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Glama

Get the athlete's recent runs

get_recent_activities
Read-only

List the signed-in athlete's recent activities with date, distance, duration, pace, average HR, and HR-zone classification. Use to ground training-status questions ("how was my last week?", "did I overdo it?"). Requires authentication.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitYesMaximum number of activities to return.
sportNoFilter by sport type, e.g. "Run", "Ride".
sinceDaysNoOnly return activities from the last N days.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
countYesNumber of activities returned.
windowYesDescription of the query window.
activitiesYesActivity list.

TDQS

A4.2/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true and destructiveHint=false, so the safety profile is known. The description adds value by specifying authentication needs and the specific data fields returned, which are not in annotations. No contradictions.

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 sentences, front-loaded with the core purpose and then usage context. Every sentence is informative and there is zero fluff, making it easy for an agent to parse quickly.

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?

With an output schema present and strong annotations, the description covers the essential operational context: what it lists, when to use it, and auth. It could mention sorting or pagination, but those are not required given the output schema and simple read-only nature.

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 coverage is 100% with clear parameter descriptions (limit, sport, sinceDays). The tool description does not add additional parameter-level detail beyond what the schema already provides, so the baseline score of 3 is appropriate.

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 uses a specific verb ('List') and resource ('signed-in athlete's recent activities') with the exact fields returned (date, distance, duration, pace, avg HR, HR zones). It clearly distinguishes from sibling tools like get_training_load or get_my_athlete, which have different purposes.

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 gives explicit when-to-use guidance via example questions ('how was my last week?', 'did I overdo it?') and states the authentication requirement. It doesn't explicitly call out alternatives or exclusions, but the use case is clear enough for an agent to select it appropriately.

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
Disambiguation5/5

Each tool targets a distinct aspect of endurance running performance: caffeine modeling, fueling, pacing, athlete data, activities, training load, heat acclimation, pacing strategy, periodization, race prediction, and running economy. No overlap.

Naming Consistency3/5

Names use snake_case but mix verb_noun (e.g., get_my_athlete, predict_race_time), noun_verb (periodization_compare), and pure noun (caffeine_protocol, fueling_plan). Inconsistent pattern but still understandable.

Tool Count5/5

11 tools cover the domain of running performance modeling without being too many or too few. Each tool serves a clear purpose within the server's scope.

Completeness4/5

Covers most core areas: personal data retrieval, activity history, training load, and multiple performance models. Minor gaps like workout creation or nutrition beyond fueling plan, but the surface is largely complete for modeling.

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