agent-bridge-mcp

agent_send

Submits a coding instruction to Claude Code, Codex, or Antigravity for asynchronous background execution, returning a task ID for tracking.

Instructions

Send an instruction to Claude Code or Codex CLI/SDK. Executes asynchronously in background and returns task_id.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "agent": "<agent>",
      "prompt": "<prompt>"
    },
    "name": "agent_send"
  }
}

Input Schema

Name	Required	Description
`cwd`	No	Working directory for execution (default: current directory)
`agent`	Yes	Target agent: 'claude', 'codex', or 'antigravity' (agy CLI)
`model`	No	Specific model name (e.g. 'sonnet', 'opus' for Claude; 'o3' for Codex)
`topic`	No	Topic or feature name (e.g. 'presence', 'auth')
`engine`	No	Execution engine: 'cli' (default) or 'sdk'
`prompt`	Yes	Instruction or prompt text for the agent
`project`	No	Directory name of the project inside the workspace
`taskType`	No	Role or nature of task
`sessionId`	No	Specific internal session ID to resume
`timeoutMs`	No	Execution timeout in milliseconds (default: 180000ms / 3min)
`forceNewSession`	No	Set true to force create a new session instead of reusing

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.2.0Aug 19, 2026

TDQS

B3.4/5.0

Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden. It states that execution is asynchronous and returns a task_id, which is useful. However, it does not disclose what happens on failure, whether the task can be cancelled, rate limits, or authentication needs. The description is adequate but not comprehensive for a tool with 11 parameters.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single sentence that is concise and directly states the purpose and key behavior. It is front-loaded and efficient. However, it could include a brief example or usage note without becoming too long.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the complexity (11 parameters, 2 required, no output schema), the description is somewhat minimal. It does not explain return values (though no output schema exists), error handling, or how to poll for results (e.g., using agent_status). The description is functional but leaves gaps for an agent to fill from context.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the input schema already documents all parameters. The description does not add any additional meaning beyond what the schema provides. Baseline 3 is appropriate; the description adds no value for parameter understanding.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the action ('Send an instruction'), the target ('Claude Code or Codex CLI/SDK'), and the key behavior ('asynchronously in background'). It returns a task_id, which distinguishes it from synchronous tools. However, it could be slightly more specific about the 'antigravity' agent and the sibling tools, but overall it's clear and informative.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies this is for sending instructions to agents, but it does not provide guidance on when to use this tool versus alternatives like 'agent_review_loop' or 'agent_evidence_check'. No explicit when-not-to-use or alternative suggestions are given, leaving the agent to infer from sibling names.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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