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Hybriqx Workout Adaptation

Server Details

Advisory workout adaptation via hosted MCP and x402. Python buyer with operator-approved tools.

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Status
Healthy
Last Tested
Transport
Streamable HTTP · MCP 2025-11-25
URL

TDQS

B3.4/5.0

Scored across 2 tools

Disambiguation5/5

scale_workout is a domain action tool and service_info is a meta/status tool; their purposes are clearly distinct with no overlap.

Naming Consistency5/5

Both tools use consistent snake_case verb_noun / noun_noun naming (scale_workout, service_info) following a predictable pattern.

Tool Count3/5

Two tools is thin for a server whose domain involves workout adaptation; service_info is auxiliary and only one functional tool exists.

Completeness3/5

scale_workout covers the core transformation, but there is no way to list available equipment, validate workouts, or retrieve workout details, leaving notable gaps for an adaptation workflow.

Available Tools

2 tools
scale_workoutBInspect

Scale a CrossFit workout to available equipment and a target time.

    Args:
        original_wod: Raw text of the workout to adapt.
        available_equipment: Equipment available to the athlete.
        target_time_domain_mins: Desired workout duration, or null to preserve it.
        athlete_notes: Movement restrictions or injuries to treat as constraints.
    
ParametersJSON Schema
NameRequiredDescriptionDefault
original_wodYes
athlete_notesNo
available_equipmentYes
target_time_domain_minsNo

TDQS

B3.1/5.0
Behavior2/5

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

No annotations are supplied, so the description carries the entire behavioral burden, and it largely deflects to the Args list. It discloses that null target time preserves the original duration and that notes act as constraints, but says nothing about whether anything is persisted, permissions, side effects, or the shape of the result.

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

Conciseness4/5

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

The purpose sentence is front-loaded and waste-free, and the Args block is a compact, scannable list. Slightly redundant with the measured, indented formatting, but nothing is padding.

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

Completeness3/5

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

For a four-parameter tool with no output schema and no annotations, the description covers inputs and intent but omits what the tool returns and any operational caveats. Adequate minimum coverage, with a clear gap on the return contract that no structured field fills.

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

Parameters4/5

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

Schema description coverage is 0%, so the description must compensate, and it does: each of the four parameters gets a short gloss, including the non-obvious semantics that target_time_domain_mins=null preserves the workout duration and that athlete_notes are treated as hard constraints. It falls short of 5 only by not reconciling the stated null behavior with the schema default of 15.

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

Purpose4/5

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

States a specific verb and resource: 'Scale a CrossFit workout to available equipment and a target time,' which tells an agent exactly what transformation is produced. The sibling service_info is clearly unrelated, so no explicit differentiation is required, but the description stops short of a 5 by not clarifying scope on the returned artifact.

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

Usage Guidelines2/5

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

The description never says when to reach for this tool versus alternatives, and the only sibling listed is service_info. No prerequisites, no exclusion conditions, and no guidance about when scaling is or isn't appropriate are provided.

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

service_infoAInspect

Return HTTP endpoint paths, demo status, and payment network.

ParametersJSON Schema
NameRequiredDescriptionDefault

No parameters

Output Schema

ParametersJSON Schema
NameRequiredDescription
nameYes
networkYes
query_endpointYes
payment_requiredYes
payments_enabledYes
wod_scaling_demoYes
wod_scaling_priceYes
wod_scaling_enabledYes
wod_scaling_endpointYes

TDQS

A3.5/5.0
Behavior3/5

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

No annotations are provided, so the description carries the burden, but for a zero-parameter informational read the risk surface is minimal. It discloses the three categories of returned data, yet says nothing about authentication requirements, caching, or whether values are static or dynamic.

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?

A single front-loaded sentence with no filler; every clause names a distinct piece of returned data. Nothing could be removed without losing information.

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?

An output schema exists, so the description need not detail return values, and the one-line summary of what comes back is sufficient for a zero-param info tool. Slightly thin on when the endpoint metadata would be relevant, but nothing an agent needs to invoke it is missing.

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

Parameters4/5

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

The tool takes no parameters, so the baseline is 4. There are no argument semantics to clarify and the description correctly focuses on the return payload instead.

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

Purpose4/5

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

States a specific verb ('Return') and enumerates the resources returned: HTTP endpoint paths, demo status, and payment network. An agent knows exactly what information this yields, though there is no differentiation from siblings (the only sibling, scale_workout, is unrelated so this is not a real gap).

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

Usage Guidelines2/5

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

No guidance on when to call this versus alternatives, no prerequisites, no indication of what triggers the need for endpoint/demo/payment metadata. The agent must infer usage entirely from the resource list.

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

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections.

  1. 2 tool updates
    • First observedscale_workout
    • First observedservice_info

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