STAS Running Coach for Claude

get_user_summary

Start here for most conversations. Load the athlete's profile, goals, rules, recent load, current fitness context, performance evidence, optional bounded historical signals, planning guidance, and persistent STAS memory opportunities. weeklyHistory.recentKeySessionEvidence is the compact 42-day factual register for current planning. It includes all qualifying key sessions unless truncated=true; each row uses columns for date, saved name/type, sourceTrainingId, confirmed actual WORK, confirmed REST only when recorded, and wholeActivity only when no confirmed interval structure exists. It never invents recovery or interval rows; null recovery means no confirmed REST inside that activity. For older comparisons, use get_trainings with a bounded date range, then get_activity_detail for one exact workout. confirmedStructuredIntervals remains a separate verified structured-session index for its stated period; do not infer unshown measurements.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`section`	No

Schema Changelog

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

Changed1 schema field changedMay 3, 2026

changedInput schema / properties / section / enum

Previous value: -[
-  "profile",
-  "history",
-  "pace",
-  "plan"
-]New value: +[
+  "profile",
+  "history",
+  "evidence",
+  "pace",
+  "plan"
+]

First observedApr 17, 2026

TDQS

A4.3/5.0

Behavior5/5

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

With no annotations provided, the description fully discloses behavioral traits: it includes all qualifying key sessions unless truncated, never invents recovery or interval rows, explains null recovery semantics, and clarifies that confirmedStructuredIntervals is a separate verified index. This is rich, non-obvious data behavior.

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 description is dense but every sentence adds value, covering content, caveats, and alternatives. It front-loads the primary use case effectively. A more structured layout could improve scannability, but there is no wasted wording.

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

Completeness5/5

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

Given the minimal schema (single optional parameter) and no output schema, the description is exceptionally complete: it defines the data included, temporal bounds, absence semantics, and relationship to complementary tools. It leaves few questions unanswered.

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

Parameters1/5

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

The input schema has one parameter 'section' with enum values, but the description never mentions it or explains how it filters the response. Schema description coverage is 0%, and the description does not compensate at all, leaving the agent to guess which section to request.

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 tool loads an athlete's profile, goals, rules, recent load, fitness context, and more. It explicitly says 'Start here for most conversations,' which distinguishes it from sibling tools and sets the expectation of a comprehensive summary.

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

Usage Guidelines5/5

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

The description explicitly says to use this tool for most conversations and provides clear alternatives for older comparisons: 'use get_trainings with a bounded date range, then get_activity_detail for one exact workout.' It also explains when not to infer unshown measurements, giving direct usage boundaries.

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

A4.3/5.0

Disambiguation5/5

Each tool targets a distinct resource and action: plan events vs note events, goals/results vs profile sections, preview vs commit, history vs current sections. The boundaries are clearly defined in descriptions, so an agent can reliably select the right tool for a given task.

Naming Consistency5/5

All tool names follow a predictable verb_noun snake_case pattern with consistent verbs (create, delete, get, preview, commit, read, restore, save). The use of 'get' for data retrieval and 'read' for profile-specific history/sections is internally consistent.

Tool Count4/5

At 20 tools, the set is slightly above the typical 3-15 range, but the complexity of the coaching domain justifies the count. The preview/commit pairs and distinct resource types (goals, profile, plan, notes) each earn their place, though the set could be streamlined by merging some read tools.

Completeness4/5

The tool surface covers the main coaching workflows: reading athlete state, managing training plan and notes, editing goals/results, maintaining profile memory, and saving strategy. Minor gaps exist around explicit creation/deletion of goals and profile sections, which are not clearly supported outside the preview/commit flow.

Resources

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