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lailai258

agent-bridge-mcp

by lailai258
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TypeScript
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run

Starts AI agents as background processes for file ops, code analysis, git workflows, web search, and more. Returns a PID to monitor progress.

Instructions

AI Agent Runner: Starts a Claude, Codex, Gemini, Forge, or OpenCode CLI process in the background and returns a PID immediately. Use list_processes and get_result to monitor progress.

• File ops: Create, read, (fuzzy) edit, move, copy, delete, list files, analyze/ocr images, file content analysis • Code: Generate / analyse / refactor / fix • Git: Stage ▸ commit ▸ push ▸ tag (any workflow) • Terminal: Run any CLI cmd or open URLs • Web search + summarise content on-the-fly • Multi-step workflows & GitHub integration

IMPORTANT: This tool now returns immediately with a PID. Use other tools to check status and get results.

Supported models: "claude-ultra", "codex-ultra", "gemini-ultra", "sonnet", "sonnet[1m]", "opus", "opusplan", "haiku", "gpt-5.4", "gpt-5.5", "gpt-5.4-mini", "gpt-5.3-codex", "gpt-5.3-codex-spark", "gpt-5.2", "gemini-2.5-pro", "gemini-2.5-flash", "gemini-3.1-pro-preview", "gemini-3-pro-preview", "gemini-3-flash-preview", "forge", "opencode", "oc-<provider/model>"

Prompt input: You must provide EITHER prompt (string) OR prompt_file (file path), but not both.

Prompt tips

  1. Be concise, explicit & step-by-step for complex tasks.

  2. Check process status with list_processes

  3. Get results with get_result using the returned PID

  4. Kill long-running processes with kill_process if needed

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
promptNoThe detailed natural language prompt for the agent to execute. Either this or prompt_file is required.
prompt_fileNoPath to a file containing the prompt. Either this or prompt is required. Must be an absolute path or relative to workFolder.
workFolderYesThe working directory for the agent execution. Must be an absolute path.
modelNoThe model to use. Aliases: "claude-ultra" (auto max effort), "codex-ultra" (auto xhigh reasoning), "gemini-ultra". Standard: "sonnet", "sonnet[1m]", "opus", "opusplan", "haiku", "gpt-5.4", "gpt-5.5", "gpt-5.4-mini", "gpt-5.3-codex", "gpt-5.3-codex-spark", "gpt-5.2", "gemini-2.5-pro", "gemini-2.5-flash", "gemini-3.1-pro-preview", "gemini-3-pro-preview", "gemini-3-flash-preview", "forge", "opencode". OpenCode also accepts explicit dynamic models using "oc-<provider/model>". "forge" is a provider key, not a Forge model family selector.
reasoning_effortNoReasoning control for Claude and Codex. Claude uses --effort with "low", "medium", "high", "xhigh", "max". Codex uses model_reasoning_effort with "low", "medium", "high", "xhigh". Gemini, Forge, and OpenCode do not support reasoning_effort in this integration.
session_idNoOptional session ID to resume a previous session. Supported for Claude, Codex, Gemini, Forge, and OpenCode. OpenCode resumes in-place via --session and may also be combined with explicit oc-<provider/model> selection.

Tool Definition Quality

A4.5/5.0
Behavior4/5

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

Despite no annotations, the description discloses that the tool returns immediately with a PID and requires other tools for results. It lists supported models, prompt requirements, and tips. Could be more specific about error states or lifecycle, but adequate.

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?

Well-structured with clear sections (purpose, capabilities, behavior note, models, input requirement, tips). Some redundancy in model list (duplicated in schema and description) and bullet points make it longer than necessary, but front-loaded with key info.

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

Completeness4/5

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

No output schema exists, but description explains return value (PID) and usage workflow. Covers capabilities (file ops, code, git, terminal, web search). Sufficiently complete for a process runner tool.

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

Parameters4/5

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

Schema descriptions cover 100% of parameters. The description adds mutual exclusivity of prompt and prompt_file, model alias meanings, and prompt tips. This adds value beyond schema.

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

Purpose5/5

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

The description clearly states it starts an AI agent CLI process in the background and returns a PID immediately. It lists supported models and distinguishes from sibling tools like list_processes, get_result, and kill_process.

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

Usage Guidelines5/5

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

Explicitly instructs to use list_processes and get_result for monitoring, and provides prompt tips for complex tasks, status checking, retrieving results, and killing processes. Clearly contrasts with sibling tools.

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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