URL: https://gofastmcp.com/servers/transforms/prompts-as-tools
Title: Prompts as Tools - FastMCP

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Basic Usage
Listing Prompts
Getting Prompts
Message Format
Binary Content
Working with Tools
Prompts as Tools
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Expose prompts to tool-only clients
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New in version
3.0.0
Some MCP clients only support tools. They cannot list or get prompts directly because they lack prompt protocol support. The
PromptsAsTools
transform bridges this gap by generating tools that provide access to your server’s prompts.
When you add
PromptsAsTools
to a server, it creates two tools that clients can call instead of using the prompt protocol:
list_prompts
returns JSON describing all available prompts and their arguments
get_prompt
renders a specific prompt with provided arguments
This means any client that can call tools can now access prompts, even if the client has no native prompt support.
​
Basic Usage
Pass your FastMCP server to
PromptsAsTools
when adding the transform. The generated tools route through the server at runtime, which means all server middleware — auth, visibility, rate limiting — applies to prompt operations automatically, exactly as it would for direct
prompts/get
calls.
PromptsAsTools
(and
ResourcesAsTools
) should be applied to a FastMCP server instance, not a raw Provider. The generated tools call back into the server’s middleware chain at runtime, so they need a server to route through. If you want to expose only a subset of prompts, create a dedicated FastMCP server for those prompts and apply the transform there.
from
fastmcp
import
FastMCP
from
fastmcp
.
server
.
transforms
import
PromptsAsTools
mcp
=
FastMCP
(
"
My Server
"
)
@
mcp
.
prompt
def
analyze_code
(
code
:
str
,
language
:
str
=
"
python
"
)
->
str
:
"""
Analyze code for potential issues.
"""
return
f
"Analyze this
{
language
}
code:
\n
{
code
}
"
@
mcp
.
prompt
def
explain_concept
(
concept
:
str
)
->
str
:
"""
Explain a programming concept.
"""
return
f
"Explain:
{
concept
}
"
# Add the transform - creates list_prompts and get_prompt tools
mcp
.
add_transform
(
PromptsAsTools
(
mcp
))
Clients now see three items: whatever tools you defined directly, plus
list_prompts
and
get_prompt
.
​
Listing Prompts
The
list_prompts
tool returns JSON with metadata for each prompt, including its arguments.
result
=
await
client
.
call_tool
(
"
list_prompts
"
,
{})
prompts
=
json
.
loads
(
result
.
data
)
# [
# {
# "name": "analyze_code",
# "description": "Analyze code for potential issues.",
# "arguments": [
# {"name": "code", "description": null, "required": true},
# {"name": "language", "description": null, "required": false}
# ]
# },
# {
# "name": "explain_concept",
# "description": "Explain a programming concept.",
# "arguments": [
# {"name": "concept", "description": null, "required": true}
# ]
# }
#]
Each argument includes:
name
: The argument name
description
: Optional description from type hints or docstrings
required
: Whether the argument must be provided
​
Getting Prompts
The
get_prompt
tool accepts a prompt name and optional arguments dict. It returns the rendered prompt as JSON with a messages array.
# Prompt with required and optional arguments
result
=
await
client
.
call_tool
(
"
get_prompt
"
,
{
"
name
"
:
"
analyze_code
"
,
"
arguments
"
:
{
"
code
"
:
"
x = 1
\n
print(x)
"
,
"
language
"
:
"
python
"
}
}
)
response
=
json
.
loads
(
result
.
data
)
# {
# "messages": [
# {
# "role": "user",
# "content": "Analyze this python code:\nx = 1\nprint(x)"
# }
# ]
# }
If a prompt has no arguments, you can omit the
arguments
field or pass an empty dict:
result
=
await
client
.
call_tool
(
"
get_prompt
"
,
{
"
name
"
:
"
simple_prompt
"
}
)
​
Message Format
Rendered prompts return a messages array following the standard MCP format. Each message includes:
role
: The message role (“user” or “assistant”)
content
: The message text content
Multi-message prompts are supported - the array will contain all messages in order.
​
Binary Content
Unlike resources, prompts always return text content. There is no binary encoding needed.
Resources as Tools
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