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Glama

list_agents

List available agents, backends, workflows, and write-allowlist paths to choose a ready execution target before dispatching work. Shows local runtime status and refreshes secrets from environment files.

Instructions

List specialist agents, backends (Cursor + external), workflows, write-allowlist directories, default cwd, and local-runtime status (late-infer running vs idle, Cursor cloud / CURSOR_API_KEY). Call this before dispatching work so you pick a ready backend. Re-reads .env and GUI secrets on each call so newly added keys take effect without restarting Cursor. Local Cursor agents may only write inside allowed directories. Cloud Cursor agents cannot reach localhost late-infer; use local-and-cloud or cloud-with-local-draft workflows.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.7/5.0
Behavior5/5

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

Even though no annotations are present, the description discloses that the tool re-reads .env and GUI secrets on each call, so newly added keys take effect without restarting Cursor. It also surfaces important operational constraints: local Cursor agents only write inside allowed directories and cloud Cursor agents cannot reach localhost late-infer. This goes well beyond a bare 'list' description.

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?

The description is dense but front-loaded with the resource list, then the when-to-use directive, then behavior and constraints. No sentence is filler; the parentheticals add useful specificity rather than wordiness. It remains readable despite its length.

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?

With no output schema and no annotations, the description must carry the entire load, and it covers purpose, usage timing, internal behavior, and access limitations. It even tells the agent how to react to the cloud/localhost limitation. Nothing essential for tool selection and invocation is missing.

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?

There are zero parameters, so the schema carries no semantic burden. The description compensates by specifying exactly what categories of information are returned. Baseline 4 applies and it fully satisfies it.

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 opens with the verb 'List' and names an explicit set of resources: specialist agents, backends, workflows, write-allowlist directories, default cwd, and local-runtime status. This clearly distinguishes it from sibling listing tools like list_allowed_dirs and list_runs. It is unambiguous about what the tool does.

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

Usage Guidelines4/5

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

It explicitly instructs 'Call this before dispatching work so you pick a ready backend', giving a clear trigger condition. It also provides routing guidance for cloud Cursor agents by noting to use local-and-cloud or cloud-with-local-draft workflows. It does not name alternatives explicitly, but the context makes the intended use clear.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.