoken_documentation", "fetch_tiktoken_documentation"],
"require_approval": "never"
}
],
"input": "how does tiktoken work?"
}'Scenario 1: non-reasoning model
curl https://api.openai.com/v1/responses \
-H "Content-Type: application/json" \
-H "Authorization: Bearer $OPENAI_API_KEY" \
-d '{
"model": "gpt-4.1",
"tools": [
{
"type": "mcp",
"server_label": "gitmcp",
"server_url": "https://gitmcp.io/openai/tiktoken",
"require_approval": "never"
}
],
"input": "how does tiktoken work?"
}' "usage": {
"input_tokens": 280,
"input_tokens_details": {
"cached_tokens": 0
},
"output_tokens": 665,
"output_tokens_details": {
"reasoning_tokens": 0
},
"total_tokens": 945
}Scenario 2: reasoning model without previous_response_id
curl https://api.openai.com/v1/responses \
-H "Content-Type: application/json" \
-H "Authorization: Bearer $OPENAI_API_KEY" \
-d '{
"model": "o4-mini",
"tools": [
{
"type": "mcp",
"server_label": "gitmcp",
"server_url": "https://gitmcp.io/openai/tiktoken",
"require_approval": "never"
}
],
"input": "how does tiktoken work?",
"reasoning": {
"effort": "medium",
"summary": "auto"
}
}' "usage": {
"input_tokens": 36436,
"input_tokens_details": {
"cached_tokens": 22964
},
"output_tokens": 1586,
"output_tokens_details": {
"reasoning_tokens": 576
},
"total_tokens": 38022
}Using MCP with other tools
The MCP tool is just another entry in the tools array, so the model can use it seamlessly with other hosted tools like code_interpreter, web_search_preview, or image_generation, and with any custom tools you define. You can also use multiple remote MCP servers together.
In this example, we’ll create an agent that is a pricing analyst for a fictional yoga attire store: it first pulls current competitor prices for women’s shorts, yoga pants, and tank tops from the Alo Yoga MCP server, then grabs the price for the same three categories from Uniqlo via the hosted web-search tool. Using Code Interpreter it analyzes last week’s sales from a CSV that was pre-loaded with the Files endpoint, in order to calculate per-item revenue and average order value. Then it measures each item’s price gap versus the newly fetched Uniqlo and Alo Yoga benchmarks. Any product priced 15 percent or more above or below market is flagged, and the agent delivers a concise text report summarizing the discrepancies and key revenue stats.
system_prompt= """You are a pricing analyst for my clothing company. Please use the MCP tool
to fetch prices from the Alo Yoga MCP server for the categories of women's
shorts, yoga pants, and tank tops. Use only the MCP server for Alo yoga data, don't search the web.
Next, use the web search tool to search for Uniqlo prices for women's shorts, yoga pants, and tank tops.
In each case for Alo Yoga and Uniqlo, extract the
price for the top result in each category. Also provide the full URLs
Using the uploaded CSV file of sales data from my store, and with the
code interpreter tool calculate revenue by product item, compute average order-value on a transaction level, and calculate the percentage price gap between the CSV data and Uniqlo/Alo Yoga prices.
Flag products priced 15% or more above or below the market.
Create and output a short report including the findings.
# Steps
1. **Fetch Alo Yoga Prices:**
- Use the Alo Yoga MCP server to fetch prices for the following products:
High-Waist Airlift Legging
Sway Bra Tank
5" Airlift Energy Short
- Ensure you find prices for each.
- Extract the price of the top result for each category.
- include URL links
2. **Query Uniqlo Prices:**
- Use the Web-Search tool to search non-sale prices for the following Uniqlo products:
Women's AIRism Soft Biker Shorts
Women's AIRism Soft Leggings
Women's AIRism Bra Sleeveless Top
- Ensure you find non-sale prices for each.
- Extract the price for the top result in each category.
- include URL links
3. **Sales Data Analysis:**
- Use the uploaded CSV sales data to calculate revenue across each
product item.
- Determine the average order-value on a transaction level.
- For each SKU, compute the percentage price gap between the
CSV data and Uniqlo/Alo Yoga prices.
- Flag products priced ≥ 15% above or below the market.
4. **Report:**
- Compile and output a report including the flagging results
# Output Format
- A short text report explaining:
- Any products that are priced ≥ 15% above or below the market,
with specific details. """Here's a sample curl with a placeholder for the above system prompt.
curl https://api.openai.com/v1/responses \
-H "Content-Type: application/json" \
-H "Authorization: Bearer $OPENAI_API_KEY" \
-d '{
"model": "gpt-4.1",
"input": [
{
"role": "system",
"content": [
{
"type": "input_text",
"text": "ABOVE_SYSTEM_PROMPT"
}
]
}
],
"tools": [
{
"type": "web_search_preview",
"user_location": {
"type": "approximate",
"country": "US"
},
"search_context_size": "medium"
},
{
"type": "code_interpreter",
"container": {
"type": "auto",
"file_ids": [
"file-WTiyGcZySaU6n218gj4XxR"
]
}
},
{
"type": "mcp",
"server_url": "https://www.aloyoga.com/api/mcp",
"server_label": "aloyoga",
"allowed_tools": [
"search_shop_catalog",
"get_product_details"
],
"require_approval": "never"
}
],
"temperature": 1,
"max_output_tokens": 2048,
"top_p": 1,
"store": true
}'
The model is able to carry forward it’s results from the MCP tool and web search into the code interpreter steps to produce a report with the following content that is formatted for legibility:
Pricing Comparison and Revenue Analysis Report
Your Store's Sales & Price Analysis
- Revenue by Product:
- Your Store's Average Selling Price by Category:
Pricing Gaps vs Market
| Category | Store Avg Price | vs Alo Yoga Gap (%) | Flagged (≥15%) | vs Uniqlo Gap (%) | Flagged (≥15%) |
|---|---|---|---|---|---|
| Shorts | $60.00 | -31.8% | YES | +100.7% | YES |
| Tank tops | $75.00 | -14.8% | +114.9% | YES | |
| Yoga pants | $110.00 | -14.1% | +267.9% | YES |
Recommendations & Flags
Flagged products (≥15% price gap):
Shorts are priced significantly below premium competitors (Alo Yoga), but far higher than budget alternatives (Uniqlo). If you want to compete in the premium segment, consider increasing your price. If you want to target budget buyers, a price decrease could be justifiable. Most of your tank tops and yoga pants are similarly positioned—much lower than Alo, but well above Uniqlo.
