animalsAPI Ninjas animals: facts about an animal species by name. Returns a list of { name, taxonomy, locations, diet, habitat, lifespan, top_speed }. Example: animals({ name: "cheetah" }).
| Name | Required | Description | Default |
|---|---|---|---|
| name | Yes | Animal name to look up, e.g. 'cheetah', 'lion' | |
| _apiKey | No | Optional — your own API Ninjas key for higher limits; omit to use the shared Pipeworx key. |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare read-only/idempotent/non-destructive behavior, so the safety profile is covered. The description adds the external API Ninjas provenance and, since there is no output schema, the concrete return shape (list of name, taxonomy, locations, diet, habitat, lifespan, top_speed). It does not address rate limits or error behavior, but these are not essential for this simple read-only lookup.
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?
Compact and front-loaded: purpose, return fields, and an example with no filler. Every sentence earns its place, and the most important usage detail (lookup by name) appears immediately.
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 one-required-parameter read-only lookup, the description provides enough: input example, output fields, and data source. The optional API key nuance is already captured in the schema, and because no output schema exists, the explicit field list completes the picture.
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%; both name and _apiKey have descriptions, including an example for name. The description's 'by name' and cheetah example echo that schema rather than adding new meaning. Baseline 3 applies because the schema carries the parameter semantics.
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?
Opens with a specific verb+resource: 'facts about an animal species by name'. It names the exact returned fields, so an agent can identify it as a direct animal-fact lookup rather than a research or comparison tool. The example appends a concrete invocation, removing ambiguity.
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?
No explicit when/when-not guidance or alternative tool names appear. The phrase 'by name' and the cheetah example imply that this tool is for fetching facts by species name, but an agent is left to infer when to choose it over broad research tools like deep_research or entity_profile.
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 have distinct roles, but there is meaningful overlap among the question-answering family (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research) and among the Polymarket analysis tools (bet_research, polymarket_arbitrage, polymarket_edges, polymarket_fill_risk). The long descriptions help separate them, but the boundaries are still subtle enough that an agent could easily pick the wrong variant.
The naming is mostly snake_case and generally follows a verb_noun pattern (ask_pipeworx, compare_entities, resolve_entity, scan_dependency, validate_claim). Deviations like entity_profile, recent_alerts, recent_changes, and bare verbs (forget, recall, remember, subscribe, unsubscribe) are minor and do not seriously harm predictability.
31 tools is heavy for a single server and suggests the surface is a bundled platform (data queries, prediction markets, memory, subscriptions, AI-visibility checks) rather than one tightly scoped domain. Each tool has a rational purpose, but the sheer count plus several meta/didactic tools makes the set feel somewhat oversized.
Core workflows are well covered: entity resolution, profiles, comparisons, grounded lookup, fact-checking, deep research, memory CRUD, and subscription lifecycle. Gaps are minor. There are no update operations for subscriptions, and some optional data sources degrade softly, but agents can accomplish the intended research, monitoring, and memory tasks without dead ends.