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Api Ninjas

by io.github.pipeworx-io

Exercises

exercises
Read-onlyIdempotent

API Ninjas exercises: fitness/gym exercises filtered by target muscle, type, difficulty, or name. Returns a list of { name, type, muscle, equipment, difficulty, instructions }. Example: exercises({ muscle: "biceps", difficulty: "beginner" }).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameNoExercise name to search for
typeNoExercise type, e.g. 'strength', 'cardio', 'stretching'
muscleNoTarget muscle, e.g. 'biceps', 'chest', 'quadriceps'
_apiKeyNoOptional — your own API Ninjas key for higher limits; omit to use the shared Pipeworx key.
difficultyNoDifficulty: 'beginner', 'intermediate', or 'expert'

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Added

TDQS

A4/5.0
Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already establish the read-only, idempotent, non-destructive nature of the operation. The description adds the concrete return shape and a usage example, which is useful but does not go far beyond what annotations communicate.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is two tight sentences with a helpful example and zero filler. Key identity and filtering scope are front-loaded, and every sentence earns its place.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple, read-only, zero-required-param tool, the description is largely complete: it communicates the output format, filter options, and an example call. Minor gaps such as default behavior with no filters or pagination details are not critical given the annotations and schema.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so every parameter is already documented. The description reinforces the filter dimensions and provides an example combining muscle and difficulty, but it does not add substantial semantics beyond the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the resource ('fitness/gym exercises'), the action ('filtered by'), and the specific filter dimensions (muscle, type, difficulty, name). It also lists the exact return fields, making the tool's purpose unambiguous and distinct from the unrelated sibling tools.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies when to use the tool: when an agent needs exercise data filtered by muscle, type, difficulty, or name. It does not explicitly name alternatives or exclusion conditions, but no sibling tool covers this domain, so the context is clear enough for correct routing.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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TDQS

A4.1/5.0
Disambiguation3/5

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.

Naming Consistency4/5

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.

Tool Count3/5

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.

Completeness4/5

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.

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