list_factsGet random dog facts. Returns interesting trivia about dog behavior, history, and abilities.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Number of facts to return (default: 10, max: 100) |
| Name | Required | Description | Default |
|---|---|---|---|
| count | Yes | Number of facts returned | |
| facts | Yes | Array of random dog facts |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Output schema / (root)Previous value: -nullNew value: +{
+ "properties": {
+ "count": {
+ "description": "Number of facts returned",
+ "type": "number"
+ },
+ "facts": {
+ "description": "Array of random dog facts",
+ "items": {
+ "properties": {
+ "fact": {
+ "description": "Dog fact text",
+ "type": "string"
+ },
+ "id": {
+ "description": "Fact identifier",
+ "type": "string"
+ }
+ },
+ "required": [
+ "id",
+ "fact"
+ ],
+ "type": "object"
+ },
+ "type": "array"
+ }
+ },
+ "required": [
+ "count",
+ "facts"
+ ],
+ "type": "object"
+}Input schema / examplesAdded value: +[
+ {
+ "limit": 10
+ },
+ {
+ "limit": 5
+ }
+]Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false, covering safety and side-effect behavior. The description adds 'random' as a behavioral trait and mentions the content type, but does not disclose additional details like rate limits or pagination. This is acceptable given annotation coverage, so a baseline 3 applies.
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?
Two concise, front-loaded sentences. The first sentence states the core action, and the second adds relevant detail about the content. No wasted words or redundant information.
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 with one optional parameter, a complete schema, rich annotations, and an output schema. The description fully conveys what the tool does without needing to explain return formats, which are already covered by the output schema.
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%: the only parameter, limit, has a clear description including default and max values. The description adds no extra parameter information, but it doesn't need to since the schema fully documents it. 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's function with a specific verb and resource: 'Get random dog facts.' It further specifies the content domains (behavior, history, abilities), which distinguishes it from sibling tools like list_breeds or get_breed. This is a clear, specific 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 description implies usage context: use when you want random dog facts. No alternatives are mentioned because no sibling tool offers the same randomized fact-fetching capability, so exclusions are unnecessary. The clear context earns a 4.
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 genuinely unclear boundaries: ask_pipeworx and ask_pipeworx_beta are described as currently identical, ask_pipeworx_grounded shares the same router, and the polymarket_edges/arbitrage/fill_risk/bet_research group overlaps heavily in purpose. The remaining clusters (dog data, memory, subscriptions) are mostly distinct, so the confusion is concentrated in a few spots but severe there.
Nearly all names are lower_snake_case and readable, but the conventions are mixed: get_/list_/ask_/scan_ verb-noun names sit alongside bare verbs (remember, forget, recall), noun-phrase names (entity_profile, bet_research, pipeworx_trending), and a versioned suffix (ask_pipeworx_beta). No single predictable pattern covers the whole set.
At 35 tools, the server is well past the 25+ threshold for feeling bloated, and the count is dominated by unrelated Pipeworx, prediction-market, and AI-visibility tools rather than the dog-data domain implied by 'dogsapi'. Only four tools actually serve the dog API, making the surface both oversized and misaligned with the server name.
The broad data-access side is thorough, covering discovery, universal routing, grounded answers, deep research, entity profiles, comparisons, claim validation, memory, and subscriptions. However, the nominal dog domain is thin (list/get/groups/facts with no filtering or additional operations), subscriptions have no update path, and some one-off tools like generate_llms_txt and scan_dependency exist without any surrounding lifecycle.