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list_agents

Discover in-process agent IDs, status, spend, budget, and capabilities. Use this read-only snapshot to find agent_id values before starting tasks.

Instructions

List in-process agent records (id, status, spend, budget, capabilities).

Use before run_task to discover agent_id values. Read-only snapshot of this process. Does not create agents (create_agent) or start work (run_task).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Changed5 schema fields changed
    • addedInput schema / additionalProperties
      Added value: +false
    • removedInput schema / title
      Removed value: -"list_agentsArguments"
    • removedOutput schema / properties / result / title
      Removed value: -"Result"
    • removedOutput schema / title
      Removed value: -"list_agentsOutput"
    • addedOutput schema / x-fastmcp-wrap-result
      Added value: +true
  2. First observedv0.1.0

TDQS

A4.7/5.0
Behavior4/5

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

With no annotations provided, the description carries the full burden and does well by declaring 'Read-only snapshot of this process' and 'Does not create agents or start work.' This gives an agent important safety and side-effect information beyond what structured metadata would provide, though it could also mention potential limitations like pagination or empty results.

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

Conciseness5/5

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

Two tight sentences, front-loaded with the core action and scope. The usage guidance and exclusions are packed in without any redundancy, and every sentence earns its place.

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

Completeness5/5

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

The description is complete for a zero-parameter, read-only list tool. It explains what it lists, why an agent would use it, the operational scope, and the key exclusions. Since an output schema exists, the description does not need to enumerate return structure.

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?

The tool has zero parameters, so there is nothing for the description to clarify. The listed fields (id, status, spend, budget, capabilities) are return values rather than inputs, but still give useful context that complements the output 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 uses a specific verb ('List') and names the exact resource ('in-process agent records') with expected fields. It also distinguishes itself from sibling tools by explicitly stating it does not create agents or start work, so an agent can clearly separate this from create_agent and run_task.

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?

Provides explicit when-to-use guidance: 'Use before run_task to discover agent_id values.' It also states what the tool does not do, naming the relevant alternatives (create_agent, run_task), which is strong routing information.

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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