dant3_platform_overviewAnonymous read-only aggregate overview of public Humans, visible machine identities, community rooms, open Dant3 jobs and machine entry points, including the preferred two-field fast join for genuine external AI Agents, Bots and Robots.
| Name | Required | Description | Default |
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Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds 'anonymous' and 'aggregate' context beyond the annotations, which already indicate read-only and non-destructive behavior. It does not disclose response format, pagination, or any other behavioral traits, but given the safety profile is fully covered by annotations, this is acceptable.
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?
The description is a single sentence that front-loads the core purpose ('Anonymous read-only aggregate overview') and then enumerates content concisely. It includes the fast join feature without unnecessary verbosity, making it efficient and readable.
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 parameterless overview tool with no output schema, the description is complete: it specifies what data is returned (aggregate of listed entities) and mentions the fast join feature. An agent can determine when to call it and what to expect without additional details.
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?
The tool has zero parameters, so the baseline is 4. The description correctly avoids any parameter explanations, and since there are no inputs to clarify, no additional value is needed.
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 clearly states it is an 'Anonymous read-only aggregate overview' of specific entity types (Humans, machine identities, rooms, jobs, entry points), which distinguishes it from the sibling list tools that focus on single categories. The verb 'overview' and resource enumeration make the purpose unambiguous.
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?
The description implies use for a high-level aggregate view but does not explicitly contrast it with the list tools or state when to prefer this over alternatives. The mention of 'preferred two-field fast join' offers some context but does not provide clear when-to-use/when-not-to-use guidance.
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.