joke_by_categoryGet a random Chuck Norris joke from a specific category. Returns joke text and ID.
| Name | Required | Description | Default |
|---|---|---|---|
| category | Yes | Category to fetch a joke from. Use list_categories to see valid values. |
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes | Unique joke identifier | |
| url | Yes | URL to the joke on chucknorris.io | |
| joke | Yes | The joke text | |
| categories | Yes | List of categories |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Output schema / (root)Previous value: -nullNew value: +{
+ "properties": {
+ "categories": {
+ "description": "List of categories",
+ "items": {
+ "type": "string"
+ },
+ "type": "array"
+ },
+ "id": {
+ "description": "Unique joke identifier",
+ "type": "string"
+ },
+ "joke": {
+ "description": "The joke text",
+ "type": "string"
+ },
+ "url": {
+ "description": "URL to the joke on chucknorris.io",
+ "type": "string"
+ }
+ },
+ "required": [
+ "id",
+ "joke",
+ "categories",
+ "url"
+ ],
+ "type": "object"
+}Input schema / examplesAdded value: +[
+ {
+ "category": "nerdy"
+ },
+ {
+ "category": "sport"
+ }
+]Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, and destructiveHint=false, which convey safety and non-mutating behavior. The description adds a small behavioral detail by stating the return includes joke text and ID. No contradictions with annotations, but it does not disclose error handling or other runtime behaviors beyond what annotations cover.
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 a single, concise sentence that front-loads the core purpose and return value. No wasted words, and it fits the simple nature of the tool.
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 the tool has only one parameter, 100% schema coverage, an output schema, and comprehensive annotations, the description fully covers the essential information. It states what the tool does and what it returns. Nothing critical is missing for an agent to select and invoke it correctly.
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?
Schema coverage is 100% with the parameter 'category' fully described as 'Category to fetch a joke from. Use list_categories to see valid values.' The description adds no extra parameter semantics beyond confirming it is a specific category, so the schema carries the burden adequately. Baseline 3 is appropriate.
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 gets a random Chuck Norris joke from a specific category and returns the joke text and ID. The verb ('Get'), resource ('random Chuck Norris joke'), and scope ('from a specific category') are all explicit, distinguishing it from sibling tools like random_joke or search_jokes.
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 when to use this tool (when a joke from a specific category is needed) and the schema parameter description explicitly instructs to use list_categories for valid values. However, it does not mention alternatives like random_joke for unfiltered jokes or search_jokes for searching, so it lacks explicit exclusions or named alternatives.
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.
Several tools have overlapping purposes: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded are near-identical query routers, and the five polymarket_* tools all hunt mispricings in subtly different ways. The memory trio (remember/recall/forget) and subscription trio (subscribe/unsubscribe/recent_alerts) are distinct, but the many data-query tools create frequent ambiguity for an agent deciding which one to call.
Naming is a mix of verb_noun (list_categories, resolve_entity, validate_claim), bare nouns (entity_profile, random_joke, deep_research), single verbs (forget, recall), and brand-prefixed nouns (pipeworx_feedback, pipeworx_trending). There's no consistent pattern across the set, so an agent cannot predict a tool's name from its function.
35 tools for a server named 'chucknorris' is an extreme mismatch; only 4 tools actually relate to Chuck Norris jokes. The rest form a sprawling collection of data-research, prediction-market, subscription, and memory utilities that have nothing to do with the stated server identity and overwhelm any agent expecting a simple joke API.
The Chuck Norris joke subset is complete (random, by-category, search, categories), but the overall server attempts many unrelated domains—structured data queries, prediction-market arb, entity profiles, subscriptions, memory—none of which are clearly scoped or fully coherent. The result is a grab-bag with no single domain that feels finished, and the incongruous inclusion of joke tools adds confusion rather than coverage.