random_jokeFetch one random dad joke from icanhazdadjoke.com. Returns joke text and alphanumeric ID.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes | Unique identifier for the joke | |
| joke | Yes | The joke text |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / examplesAdded value: +[
+ {}
+]Output schema / (root)Previous value: -nullNew value: +{
+ "properties": {
+ "id": {
+ "description": "Unique identifier for the joke",
+ "type": "string"
+ },
+ "joke": {
+ "description": "The joke text",
+ "type": "string"
+ }
+ },
+ "required": [
+ "id",
+ "joke"
+ ],
+ "type": "object"
+}Input schema / examplesRemoved value: -[
- {}
-]Output schema / (root)Previous value: -{
- "properties": {
- "id": {
- "description": "Unique identifier for the joke",
- "type": "string"
- },
- "joke": {
- "description": "The joke text",
- "type": "string"
- }
- },
- "required": [
- "id",
- "joke"
- ],
- "type": "object"
-}New value: +nullOutput schema / (root)Previous value: -nullNew value: +{
+ "properties": {
+ "id": {
+ "description": "Unique identifier for the joke",
+ "type": "string"
+ },
+ "joke": {
+ "description": "The joke text",
+ "type": "string"
+ }
+ },
+ "required": [
+ "id",
+ "joke"
+ ],
+ "type": "object"
+}Input schema / examplesAdded value: +[
+ {}
+]Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, and destructiveHint, so safety profile is covered. The description adds valuable context by naming the external source (icanhazdadjoke.com) and the exact return shape (joke text and ID), which goes beyond annotations.
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 short sentences with no filler. The key verb and resource are front-loaded, and every word adds meaning.
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?
The tool is simple, has an output schema, and annotations cover safety. The description provides enough behavioral context (source, return values) to be fully understood without any gaps.
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 and the schema coverage is 100%, so the baseline is 4. The description adds no parameter-specific details, but none are needed since 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 starts with the specific verb 'Fetch' and clearly identifies the resource: 'one random dad joke from icanhazdadjoke.com'. It also states the return values ('joke text and alphanumeric ID'), which distinguishes it from sibling tools like get_joke (specific joke) and search_jokes (search).
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 usage context: use this when you need a random dad joke. It does not explicitly name alternatives or exclusions, but the 'random' qualifier clearly separates it from search/get siblings, providing clear context without needing explicit contrast.
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 are clearly distinct, but the three ask_pipeworx variants (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded) are nearly identical, with the beta explicitly matching the stable version. Additionally, ai_visibility_check and scan_competitor_ai_presence overlap in purpose, creating some selection ambiguity.
All tool names follow a consistent snake_case verb_noun pattern (e.g., get_joke, search_jokes, resolve_entity, validate_claim). Even the more noun-like names like entity_profile and pipeworx_feedback fit the readable convention. No mixed camelCase or erratic verb usage.
With 34 tools, the count is far above the typical well-scoped range of 3-15. The server is named 'dadjokes' but only 3 tools actually serve that purpose; the remaining 31 are an unrelated Pipeworx research and prediction-market platform, making the scope feel bloated and misaligned.
For the nominal dad-joke domain, the surface is complete (get, random, search). However, the intended domain is ambiguous given the massive unrelated toolset; it's unclear what a user should expect the server to cover, and the analysis/prediction tools lack obvious CRUD or management counterparts.