get_groupsGet all AKC dog breed groups (e.g., Sporting, Herding, Terrier). Returns group names and descriptions.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
| Name | Required | Description | Default |
|---|---|---|---|
| count | Yes | Number of breed groups | |
| groups | Yes | Array of AKC dog breed groups |
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 breed groups",
+ "type": "number"
+ },
+ "groups": {
+ "description": "Array of AKC dog breed groups",
+ "items": {
+ "properties": {
+ "id": {
+ "description": "Group identifier",
+ "type": "string"
+ },
+ "name": {
+ "description": "Group name",
+ "type": "string"
+ }
+ },
+ "required": [
+ "id",
+ "name"
+ ],
+ "type": "object"
+ },
+ "type": "array"
+ }
+ },
+ "required": [
+ "count",
+ "groups"
+ ],
+ "type": "object"
+}Input schema / examplesAdded value: +[
+ {}
+]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, covering the safety profile. The description adds the return content (group names and descriptions) but this is likely already captured by the output schema. No additional behavioral traits like rate limits or auth needs are mentioned.
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, highly concise, and front-loaded with the core purpose. The example groups add clarity without unnecessary length. Every word earns its place.
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—no parameters, an output schema exists, and annotations cover safety. The description is complete for the task: it states what the tool retrieves, gives examples, and mentions the returned fields. No additional context is 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?
The tool has 0 parameters, and the baseline for 0 params is 4. The description does not need to explain parameter semantics. It adds context about the return value (group names and descriptions) which is helpful for understanding the tool's output.
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 specific action ('Get all AKC dog breed groups'), identifies the resource (AKC dog breed groups), and provides examples (Sporting, Herding, Terrier). It is distinct from sibling tools like get_breed and list_breeds by explicitly focusing on groups rather than breeds.
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 the tool is for retrieving breed groups and gives examples of what to expect. It does not explicitly mention alternatives or when not to use, but the context is clear enough to avoid confusion with related breed tools. No explicit exclusions are given.
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.