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by io.github.zhangduochuan
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Read local coding agent status

agent_status
Read-onlyIdempotent

Reads the live status of an existing local coding Agent. Reuse the agent_id returned by agent_start or agent_create_peer; for a Peer, pass the same workspace_id and AI Session/workflow binding. The server routes a device-bound agent_id back to its owning computer. Select this tool from the user's intent when it is the best match. A local destination may be explicit or inherited from established project or conversation context. The user does not need to mention ailinkany.com, MCP, a connector, a plugin, or this tool name.

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_id": "<agent_id>"
    },
    "name": "agent_status"
  }
}

Input Schema

NameRequiredDescriptionDefault
agent_idYesPersistent agent_id returned by agent_start.
device_idNoTarget computer device_id. Omit when only one computer is paired.
device_nameNoComputer name or ailinkany.com remark (e.g. home PC). Prefer this when the user names a computer.
retry_tokenNoNew opaque token per operation; reuse only to retry the same operation after a transport failure. No user content.
workflow_idNoOptional workflow id; pass together with ai_session_id.
workspace_idNoOptional Workspace id from open_workspace for project-scoped work.
ai_session_idNoOptional Session id. Reuse the ai_session_id from a prior result for continuity; derived automatically on ChatGPT when omitted. Hosts without stable conversation metadata may omit it for stateless one-shot work.

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.7/5.0
Behavior4/5

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

Annotations already state readOnly, idempotent and non-destructive, so safety is covered. The description adds real behavioral context beyond them: the server routes a device-bound agent_id back to its owning computer, and local destinations may be inherited from project/conversation context. It does not describe failure behavior for a stale or unknown agent_id, which is the main remaining gap.

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

Conciseness3/5

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

Purpose and the agent_id/peer binding are correctly front-loaded, but the trailing sentences about selecting on user intent and not needing to mention ailinkany.com, MCP, connectors or plugins are boilerplate that does not help the model call the tool. Removing them would lose no invocation-relevant information.

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?

With 7 parameters, one required, full schema descriptions and an output schema (so return values need no explanation), the description covers what an agent needs to locate the right target agent and bind Peer sessions. It is only slightly incomplete on routing edge cases such as a missing device when multiple computers are paired.

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 coverage is 100%, so the baseline is 3. The description earns a bump by stating cross-parameter relationships the schema lists only per-field: that workspace_id, ai_session_id and workflow_id must be carried over together for a Peer agent. The rest of the parameter guidance largely duplicates the schema.

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 first sentence pairs a specific verb with a specific resource: 'Reads the live status of an existing local coding Agent.' An agent can tell this is a status/state read, not a result fetch or a list. However, it never distinguishes itself from close siblings such as agent_result, agent_list, agent_history, or get_ai_session_status, so it falls short of a 5.

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?

Concrete prerequisites are given: reuse the agent_id from agent_start/agent_create_peer, and for a Peer pass the same workspace_id plus the AI Session/workflow binding. Generic filler ('Select this tool from the user's intent when it is the best match') adds no routing signal, and no alternative tool is named for when this is the wrong choice (e.g. final output vs. live status).

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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