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Glama
aaryagodbole

dsa-coach-mcp

by aaryagodbole
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

Server Configuration

Describes the environment variables required to run the server.

NameRequiredDescriptionDefault
DSA_COACH_DBNoWhere revision cards are stored~/.dsa-coach/coach.db

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": false
}
prompts
{
  "listChanged": false
}
resources
{
  "subscribe": false,
  "listChanged": false
}

Tools

Functions exposed to the LLM to take actions

NameDescription
list_platformsB

Connected platforms, with the tags and levels each one accepts as filters.

get_profileB

The user's standing on one platform: level, solved and unsolved counts per tag, and their most recent submissions.

search_problemsA

Problems having every tag given, at the level given. Tags and levels must be values from list_platforms. With exclude_solved_by, skips that handle's solved problems.

log_reviewA

Record how well the user recalled a problem (0 = could not, 5 = instantly), schedule the next revision, and journal any mistakes made on the way.

    Pass the problem's tags from search_problems or get_profile. Give each mistake a
    short kind, reusing an existing one from mistake_kinds when it fits, and a note
    on what went wrong in the user's own words.
revisions_dueB

Problems due for revision today or overdue, oldest first.

mistake_kindsA

Every mistake label used so far, most frequent first. Reuse these labels when logging new mistakes so the same kind of mistake is counted together.

list_mistakesB

Journaled mistakes, newest first, with the problem each was made on. Filter by label, problem tag, platform, one problem (platform and problem_id), or the last days days.

mistake_patternsB

Mistake counts per label, broken down by problem tag, platform and month. Counts only; reading what they say about the user is up to you.

Prompts

Interactive templates invoked by user choice

NameDescription
practice_planPlan the next practice session from the user's own mistakes and progress.
revision_quizQuiz the user on today's revisions using the mistakes they made before.

Resources

Contextual data attached and managed by the client

NameDescription

No resources

Deploy Server
Shareable, User-Owned Context BackendPeople, applications, and agents read, write, and coordinate together in one context lake. Reach it over MCP, a CLI, a Python SDK, or REST.fulcradynamics.comAd

TDQS

B3.4/5.0

Scored across 8 tools

Disambiguation4/5

Each tool maps to a distinct action: platform metadata, profile lookup, problem search, review logging, due revisions, mistake labels, mistake journaling, and mistake analytics. The closest overlap is between mistake_kinds (label vocabulary) and mistake_patterns (aggregated counts), but the descriptions make the boundary clear enough.

Naming Consistency3/5

The tools are all snake_case and readable, but they mix verb-first names (list_platforms, get_profile, search_problems, log_review) with noun-phrase names (revisions_due, mistake_kinds, mistake_patterns). The inconsistency is noticeable but not chaotic.

Tool Count5/5

Eight tools is a well-scoped set for a DSA coaching workflow, covering discovery, review scheduling, and mistake tracking without obvious filler. Each tool appears to earn its place.

Completeness4/5

The surface covers the main coaching loop: inspect platforms and profiles, find problems, log reviews, see due revisions, and analyze mistakes. Minor gaps exist around explicitly updating solved status or managing platform connections, but core workflows are supported.

Maintenance

ActivityMaintained
ResponsivenessNo issues

Resources