random_adviceGet a random piece of advice. Returns the advice text and slip ID for reference or follow-up queries.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes | Numeric ID of the advice slip | |
| advice | Yes | The advice text |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Output schema / (root)Previous value: -nullNew value: +{
+ "properties": {
+ "advice": {
+ "description": "The advice text",
+ "type": "string"
+ },
+ "id": {
+ "description": "Numeric ID of the advice slip",
+ "type": "number"
+ }
+ },
+ "required": [
+ "id",
+ "advice"
+ ],
+ "type": "object"
+}Input schema / examplesAdded value: +[
+ {}
+]Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnly and non-destructive hints. The description adds the 'random' behavior (not captured in annotations) and explains the purpose of the slip ID for follow-ups, which goes beyond structured data.
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?
Single sentence, front-loaded with the action, no redundant information. Every clause adds value: the random nature, what is returned, and the purpose of the ID.
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?
Given no parameters and a simple output (advice text + slip ID), the description fully explains the tool's behavior and result. Annotations cover safety, and the output schema exists, so no further details are needed.
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 description needs no parameter explanations. Schema coverage is vacuously 100%. The baseline for 0 params is 4, and the description appropriately focuses on output rather than inputs.
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 uses a specific verb ('Get') and resource ('random piece of advice'), clearly distinguishing it from siblings like get_advice and search_advice. It also states the return value (advice text and slip ID), reinforcing purpose.
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 word 'random' and the reference to 'follow-up queries' imply the intended use case (e.g., discovery or inspiration) and how to use the slip ID later. It does not explicitly mention alternatives or exclusions, but the context is clear enough for a simple tool.
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.
The ask_pipeworx family is a major confusion source: ask_pipeworx_beta explicitly states it 'currently matches ask_pipeworx exactly', and ask_pipeworx_grounded/deep_research heavily overlap with the base router. polymarket_edges vs polymarket_arbitrage and discover_tools vs suggest_questions also have fuzzy boundaries, though long descriptions partially mitigate the overlap.
All tool names are lowercase snake_case, and most follow a verb_noun pattern (validate_claim, resolve_entity, compare_entities, generate_llms_txt). A few bare verbs (remember, recall, forget) and noun-style names (entity_profile, polymarket_arbitrage, pipeworx_trending) deviate slightly, but the overall style is predictable and readable.
34 tools is heavy and spans several unrelated domains: Pipeworx data research, prediction markets, subscriptions, memory, AI visibility, advice slips, npm dependency checks, and llms.txt generation. The count is inflated by near-duplicate research routers and disconnected outliers like generate_llms_txt and scan_dependency, making the set feel like a kitchen sink rather than a focused server.
The Pipeworx research and prediction-market surfaces are quite complete (ask, grounded, deep research, entity profile, compare, resolve, validate, subscriptions with full lifecycle, memory with save/recall/delete). However, the server is named 'advice' yet the advice domain only has three thin tools (get/search/random) with no other operations, and the mixed domains leave obvious dead ends for any single stated purpose.