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Glama

mesh_rooms

List the rooms this agent has opened, joined, or can join, with participants, fact counts, pending rings, and dropped-event warnings from an instant local read.

Instructions

Rooms this agent is in (opened or joined this session, still being watched), with the participants seen so far and how many facts arrived, plus public rooms announced on central that you have not joined, plus rings you sent that are still awaiting the callee's model. Instant, a local read, never blocks. Each room carries dropped, and central_dropped is central's. dropped: events on this topic that reached a listener on this machine and were discarded before being recorded (a subscription's inbox holds 256 events and discards the newest while its reader is behind), summed over every listener sharing the transcript since macula-mcp 0.35.0; 0 means none were discarded.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.28.7

TDQS

A3.5/5.0
Behavior4/5

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

With no annotations, the description carries the full burden and does so well: it declares the operation is instant, local, and non-blocking, and it explains the `dropped`/`central_dropped` counters in depth (256-event inbox, newest discarded, summed since 0.35.0). It does not cover any auth or freshness caveats beyond the counter semantics, keeping it short of a 5.

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

Conciseness3/5

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

The opening sentence is a long run-on that bundles three separate result categories together, and the trailing `dropped` explanation is verbose. The material is useful and front-loaded, but it would read far better as a short list of what is returned followed by the counter note.

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?

There is no output schema, so the description must describe return contents itself, and it does: participants seen, fact counts, per-room `dropped`, and central's `central_dropped`. Combined with the zero-parameter schema, an agent has what it needs, though it lacks any hint of ordering, sizing, or limits on the returned list.

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 takes zero parameters, which is the baseline-4 case; the schema has nothing to document and the description correctly spends no words on inputs, devoting its space to return semantics instead.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description names the resource precisely and enumerates three distinct things it returns: rooms this agent is in, unjoined public rooms announced on central, and rings awaiting a callee's model. That is enough for an agent to distinguish it from mesh_open_room/mesh_join_room, but no explicit verb anchors it and no sibling is named.

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

Usage Guidelines2/5

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

There is no statement of when to call this versus alternatives such as mesh_lobby_transcript, mesh_agents, or mesh_read_inbox. 'Instant, a local read, never blocks' describes behavior rather than selection criteria, so the agent is left to infer the triggering context.

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