Learners

MCP server that turns any source material into a guided learning experience:

• Orientation — material-level learning map + per-section focus briefs; cross-material concept linking.
• Notes — handwritten-style Markdown notes extracted via a map → reduce → consistency pipeline.
• Four-phase study loop — Preview → Explain → Question → Anchor (soft-guidance, not locked).
• Flashcards — SM-2 spaced repetition; no duplicates.
• Grounded Q&A — ad-hoc questions against the material with [§N] citations.
• Prerequisite checks — before a section, surface unmastered cards from sections its key concepts build on.
• Study plans — calendar-aware schedule using focus-brief time estimates + suggested path.
• Streak + weekly report — activity roll-ups across the library.
• Phase evaluation — opt-in structured assessment of a phase response (strengths, gaps, misconceptions, follow-ups).
• Exports — auto-written learner Markdown mirror, Anki .apkg, CSV, combined Markdown notes, explicit JSON artifact export, portable project JSON (full round-trip).

Host-agnostic: works with any MCP-capable agent (Claude Desktop, Claude Code, Codex, Gemini, Cursor, Zed, Continue, etc.).

---

Quick Start

What you need

• uv — a fast Python package manager that handles everything below.
• Claude Desktop (or another MCP-compatible app).
• An API key from Anthropic (or another provider — see Configuring models below).

---

1. Install uv

uv installs Python for you and runs packages in isolated environments — no separate Python install needed.

macOS / Linux — open Terminal and run:

curl -LsSf https://astral.sh/uv/install.sh | sh

Windows — open PowerShell and run:

powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex"

Close and reopen the terminal after install so the uvx command is available.

---

2. Get an API key

Go to console.anthropic.com, sign up or log in, and create an API key. It will look like sk-ant-.... Copy it — you'll need it in the next step.

---

3. Register with Claude Desktop

Find and open the Claude Desktop config file in any text editor (Notepad on Windows, TextEdit on macOS):

• macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
• Windows: press Win + R, type %APPDATA%\Claude, press Enter, then open claude_desktop_config.json

If the file is empty or doesn't exist, create it with exactly this content (replace the key with yours):

{
  "mcpServers": {
    "learners": {
      "command": "uvx",
      "args": ["learners-mcp"],
      "env": { "ANTHROPIC_API_KEY": "sk-ant-..." }
    }
  }
}

If the file already has other servers, add the "learners" block inside the existing "mcpServers" section — don't replace what's already there.

Restart Claude Desktop. The learners server should appear in the tools panel (the hammer/plug icon). uvx downloads and runs learners-mcp automatically on first use — no separate install step needed.

---

5. Load your first book or PDF

Open a chat in Claude Desktop and say something like:

Load this PDF for studying: /Users/yourname/Documents/my-book.pdf

On Windows the path looks like C:\Users\yourname\Documents\my-book.pdf.

Claude will ingest the file and return a confirmation. Then ask:

Prepare it — generate the learning map, focus briefs, and notes.

This takes one to two minutes depending on file size. When it finishes, ask:

Show me the learning map.

You'll get a structured overview: key concepts, difficulty, time estimate, and a suggested reading path. From there, ask Claude to start a section and it will walk you through the four-phase study loop — Preview, Explain, Question, and Anchor (spaced-repetition flashcards).

You can also load a URL or a YouTube video URL the same way:

Load this for studying: https://en.wikipedia.org/wiki/Thermodynamics

---

Troubleshooting

"command not found: uvx" — close and reopen the terminal after installing uv. On Windows, open a fresh PowerShell window.

"API key missing" or requests failing — check that the key in claude_desktop_config.json starts with sk-ant- and has no extra spaces or quote characters around it. Restart Claude Desktop after any edit to that file.

Learners server not showing up in Claude Desktop — the JSON file likely has a syntax error (a missing comma or brace). Paste its contents into jsonlint.com to find the problem.

To start completely fresh — delete the data folder:

• macOS/Linux: ~/.learners-mcp/
• Windows: %USERPROFILE%\.learners-mcp\

---

Configuring models

By default learners-mcp uses Anthropic models (haiku for fast tasks, sonnet for most work, opus for the learning map). Copy examples/llm.yaml to ~/.learners-mcp/llm.yaml to change models, providers, or per-task routing.

YAML structure

profiles:
  default:
    model: openrouter/anthropic/claude-sonnet-4.6
    params:
      reasoning_effort: low
    prompt_cache: auto  # auto|on|off

routes:
  qa: default
  learning_map: oneshot
  # ... (11 tasks total — see examples/llm.yaml for full list)

Supported providers

Any provider supported by LiteLLM: Anthropic, OpenRouter, OpenAI, Gemini, Bedrock, Vertex, and custom OpenAI-compatible endpoints.

Set the matching API key in your environment — LiteLLM reads them automatically: ANTHROPIC_API_KEY, OPENROUTER_API_KEY, OPENAI_API_KEY, GEMINI_API_KEY.

