PlyMove

PlyMove

Elo-aware chess game storyboards.

Give PlyMove a chess game (PGN) and a learner Elo. It returns an opening-aware story arc, ranked critical chapters, what-if branches, and a main lesson.

Explore the APIUse PlyMove via MCPContact for embedded access

Three questions. One pedagogical layer.

PlyMove does not replace Stockfish or human-move models. It combines them with learned annotation priors and learner-relative scoring to decide what to teach.

Stockfish

Objective engine analysis.

“What is objectively best?”

Human-move modelling

Plausible alternatives at a strength band.

“What is a player likely to consider?”

PlyMove

Learner-specific teaching decisions.

“What should this learner be taught?”

One concrete example

Homepage copy stays learner-facing. Expand for a sample developer payload.

Input
FEN: … (middlegame tactics)
target_elo: 1600
Why this position matters

Qc4 looks natural, but it allows Qe7#.Qxe3 keeps the game going.

For a 1600-rated learner, this is worth highlighting because the losing move is human-plausible and the tactical punishment is immediate.

Developer output (illustrative)
{
  "schema": "plymove/explain-position-v1",
  "target_elo": 1600,
  "teaching_reason": "critical_position",
  "explanation": "Qc4 looks natural, but it allows Qe7#. Qxe3 keeps the game going."
}

Capabilities

Build game storyboard

PGN + learner Elo → opening · story arc · chapters · main lesson

Explain a position

FEN + learner Elo → learner-specific explanation

Compare moves

Position + candidate moves → learner-relative comparison

Analyze what-if

PGN + anchor + alternative SAN → Stockfish line · Maia · RAV PGN

Legacy aliases

explain_game and find_teaching_moments→ same storyboard v2 flow

Same intelligence, multiple surfaces

REST, MCP, and agent-paid calls share the Core analysis stack.

  • REST APIProgrammatic access with API keys for products and backends.
  • MCPSix remote tools for AI agents and MCP-compatible clients.
  • ChatGPTApps SDK adapter at chatgpt.plymove.com (same Core stack).
  • Agent-nativePaid HTTP calls where x402-compatible payment is enabled.
  • Batch / embeddedVolume annotation and OEM integrations.

Built for AI agents

PlyMove can be called as a specialist chess capability by AI agents, without turning your agent into a chess engine.

“Build a storyboard of this game for a 1450-rated learner.”
↓
AI agent calls PlyMove (MCP or REST)
↓
Structured storyboard returned (arc, chapters, lesson)

PlyMove provides pedagogical chess intelligence via MCP and REST APIs. MCP: https://mcp.plymove.com/mcp (Streamable HTTP; API key or x402).MCP docs →·Agent payment details →

Embed learner-adaptive explanations

Add learner-adaptive chess explanations to your existing product without building the chess-teaching stack yourself.

Outputs include JSON storyboards with opening context, causal chapters, branches, and pedagogical metadata.

Discuss embedded access