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nolag_create_agent

Create an AI agent actor with persistent session support. Agents get stable MQTT sessions that survive disconnects, so queued messages are delivered on reconnect. The access token is only shown once - save it immediately!

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYesAgent name (e.g., 'support-bot', 'data-processor', 'content-analyzer')
externalIdNoExternal ID for linking to your system
descriptionNoDescription of what this agent does
capabilitiesNoList of capability tags (e.g., ['nlp', 'code-review', 'summarization'])

TDQS

A4/5.0
Behavior4/5

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

No annotations are available, so the description carries the full burden of disclosure. It reveals two important behavioral traits: sessions survive disconnects and queued messages are delivered on reconnect, plus the access token is only shown once. These are non-obvious and critical for safe usage, though it doesn't mention permissions or reversibility.

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 two sentences: the first states the core purpose, the second provides a critical operational warning. It is front-loaded, free of fluff, and every word contributes value.

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 partially compensates by highlighting the one-time access token. The parameters are fully documented in the schema, and the description covers the most important runtime behavior. It lacks a full return-value description but is reasonably complete for a create operation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema provides 100% coverage with detailed descriptions for all 4 parameters. The tool description adds no extra parameter-specific meaning beyond what the schema already states, so the baseline score of 3 applies.

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 begins with a specific action: 'Create an AI agent actor', clearly identifying the verb and resource. It further differentiates from siblings like nolag_create_actor by specifying 'persistent session support', making the tool's unique purpose unmistakable.

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

Usage Guidelines3/5

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

The description implies when to use the tool (when an AI agent with persistent MQTT sessions is needed) but does not explicitly contrast it with alternatives like nolag_create_actor or state when not to use it. No clear exclusion or alternative guidance is provided, so usage context is inferred rather than explicit.

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

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TDQS

B3.3/5.0
Disambiguation4/5

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.

Naming Consistency5/5

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.

Tool Count2/5

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.

Completeness4/5

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.

Resources