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Rumblingb

agent-messaging

by Rumblingb

AgentMessaging MCP Server

Async messaging protocol for AI agents. Send messages, proposals, and manage threaded conversations between agents using the Model Context Protocol (MCP).

Pricing

Related MCP server: MCP Agentic Framework

Tools

1. msg_send

Send a message to another agent.

Parameters:

Name

Type

Required

Description

to_agent_id

string

yes

Target agent ID

subject

string

yes

Message subject line

body

string

yes

Message body content

priority

string

no

low, normal (default), high, or urgent

reply_to

string

no

Message ID this is a reply to (for threading)

Returns: message_id, timestamp, delivery_status

2. msg_inbox

Get messages for an agent.

Parameters:

Name

Type

Required

Description

agent_id

string

yes

Agent ID to fetch inbox for

status_filter

string

no

Filter: unread, read, or archived

max_results

integer

no

Maximum number of messages to return

Returns: Array of message objects

3. msg_read

Read full message content. Automatically marks the message as read.

Parameters:

Name

Type

Required

Description

message_id

string

yes

ID of the message to read

Returns: Full message object with status updated to read

4. msg_reply

Reply to a message. Creates a threaded conversation.

Parameters:

Name

Type

Required

Description

message_id

string

yes

Message ID to reply to

body

string

yes

Reply body content

Returns: message_id, timestamp, reply_to

5. msg_thread

Get the full message thread (original + all replies, recursively).

Parameters:

Name

Type

Required

Description

message_id

string

yes

ID of any message in the thread

Returns: Array of messages in thread order (root first)

Search messages by content (case-insensitive). Searches subject, body, and message_id.

Parameters:

Name

Type

Required

Description

agent_id

string

yes

Agent ID whose messages to search

query

string

yes

Search query

Returns: Array of matching message objects

7. msg_send_proposal

Send a structured work proposal to another agent.

Parameters:

Name

Type

Required

Description

to_agent_id

string

yes

Target agent ID

task_description

string

yes

Description of the proposed task

budget

number

yes

Budget for the task

deadline

string

yes

Deadline (ISO date or freeform text)

Returns: message_id, timestamp, delivery_status, proposal_status

8. msg_respond_proposal

Accept, reject, or counter a proposal.

Parameters:

Name

Type

Required

Description

message_id

string

yes

Proposal message ID

accept

boolean

no

Accept the proposal (default: true). Set false to reject or counter

counter_offer

object

no

Counter-offer details, e.g. {"budget": 150, "deadline": "2026-06-01"}

Returns: message_id, proposal_status, timestamp

Storage

All messages are stored locally in ~/.agentmessages/ organized by agent ID:

~/.agentmessages/
├── agent-alpha/
│   ├── msg_1a2b3c4d5e6f.json
│   └── msg_9z8y7x6w5v4u.json
├── agent-beta/
│   └── msg_3d4e5f6g7h8i.json
└── _archive/
    └── (legacy flat-file messages)

Each message is a JSON file containing the full message object with metadata.

Installation

pip install -r requirements.txt

Usage

Run the server with any MCP host (e.g., Claude Desktop, Cline, Continue):

{
  "mcpServers": {
    "agent-messaging": {
      "command": "python",
      "args": ["/path/to/agent-messaging-mcp/server.py"]
    }
  }
}

Or run directly:

cd /mnt/d/Projects/pickaxes/agent-messaging-mcp
python server.py

The server communicates over stdio using the MCP protocol.

Example

# Send a message
msg_send(
    to_agent_id="worker-42",
    subject="Need help with data analysis",
    body="Can you analyze the Q2 sales data?",
    priority="high"
)
# Returns: {"message_id": "msg_a1b2c3d4e5f6", "timestamp": "2026-05-11T06:16:00Z", "delivery_status": "sent"}

# Send a proposal
msg_send_proposal(
    to_agent_id="worker-42",
    task_description="Analyze Q2 sales dataset and produce a summary report",
    budget=500.0,
    deadline="2026-05-18"
)
# Returns: {"message_id": "msg_xyz789", ...}

License

Proprietary — see pricing above.

A
license - permissive license
-
quality - not tested
F
maintenance

Maintenance

Maintainers
Response time
Release cycle
Releases (12mo)
Commit activity

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