Prompting guidelines to improve MCP tool calls
Depending on your use case you might find that the model invokes many MCP calls, for instance when using catalog-search tools. To avoid endless iterations over large product inventories it’s helpful to instruct the model to limit it’s search to N items and to offer to continue only when the user explicitly asks for more information. This keeps responses focused and snappy.
If the MCP servers you’re using include exhaustive mcp_list_tools, it’s also worth Including some targeted few-shot examples to show the model how to choose the correct server and to stop once it has what it needs, instead of issuing redundant calls.
Finally, adding guidance to remind the model that if essential information (size, color, product line, etc.) is missing from the user query, it should ask a follow-up question rather than launching a broad search. This small prompt nudge reduces unnecessary tool calls and improves answer quality. Here’s a sample prompt that shows how these guidelines come together:
system_prompt= """You are an AI assistant that can call the following MCP servers:
1. allbirds_store
2. gitmcp
Steps
1. Use the MCP servers above to answer the user query, not every MCP server will relevant for a given query so you can choose which ones to invoke.
2. If the user’s request lacks essential details (size, color, etc.), ask a follow-up question first rather than guessing and using the MCP server tool prematurely.
3. If searching for products in a catalog, only return 4 results at a time before asking the user whether they want to see more.
### Few-shot examples
# EX1 — Direct product search, then fetch variant details
user: Do you have the Allbirds Tree Runner in men’s size 10?
assistant: {"name":"allbirds_store.search_shop_catalog",
"arguments":{"query":"Tree Runner",
"context":"Men’s size 10 shoes"}}
# (assume the search returns product_id "gid://shopify/Product/987")
assistant: {"name":"allbirds_store.get_product_details",
"arguments":{"product_id":"gid://shopify/Product/987",
"options":{"Size":"10"}}}
# EX2 — Clarify missing size, then two-step color variant lookup
user: I want the Tree Runner in blue.
assistant: Sure — what size do you need?
user: Size 10.
assistant: {"name":"allbirds_store.search_shop_catalog",
"arguments":{"query":"Tree Runner",
"context":"Blue variant, size 10"}}
# (assume the search returns product_id "gid://shopify/Product/987")
assistant: {"name":"allbirds_store.get_product_details",
"arguments":{"product_id":"gid://shopify/Product/987",
"options":{"Size":"10","Color":"Blue"}}}
# EX3 — Git docs: search then fetch specific file
user: Can you show me how tiktoken does byte-pair encoding?
assistant: {"name":"gitmcp.search_website_documentation",
"arguments":{"query":"tiktoken byte-pair encoding"}}
# (assume the search returns document_id "docs/ENCODING.md")
assistant: {"name":"gitmcp.fetch_website_documentation",
"arguments":{"document_id":"docs/ENCODING.md"}} """
curl https://api.openai.com/v1/responses \
-H "Content-Type: application/json" \
-H "Authorization: Bearer $OPENAI_API_KEY" \
-d '{
"model": "gpt-4.1",
"input": [
{
"role": "system",
"content": [
{
"type": "input_text",
"text": "ABOVE_SYSTEM_PROMPT"
}
]
},
{
"role": "user",
"content": [
{
"type": "input_text",
"text": "find me womens tree loungers in size 8"
}
]
}
],
"tools": [
{
"type": "mcp",
"server_url": "https://www.allbirds.com/api/mcp",
"server_label": "allbirds",
"allowed_tools": [
"search_shop_catalog",
"get_cart",
"update_cart",
"search_shop_policies_and_faqs",
"get_product_details"
],
"require_approval": "never"
},
{
"type": "mcp",
"server_label": "gitmcp",
"server_url": "https://gitmcp.io/openai/tiktoken",
"allowed_tools": [
"fetch_tiktoken_documentation",
"search_tiktoken_documentation",
"search_tiktoken_code",
"fetch_generic_url_content"
],
"require_approval": "never"
}
],
"temperature": 1,
"max_output_tokens": 2048
}'
结论
The hosted MCP tool in the Responses API turns external-service access from a bespoke plumbing task into a first-class capability of the API. By connecting to a remote server, letting the runtime cache its tool list, and trimming that list with allowed_tools, you eliminate the extra network hop, cut token overhead, and give the model a concise, discoverable action set. When combined with built-in tools such as code_interpreter, web_search_preview, or image_gen, MCP unlocks rich, multi-service workflows whether you’re analyzing sales data, triaging production errors, or automating checkout flows.