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

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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)

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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 toolPurposeRegistered when
list_mcp_resourcesList concrete resources on the serverserver exposes resources or resource templates
list_mcp_resource_templatesList URI templates the server can materialiseserver 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_promptsList prompt templatesserver exposes prompts
get_mcp_prompt(name, arguments={})Render a prompt with argumentsserver 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.

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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.

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Configuration Options

No config class is required — behaviour is capability-gated automatically. Two knobs let you shape what the agent sees.
OptionTypeDefaultDescription
allowed_toolsList[str]NoneInclude whitelist — only these tool names are kept (applies to synthetic tools too)
disabled_toolsList[str]NoneExclude blacklist — these tool names are filtered out
with_tool_prefix("<name>")methodnoneNamespace 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"]),
)

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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}
    ],
}

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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].

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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"])

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Best Practices

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

MCP Tools

MCP module basics and tool usage

MCP Transports

How PraisonAI connects to MCP servers