get_factsGet multiple random cat facts at once. Specify count (e.g., 5). Returns array of fact texts with character lengths.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Number of facts to return. Defaults to 5. |
| Name | Required | Description | Default |
|---|---|---|---|
| facts | Yes | List of cat facts | |
| total | Yes | Total number of available facts |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Output schema / (root)Previous value: -nullNew value: +{
+ "properties": {
+ "facts": {
+ "description": "List of cat facts",
+ "items": {
+ "properties": {
+ "fact": {
+ "description": "The cat fact text",
+ "type": "string"
+ },
+ "length": {
+ "description": "Character length of the fact",
+ "type": "number"
+ }
+ },
+ "required": [
+ "fact",
+ "length"
+ ],
+ "type": "object"
+ },
+ "type": "array"
+ },
+ "total": {
+ "description": "Total number of available facts",
+ "type": "number"
+ }
+ },
+ "required": [
+ "total",
+ "facts"
+ ],
+ "type": "object"
+}Input schema / examplesAdded value: +[
+ {
+ "limit": 5
+ },
+ {
+ "limit": 10
+ }
+]Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Beyond the read-only, idempotent, and non-destructive annotations, the description discloses that facts are random and that it returns an array of fact texts with character lengths, adding useful behavioral context. It does not mention the default count, but the schema covers that.
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, front-loaded with the core action, and every sentence earns its place. No redundant or filler content.
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's simplicity, full annotations, and output schema, the description is complete: it covers purpose, randomness, count specification, and return format. No significant gaps remain.
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 schema provides full coverage for the 'limit' parameter with a description and default value. The tool description adds an example ('e.g., 5') but no additional meaning beyond the schema, so 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 uses a specific verb ('Get'), resource ('multiple random cat facts'), and scope ('at once'), clearly distinguishing it from the singular sibling get_fact. It also mentions specifying a count, which clarifies the tool's function.
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 gives clear context: use this tool when you need multiple random cat facts at once. It does not explicitly mention alternatives or exclude single-fact usage, but the plural language implies the distinction from get_fact.
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 nearly identical in routing, differing only in response mode; bet_research and polymarket_edges both surface betting opportunities. Even with detailed descriptions, an agent could easily select the wrong one for a given task.
Tool names are all snake_case, but the pattern is inconsistent: some are verb_noun (get_fact, list_breeds, validate_claim), some are noun/adjective compounds (entity_profile, deep_research, bet_research), and several use a pipeworx_ prefix. There is no consistent verb style or object-first convention.
34 tools is over the 25-tool threshold for a server whose name suggests a narrow cat-facts focus. Only 3 tools relate to cat facts; the rest form a sprawling data platform, creating a severe scope mismatch that makes the count feel excessive and unfocused.
For the cat-facts domain, the set covers the essentials: single fact, multiple facts, and breed listing. However, the overall tool surface is a mix of unrelated capabilities (data lookups, memory, subscriptions, prediction markets) that don't form a coherent domain, leaving the cat-facts portion sparse and the broader set without clear lifecycle coverage.