list_equipmentList all gym equipment types in the wger database. Returns each item's numeric ID and name (e.g., 'Barbell', 'Dumbbell', 'Kettlebell').
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
| Name | Required | Description | Default |
|---|---|---|---|
| count | Yes | Total number of equipment types in the database | |
| equipment | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare read-only, idempotent, non-destructive behavior. The description adds 'Returns each item's numeric ID and name' and examples, which is useful but largely duplicates what the output schema likely provides. No extra behavioral context like ordering or completeness guarantees beyond 'all' is given.
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 exactly two sentences, front-loaded with the primary action and resource, and includes concrete examples without unnecessary padding. Every sentence adds value.
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 zero parameters, rich annotations, an output schema, and a simple list operation, the description fully covers the essential context: the resource domain, scope ('all'), and return contents. No critical information is missing.
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, so there is nothing to describe. The baseline for zero-parameter tools is 4, and the description's 'all' confirms no filtering inputs exist, aligning with the empty input schema.
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 ('List') and resource ('all gym equipment types in the wger database') and clarifies the returned data ('numeric ID and name'). This clearly distinguishes it from sibling list tools like list_exercises and list_muscles.
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 purpose implies the usage scenario (when you need gym equipment types), but it does not explicitly state when to prefer this tool over alternatives or when not to use it. No exclusions or alternative tool references 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.
Many tools have detailed, differentiated roles, but there are several overlapping clusters: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, and validate_claim all answer natural-language data questions, and ask_pipeworx_beta is currently identical to ask_pipeworx. Onboarding/discovery and Polymarket edge tools also blur together, so an agent can easily select the wrong entry point.
Names are uniformly snake_case and readable, with coherent subfamilies like ask_pipeworx*, polymarket_*, and list_*. But conventions are mixed across the set: bare verbs (remember, forget, subscribe), noun phrases (entity_profile, recent_changes), and adjective-led names (recent_alerts) exist alongside verb_noun names, so there is no consistent pattern.
35 tools is already in the 'too many' range, and only four (get_exercise, list_exercises, list_equipment, list_muscles) belong to a wger fitness server. The remaining ~31 tools are unrelated Pipeworx/prediction-market/memory utilities, making the count inappropriate for the apparent domain.
As a wger fitness server, the surface is a read-only reference slice: exercise, equipment, and muscle lookups, with no workout routine management, user data, or create/update/delete operations for any wger resource. Even ignoring the unrelated Pipeworx tools, the fitness domain has severe gaps that would block most real usage.