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agent-shuttle-mcp

by plartex
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

get_agent_info

Inspect live models, account quota and enforceable tool policies for Antigravity, Codex, Claude Code, OpenCode or a configured ACP agent before delegating research, review, implementation or a second opinion.

Instructions

Inspect live models, account quota and enforceable tool policies for Antigravity, Codex, Claude Code, OpenCode or a configured ACP agent before delegating research, review, implementation or a second opinion. Manages the local server automatically. This checks live metadata without a model turn; use list_agents for offline inventory.

Input Schema

NameRequiredDescriptionDefault
agent_idYesTarget: codex, antigravity, opencode, claude_code, or a configured agent_id from list_agents.
workspaceNoExisting absolute directory on the local host for the delegated task; otherwise use the server's configured workspace.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changedv0.8.2
    • addedInput schema / properties / agent_id / description
      Added value: +"Target: codex, antigravity, opencode, claude_code, or a configured agent_id from list_agents."
    • addedInput schema / properties / workspace
      Added value: +{
+  "anyOf": [
+    {
+      "type": "string"
+    },
+    {
+      "type": "null"
+    }
+  ],
+  "default": null,
+  "description": "Existing absolute directory on the local host for the delegated task; otherwise use the server's configured workspace.",
+  "title": "Workspace"
+}
  2. First observedv0.5.0

TDQS

A4.3/5.0
Behavior4/5

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

With no annotations, the description carries the burden and does well: it discloses that the local server is managed automatically (a side effect), that no model turn is consumed, and that the data is live rather than cached. It stops short of documenting permissions, failure modes, or the shape of the returned metadata.

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?

It is four short sentences with the primary purpose and the delegation use case front-loaded, and nothing is wasted. The hard line breaks and missing terminal structure make it slightly harder to scan than ideal, but there is no filler.

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?

For a read-only inspection tool with a fully documented two-parameter schema, the description covers purpose, timing, cost, and the sibling alternative. Without an output schema, a brief word on what the returned metadata looks like would close the last gap.

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 both agent_id and workspace are already documented with their accepted values and fallback semantics. The description only restates the provider list already present in the schema and adds no new meaning, so the baseline 3 applies.

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 uses a specific verb ('Inspect') and enumerates the resources inspected: live models, account quota, and enforceable tool policies, scoped to named providers. It explicitly contrasts with list_agents ('offline inventory'), so an agent can distinguish it from siblings without opening the schema.

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?

It names the triggering context ('before delegating research, review, implementation or a second opinion') and the exclusion plus alternative ('use list_agents for offline inventory'). It also clarifies the cost model ('without a model turn'), which is decision-relevant guidance.

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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