The 11 tasks and default profiles

Task	Default profile	Used for
notes_map, notes_tldr, focus_brief	fast (haiku)	Per-chunk work, high volume
notes_reduce, notes_polish, rolling_summary, qa, phase_evaluation, completion_report, flashcards	default (sonnet)	Most analytic work
learning_map	oneshot (opus)	Material-level orientation, one call

Env overrides

Override without editing the YAML:

• LEARNERS_MCP_MODEL_DEFAULT=gpt-4o-mini — change the model for a profile
• LEARNERS_MCP_PARAMS_DEFAULT='{"reasoning_effort":"low"}' — change params (JSON)
• LEARNERS_MCP_ROUTE_QA=fast — re-route a task to a different profile
• LEARNERS_MCP_LLM_CONFIG=/path/to/llm.yaml — use a custom config path

Prompt caching

For Anthropic-family models (including via OpenRouter), cache_control blocks are preserved and caching applies automatically. Non-Anthropic models (OpenAI, Gemini, etc.) use flat text — no block-level caching, so map-reduce pipelines cost more. Set prompt_cache: on to force pass-through if you know your proxy supports it.

Developer install

pip install -e ".[dev]"   # editable + test deps
export ANTHROPIC_API_KEY=sk-ant-...

Register with a host

Claude Desktop (claude_desktop_config.json — see Quick Start for file location):

{
  "mcpServers": {
    "learners": {
      "command": "uvx",
      "args": ["learners-mcp"],
      "env": { "ANTHROPIC_API_KEY": "sk-ant-..." }
    }
  }
}

Claude Code — .mcp.json in the project or ~/.claude.json:

{ "mcpServers": { "learners": { "command": "learners-mcp" } } }

Other MCP hosts (Codex, Gemini CLI, Cursor, Zed, Continue) use the same command + env pattern — consult their docs for the exact config file location.

Surface

• Tools (38): ingestion/prep (ingest_material, prepare_material, get_preparation_status, start_background_preparation, get_background_status); orientation (get_material_map, regenerate_map, get_focus_brief); notes (get_notes, extract_notes_now); library (list_sections, list_materials, material_progress, library_dashboard); study loop (start_section, get_phase_prompt, record_phase_response, complete_phase, check_prerequisites, plan_study, study_streak, weekly_report); evaluation (evaluate_phase_response, list_evaluations); flashcards (suggest_flashcards, add_flashcard, list_flashcards, review_flashcard, next_due); ad-hoc (answer_from_material, recommend_next_action); completion (get_completion_report, regenerate_completion_report); exports (export_anki, export_notes, export_material_artifacts, export_project, import_project).
• Resource templates: material://{id}, learning_map://{id}, focus_brief://{section_id}, notes://{id}, notes://{id}/{section_id}, section://{section_id}, section_state://{section_id}, completion_report://{section_id}, evaluations://{section_id}, plan://{material_id}.
• Concrete resources: library://, review://due, streak://, report://weekly.
• Prompts: preview, explain, question, anchor (phase-coaching prompts the host agent executes).

Typical flow

Preferred study order:

1. list_materials() if the library may already contain the source; otherwise ingest_material("/path/to/book.pdf").
2. prepare_material(material_id) or start_background_preparation(material_id) immediately after ingest.
3. get_preparation_status(material_id) and prefer waiting until the learning map and focus briefs are ready.
4. get_material_map(material_id) once per material to orient the learner before section work.
5. list_sections(material_id) and choose the next section.
6. start_section(section_id) to activate it.
7. For each phase in order (preview → explain → question → anchor): host invokes the matching prompt, learner responds, server records via record_phase_response + complete_phase.
8. In Anchor: suggest_flashcards → add_flashcard × N. complete_phase(section_id, 'anchor') triggers a completion report.
9. Later: recommend_next_action(material_id), next_due(material_id), and review_flashcard(id, knew_it) drive follow-up study and spaced repetition.

Prefer orientation before deep section work unless the learner explicitly wants to jump ahead.

Context discipline

Heavy learner artifacts and section source text are available through MCP resources and the Markdown mirror in ./learners/<material-slug>/.

Tool calls intentionally return compact summaries for the model and link out to full resources instead of dumping large Markdown or section bodies into the chat context. Read the linked resources when you need the full artifact.

Learner artifacts

Generated study material is mirrored as readable Markdown in ./learners/<material-slug>/ by default. The SQLite DB remains the canonical state, but the learner can open files such as learning-map.md, focus-briefs.md, notes.md, flashcards.md, and progress.md directly from the working directory.

Set LEARNERS_MCP_ARTIFACT_DIR=/path/to/dir to write the mirror somewhere else, or LEARNERS_MCP_ARTIFACT_MIRROR=off to disable automatic Markdown writes. JSON artifacts are explicit only: call export_material_artifacts(material_id, format="json") or format="all" to write structured files under json/.

State

SQLite DB + server config live in ~/.learners-mcp/. Override with LEARNERS_MCP_DATA_DIR. Delete the directory to start fresh.

Tests

pip install -e ".[dev]"
pytest