list_stylesList all available avatar styles. Returns style names and descriptions—use with generate_avatar to pick a style.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
| Name | Required | Description | Default |
|---|---|---|---|
| count | Yes | The number of available avatar styles | |
| styles | Yes | List of available avatar style names |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Output schema / (root)Previous value: -nullNew value: +{
+ "properties": {
+ "count": {
+ "description": "The number of available avatar styles",
+ "type": "integer"
+ },
+ "styles": {
+ "description": "List of available avatar style names",
+ "items": {
+ "type": "string"
+ },
+ "type": "array"
+ }
+ },
+ "required": [
+ "count",
+ "styles"
+ ],
+ "type": "object"
+}Input schema / examplesAdded value: +[
+ {}
+]Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint, covering the safety profile. The description adds the return value content ('style names and descriptions'), which goes beyond annotations and clarifies the output.
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 function, and includes a practical usage note. Every clause earns its place with no redundancy.
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 zero-parameter list tool with rich annotations and an output schema, the description fully covers the tool's behavior, return content, and how it fits into the broader avatar generation workflow.
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?
There are zero parameters, so the baseline for parameter semantics is 4. The description doesn't need to explain parameters, and the schema confirms the tool takes no input.
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 verb ('List'), the resource ('all available avatar styles'), and the scope. It also explicitly connects the tool to generate_avatar, distinguishing its purpose from sibling tools.
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 phrase 'use with generate_avatar to pick a style' provides explicit contextual guidance on when to use this tool. It does not list exclusions or alternative tools explicitly, but the context is clear enough for a simple list operation.
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.
Tools have mostly clear distinctions: ask_pipeworx variants differ by grounding/evidence guarantees, and meta-tools (discover_tools, suggest_questions) serve onboarding. However, ask_pipeworx_beta currently matches ask_pipeworx exactly, creating transient ambiguity, and deep_research vs ask_pipeworx overlap in routing capability though with different scopes.
All tools use snake_case and most follow verb-first naming (ask_pipeworx, compare_entities, generate_avatar, subscribe). A few are descriptive nouns (recent_alerts, recent_changes, pipeworx_trending) but still readable and predictable. No mixed conventions; overall consistent style.
33 tools is excessive for a single server, and many are auxiliary (avatar generation, memory, feedback) that do not serve the core data-access purpose. The primary question-answering capability is centralized in a few routers, making many separate tools feel redundant or unrelated, which dilutes navigability.
The core domain of structured data access is well covered with routing, grounded answering, deep research, entity profiles, comparisons, and claim validation. Subscription lifecycle (subscribe/unsubscribe/alerts) and memory (remember/recall/forget) round out the surface. Minor gaps like lack of direct tool invocation outside the router are covered by discover_tools, and no critical dead ends exist for typical queries.