MCP Resources & Prompts

Let agents browse and read MCP server resources and prompt templates as tools

Some MCP servers ship files and prompt templates, not just tools. PraisonAI surfaces both as agent-callable tools automatically — no extra config.

Quick Start

1. Read a resource

   An agent connects to a resource server and reads a file — no extra setup.

   from praisonaiagents import Agent
   from praisonaiagents.mcp import MCP
   
   agent = Agent(
       name="Docs assistant",
       instructions="Answer questions using the docs available on the MCP server.",
       tools=MCP("uvx some-resource-server"),
   )
   
   agent.start("Show me what docs are available, then read the intro one.")
   

   list_mcp_resources and read_mcp_resource were auto-registered because the server advertises resources.

2. Run a prompt

   Servers that ship prompt templates expose list_mcp_prompts and get_mcp_prompt.

   from praisonaiagents import Agent
   from praisonaiagents.mcp import MCP
   
   agent = Agent(
       name="Prompt-pack agent",
       instructions="Use the server's prompt library to answer.",
       tools=MCP("uvx some-prompt-server"),
   )
   
   agent.start("List the available prompts, then run the 'summarise' one on this text: ...")
   
3. Namespace with a prefix (optional)

   Add a per-server prefix when combining multiple servers on one agent.

   from praisonaiagents import Agent
   from praisonaiagents.mcp import MCP
   
   mcp = MCP("uvx some-server")
   mcp.with_tool_prefix("docs")  # -> docs_list_mcp_resources, docs_read_mcp_resource, ...
   
   agent = Agent(name="Namespaced", instructions="...", tools=mcp)
   

---

How It Works

Resources and prompts are looked up during connect, then routed through synthetic tools the agent can call.

Synthetic tools are capability-gated — they only appear when the server advertises them.

Synthetic tool	Purpose	Registered when
list_mcp_resources	List concrete resources on the server	server exposes resources or resource templates
list_mcp_resource_templates	List URI templates the server can materialise	server exposes resources or resource templates
read_mcp_resource(uri)	Fetch a resource by URI (returns normalised text)	server exposes resources or resource templates
list_mcp_prompts	List prompt templates	server exposes prompts
get_mcp_prompt(name, arguments={})	Render a prompt with arguments	server exposes prompts

Resources are looked up during connect via list_resources(), list_resource_templates(), and list_prompts(). Each is a best-effort call — tools-only servers still initialise cleanly.

User interaction flow

A real run stitches the synthetic tools together automatically.

User: “What’s the tallest mountain?” Agent → list_mcp_resources() (finds docs://mountains.md) → read_mcp_resource("docs://mountains.md") → composes the final answer.

---

Configuration Options

No config class is required — behaviour is capability-gated automatically. Two knobs let you shape what the agent sees.

Option	Type	Default	Description
allowed_tools	List[str]	None	Include whitelist — only these tool names are kept (applies to synthetic tools too)
disabled_tools	List[str]	None	Exclude blacklist — these tool names are filtered out
with_tool_prefix("<name>")	method	none	Namespace tool names as <name>_<tool> to avoid collisions across servers

from praisonaiagents import Agent
from praisonaiagents.mcp import MCP

agent = Agent(
    name="Tight agent",
    instructions="Read docs but never browse prompt packs.",
    tools=MCP("uvx some-server", disabled_tools=["list_mcp_prompts"]),
)

Introspection API

Inspect capability without invoking any tool.

from praisonaiagents.mcp import MCP

mcp = MCP("uvx some-server")

mcp.get_resources()  # -> List[dict]
mcp.get_prompts()    # -> List[dict]

MCP.get_resources() returns resource and resource-template dicts:

# resource
{"uri": "docs://intro.md", "name": "Intro", "description": "...", "mimeType": "text/markdown"}
# resource template
{"uriTemplate": "docs://{slug}.md", "name": "Doc", "description": "...", "mimeType": "text/markdown"}

MCP.get_prompts() returns prompt dicts with argument hints:

{
    "name": "summarise",
    "description": "Summarise the given text",
    "arguments": [
        {"name": "text", "description": "Text to summarise", "required": True}
    ],
}

Payload guard

Content is normalised before it reaches the agent, capped by MAX_INLINE_BYTES = 100_000.

• Text over the limit is truncated with a ...[truncated, N chars total] marker.
• Binary payloads never inline — they become [binary resource: <mimeType>, <N> bytes].

---

Common Patterns

Combine tools + resources on one agent. A research server offering both a search tool and a corpus of resources feeds one agent — the tool searches, the resources get read.

from praisonaiagents import Agent
from praisonaiagents.mcp import MCP

agent = Agent(
    name="Researcher",
    instructions="Search, then read the most relevant resource.",
    tools=MCP("uvx research-server"),
)

Multiple MCP servers with prefixes. A docs server and a prompts server sit side by side, each namespaced to keep tool names distinct.

from praisonaiagents import Agent
from praisonaiagents.mcp import MCP

docs = MCP("uvx docs-server")
docs.with_tool_prefix("docs")

prompts = MCP("uvx prompt-server")
prompts.with_tool_prefix("prompts")

agent = Agent(name="Combined", instructions="...", tools=[docs, prompts])

Introspect before calling. Log or gate what the agent can see at wire-up time.

from praisonaiagents.mcp import MCP

mcp = MCP("uvx some-server")
for resource in mcp.get_resources():
    print(resource["uri"], resource["mimeType"])

---

Best Practices

Prefer smaller resources

Servers that expose very large files hand agents truncated content once past MAX_INLINE_BYTES = 100_000. Split resources or add a search tool so the agent fetches only what it needs.

Use with_tool_prefix when combining servers

Two servers advertising read_mcp_resource otherwise show up with the same name and confuse the model. Prefix each server so names stay distinct.

Filter with disabled_tools for tighter agents

If an agent shouldn’t browse prompt packs, drop list_mcp_prompts explicitly via disabled_tools. Synthetic tools go through the same filters as server tools.

A server tool of the same name always wins

If the server registers a real read_mcp_resource, the synthetic version is skipped by design so the callable and its schema stay consistent. Don’t rely on the synthetic tool when the server exposes its own.

---

Related

MCP Tools

MCP module basics and tool usage

MCP Transports

How PraisonAI connects to MCP servers