get_factGet a random cat fact. Returns the fact text and character length. Use get_facts to retrieve multiple facts at once.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
| Name | Required | Description | Default |
|---|---|---|---|
| fact | Yes | The cat fact text | |
| length | Yes | Character length of the fact |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Output schema / (root)Previous value: -nullNew value: +{
+ "properties": {
+ "fact": {
+ "description": "The cat fact text",
+ "type": "string"
+ },
+ "length": {
+ "description": "Character length of the fact",
+ "type": "number"
+ }
+ },
+ "required": [
+ "fact",
+ "length"
+ ],
+ "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 non-destructive behavior, so the safety profile is covered. The description adds the 'random' nature of the fact and the specific return fields (fact text and character length), which are behavioral details beyond the annotations. It does not mention any edge cases, but for a simple read-only tool this is sufficient.
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 primary action, then the return value, and finally the alternative. Every sentence contributes meaningful information with no redundancy or filler.
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?
For a zero-parameter tool with strong annotations (read-only, idempotent, non-destructive) and a defined output schema, the description provides sufficient detail: what it does, what it returns, and when to use the sibling tool. There are no gaps for this level of complexity.
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 zero parameters and the schema coverage is 100%, so there are no parameter semantics to explain. The baseline for zero parameters is 4; no additional explanation is needed or provided. The description does not need to add parameter information.
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 'Get a random cat fact' with a specific verb and resource, and further specifies what is returned ('fact text and character length'). It also distinguishes from the sibling tool get_facts by indicating this one retrieves a single fact. 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 explicitly says 'Use get_facts to retrieve multiple facts at once,' providing direct guidance on when to use this tool versus the alternative. This is explicit and actionable usage guidance.
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.