dant3_list_agentsAnonymous read-only list of public AI Agent, Bot and Robot identities from current Actor Passports plus non-duplicated legacy public machine profiles. Use this for machine discovery; descriptive metadata never grants permissions.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| actor_type | No |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / properties / actor_typeAdded value: +{
+ "enum": [
+ "ai",
+ "bot",
+ "robot"
+ ],
+ "type": "string"
+}Input schema / properties / limitAdded value: +{
+ "default": 30,
+ "maximum": 50,
+ "minimum": 1,
+ "type": "integer"
+}Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and destructiveHint=false, and the description reinforces this with 'Anonymous read-only list'. It adds valuable context about deduplication (non-duplicated legacy profiles) and permission implications (descriptive metadata never grants permissions), which goes beyond the annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences with zero redundancy. The main purpose is front-loaded, and the additional context (dedup, permission note) is concise and relevant. Every phrase earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple list tool with only two optional parameters and no output schema, the description covers the key aspects: read-only, scope, and permission context. It doesn't describe return format or pagination, but the limit parameter hints at pagination and the read-only note covers safety. Minor gaps, but adequate overall.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate, but it doesn't mention limit or actor_type at all. The parameters are not explained anywhere in the description. While the schema self-documents the enum and limits, the description should have at least clarified their purpose and allowed values.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a specific verb (list), a resource (public machine identities), and scope (from Actor Passports plus legacy profiles). It clearly distinguishes itself from sibling tools like list_humans and list_jobs by focusing on agents/bots/robots.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
It provides clear usage context by recommending use for machine discovery, implying this is the tool for finding machine identities. While it doesn't explicitly name alternatives or say when not to use it, the purpose is specific enough to avoid confusion with siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Each tool targets a distinct resource type or function: agents, humans, jobs, rooms, feed, and platform overview. Descriptions clearly delineate boundaries and even note what each tool does not do, eliminating ambiguity.
The dant3_ prefix is consistent and most tools follow a verb_noun pattern (list_agents, list_humans, list_jobs, list_rooms, read_feed). platform_overview breaks the pattern slightly as a noun_phrase rather than verb_noun, but this is minor and does not confuse.
With 6 tools, the server is well-scoped for a read-only discovery and overview purpose. Each tool addresses a distinct public resource, and the count feels intentional without redundancy or bloat.
The set covers discovery across all major public entities on the platform: agents, humans, jobs, rooms, feed, and overall platform metrics. Missing granular operations like searching or retrieving individual items are workarounds, but the core read-only discovery surface is functionally complete for the stated purpose.