Read the current blackboard (shared state) for a room. Returns the most recent retained state messages, representing the current key-value state visible to all agents.
| Name | Required | Description | Default |
|---|---|---|---|
| key | No | Optional: get a specific state key. Omit to get all state entries. | |
| appId | Yes | App ID | |
| roomId | Yes | Room ID |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the burden of behavioral disclosure. It clearly indicates a read operation and explains the return value as retained state messages. However, it does not mention permissions, error conditions, or what happens when the state is empty, leaving some gaps.
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 two sentences, front-loaded with the primary action, and contains zero filler. Every word adds meaning, efficiently conveying the tool's purpose and return semantics.
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 read tool with no output schema, the description adequately explains what it does and what it returns. It provides enough context for an agent to use it correctly, though it could elaborate on the structure of the returned state messages or edge cases.
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 input schema covers all parameters with descriptions (100% coverage), so the description adds limited value. It does reinforce that 'key' is optional and 'room' is the scope, but the schema already provides this information.
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 the tool's purpose with a specific verb ('Read') and resource ('current blackboard shared state for a room'). It distinguishes itself from siblings by focusing on the blackboard concept and its key-value state visibility to all agents.
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 the tool is for reading current shared state but does not provide explicit guidance on when to use this tool versus alternatives like nolag_get_messages or nolag_publish. No exclusions or alternative tools are mentioned.
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.
Most tools follow a clear resource-action pattern, but create_actor, create_agent, and create_orchestrator could be confused, as agents and orchestrators are specialized actors. Similarly, publish and dispatch_task both send messages to topics, though descriptions clarify. Overall, boundaries are mostly distinct.
All tools use the nolag_ prefix with consistent verb_noun snake_case naming. Verbs include create, delete, get, list, update, set, retry, dispatch, publish, generate, and configure. No mixed conventions are present.
At 35 tools, this server exceeds the recommended range for a typical MCP server. While each tool serves a purpose, many CRUD operations could be consolidated (e.g., create_actor, create_agent, and create_orchestrator could be one tool with a type parameter). The count feels heavy.
The toolset covers core lifecycle operations for apps, actors, rooms, scopes, and webhooks, plus messaging, task dispatch, blackboard state, and agent event streaming. Minor gaps exist (no explicit agent-specific update, no webhook delete), but agents can work around these with general actor/app operations.
