agent-bridge-mcp
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| CODEX_CLI_NAME | No | 覆盖Codex CLI的命令名或绝对路径 | |
| FORGE_CLI_NAME | No | 覆盖Forge CLI的命令名或绝对路径 | |
| CLAUDE_CLI_NAME | No | 覆盖Claude CLI的命令名或绝对路径 | |
| GEMINI_CLI_NAME | No | 覆盖Gemini CLI的命令名或绝对路径 | |
| OPENCODE_CLI_NAME | No | 覆盖OpenCode CLI的命令名或绝对路径 |
Instructions
Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.
This server publishes no instructions, or was last inspected before Glama recorded them.
Capabilities
Features and capabilities supported by this server
Protocol revision2025-11-25
| Capability | Details |
|---|---|
| tools | {} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| runA | 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
|
| list_processesA | List all running and completed AI agent processes. Returns a simple list with PID, agent type, and status for each process. |
| get_resultA | Get the current output and status of an AI agent process by PID. Defaults to a compact result shape; set verbose to true for full metadata and detailed parsed output. |
| waitB | Wait for multiple AI agent processes to complete and return their results. Defaults to compact result items; set verbose to true for full metadata and detailed parsed output. |
| peekA | One-shot short observation window for running child agents. Returns only natural-language message events, and optionally normalized tool_call events, observed during this call; not a history API, not gapless streaming, and not stdout/stderr tailing. In v1, message extraction is supported for Codex, Claude, OpenCode, Gemini, and best-effort Forge Summary/Completed successfully lines. Forge tool calls are low-precision Execute/Finished markers and never include command output. Tool calls exclude raw tool output. |
| kill_processC | Terminate a running AI agent process by PID. |
| cleanup_processesA | Remove all completed and failed processes from the process list to free up memory. |
| doctorA | Check supported AI CLI binary availability and path resolution. Does not verify login state or terms acceptance. |
| modelsA | List supported model names, model aliases, and dynamic backend discovery hints. |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
No prompts | |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
No resources | |
TDQS
Scored across 9 tools
Each tool targets a distinct lifecycle action: run starts, list_processes enumerates, get_result retrieves output, kill_process terminates, peek observes running agents, wait blocks for completion, cleanup_processes removes old entries, doctor checks CLI availability, and models lists supported models. No overlap in purpose.
Naming is mixed: some tools use verb_noun with underscores (cleanup_processes, kill_process, list_processes, get_result) while others are single verbs or nouns (doctor, models, peek, run, wait). This inconsistency reduces predictability, though each name is still descriptive.
With 9 tools, the server covers the essential operations for managing AI agent processes—start, monitor, retrieve, wait, kill, cleanup, plus auxiliary checks for setup and model info. Neither too few nor too many.
The tool set provides a complete lifecycle: creation (run), monitoring (list_processes, peek, wait), retrieval (get_result), termination (kill_process), cleanup (cleanup_processes), setup verification (doctor), and configuration (models). No obvious gaps for process management.