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Random-Stack-Random-Day

RepStack

by Random-Stack-Random-Day
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

Server Configuration

Describes the environment variables required to run the server.

NameRequiredDescriptionDefault
REPSTACK_LLM_PROVIDERNoOptional LLM provider name (default 'openai'). To use LLM parsing, set this to a registered provider name.openai
REPSTACK_OPENAI_MODELNoOptional model name for OpenAI (default 'gpt-4o-mini').gpt-4o-mini
REPSTACK_OPENAI_API_KEYNoAPI key for OpenAI (required if using OpenAI provider).

Instructions

Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.

This server publishes no instructions, or was last inspected before Glama recorded them.

Capabilities

Features and capabilities supported by this server

Protocol revision2025-11-25

CapabilityDetails
tools
{
  "listChanged": true
}
logging
{}
prompts
{
  "listChanged": false
}
resources
{
  "subscribe": false,
  "listChanged": false
}

Tools

Functions exposed to the LLM to take actions

NameDescription
repstack.ingest_logA

Ingest a workout log (text, CSV, or JSON). Returns canonical structured JSON, issues, and summary. Stateless: does not store anything. Set allow_llm=true for text and configure an LLM parser to use it; response includes meta.llm_available and meta.llm_used.

repstack.compute_metricsA

Compute deterministic metrics from provided canonical data (stateless). Provide either sessions (array of canonical session objects) or logs (array of { canonical_json: { sessions } }). Optional range: { start, end } (YYYY-MM-DD) to filter; if omitted, all provided sessions are used. Returns weekly volume, tonnage, e1rm, PRs, volume_spike flags. Payloads exceeding max_sessions or max_sets return needs_clarification.

repstack.search_exercisesA

Search the local exercise registry by query (matches display and aliases). Returns { query, count, results } with match metadata (strategy, score, matched_text, normalized_query) and is_exact_match. Optional filters: equipment, movement_pattern. Optional limit (default 20).

Prompts

Interactive templates invoked by user choice

NameDescription

No prompts

Resources

Contextual data attached and managed by the client

NameDescription

No resources

Deploy Server
One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

TDQS

A4.4/5.0

Scored across 3 tools

Disambiguation5/5

Each tool has a clearly distinct purpose: ingest_log parses and normalizes workout data, compute_metrics derives analytics from canonical data, and search_exercises queries the exercise registry. There is no overlap or ambiguity between them.

Naming Consistency5/5

All tool names follow a consistent verb_noun snake_case pattern: ingest_log, compute_metrics, search_exercises. The naming is predictable and clearly indicates what each tool does.

Tool Count5/5

Three tools is well-scoped for a stateless workout log processing server. Each tool covers a necessary step in the workflow without redundancy or bloat.

Completeness5/5

The tool set covers the full intended pipeline: ingest raw logs into canonical JSON, compute metrics from canonical sessions, and search exercises for enrichment or validation. Since the design is explicitly stateless, no persistence/retrieval tools are needed.

Maintenance

ActivityInactive
ResponsivenessNo issues

Resources