integration_instructionsMCP-native

Stop Copy-Pasting Task Context to AI

mcptask.online is an MCP-native project tracker. Claude Code, Claude Desktop, and any MCP-compatible assistant can read your tasks and log work directly — no copy-paste, no lost context.

content_copyManual Workflow
Task #47: Implement OAuth Login
Description: Add OAuth 2.0 login flow...
Acceptance Criteria: User can login...
south
content_copy
Manual Copy & Paste
south
smart_toyClaude Code
Pasted task context...
schedule5-10 min wasted
check_circleMCP Workflow
smart_toy
Claude Code
Connected via MCP
cable
MCP Server
Direct task access
south
Reading task #47...sync
✓ Description
✓ Acceptance criteria
✓ Related tasks
✓ Attachments
south
AI Working...
bolt10 seconds
ZeroCopy-Paste
AutoLogging

What You Need

Three things. No Ruby, no servers, no CLI magic. A free mcptask.online account is enough to try it today.

1

An MCP-capable AI client

Any client that speaks the Model Context Protocol — the open standard Claude Code uses to connect to external tools.

Examples: Claude Code, Claude Desktop, Cursor, Cline, Continue.

2

A free mcptask.online account

Sign up, create a project, add a few tasks. Two minutes.

No credit card. 30-day Professional trial on every new account.

3

An MCPTASK_TOKEN from Settings → API Access

A long secret string that proves the agent is acting as you. Treat it like a password — anyone with it can act as you on mcptask.online.

Free on every plan. Generate one in a click, revoke it the same way.

Optional for autonomous overnight loops: the `mcptask_runner` gem (Rails projects only) — see the install guide.

AI Reads Tasks Directly via MCP

How It Works

1

Set Up the MCP Connection

Add this entry to your client's MCP config (for Claude Code: `.mcp.json` in your project root; for Claude Desktop: Settings → Developer → Edit Config). The endpoint, transport, and auth header are exactly what every MCP-native client expects.

{
  "mcpServers": {
    "mcptask-online": {
      "type": "sse",
      "url": "https://mcptask.online/mcp/sse",
      "headers": { "Authorization": "Bearer ${MCPTASK_TOKEN}" }
    }
  }
}
  1. 1
    Sign up at mcptask.online (free, 30-day Professional trial)
  2. 2
    Open Settings → API Access → click "Generate token"
  3. 3
    Copy the token, then `export MCPTASK_TOKEN=mcptask_...` in your shell (or add it to ~/.zshrc / ~/.bashrc)
  4. 4
    Paste the config above into `.mcp.json` (or your client's MCP settings)
  5. 5
    Restart your AI client
  6. 6
    Ask the agent: "List the next open task from my mcptask.online project."

Where this goes: `.mcp.json` (Claude Code) or the developer's MCP config dialog (Claude Desktop, Cursor, Cline). The `${MCPTASK_TOKEN}` reference is resolved from the environment variable — export it once in your shell profile so every client picks it up.

arrow_downward
2

Ask the AI to Read a Task

In plain English, ask the agent to work on something. The agent resolves `mcptask://pieces/{account_code}/{piece_id}` against mcptask.online over MCP — no copy-paste.

"Read task #47 and implement the login feature"

The URI pattern is the canonical read entry point. Everything in mcptask.online is a **piece** — tasks, stories, epics, recurents, projects — and they all share the same `mcptask://pieces/{account_code}/{id}` shape.

linkmcptask://pieces/jchsoft/10575
arrow_downward
3

AI Gets Full Context Automatically

AI receives:

  • check_circlePiece name and full description
  • check_circleParent epic/story context
  • check_circleAcceptance criteria
  • check_circleRelated pieces and dependencies
  • check_circleAll comments and discussion
  • check_circleAttached files (specs, designs)
  • check_circleWork history
arrow_downward
4

AI Logs Work Automatically

When done, the agent calls MCP to log progress — duration, what changed, status, blockers. You can ask it to log, or you can run the optional `mcptask_runner` gem that handles the autonomous loop end-to-end.

  • check_circleLog time spent
  • check_circleDescribe what was done
  • check_circleUpdate task status
  • check_circleAdd notes and blockers

Note: Plain MCP access does not log work on its own — the agent has to call the log tool, or the runner has to drive it. Zero-effort logging is a property of a well-instructed agent or the runner, not of MCP itself.

verified

Zero manual context setup. Zero manual logging — when the agent (or runner) does the logging.

The AI Context Problem

content_copy

Manual Context Setup Every Time

Every Claude Code session starts the same:

  • closeCopy task description
  • closePaste into chat
  • closeCopy acceptance criteria
  • closePaste into chat
  • closeExplain the context
  • closeFinally start working
Time Wasted:5-10 minutes per task
Sessions Per Day:10-20
Daily Waste:1-3 hours
schedule

No Automatic Work Logging

AI helped you fix 5 bugs today. But your task system shows nothing. You spend 30 minutes at day's end reconstructing what AI did and logging it manually.

Lost Data:

  • cancelWhat AI actually implemented
  • cancelTime spent per task
  • cancelContext for future reference
extension

Inconsistent Context

Sometimes you copy the full task. Sometimes you summarize. Sometimes you forget related tasks. AI works with incomplete information. Results vary.

Consequences:

  • warningAI misses requirements
  • warningRework needed
  • warningFrustration
search_off

No Audit Trail

Manager asks: "What exactly did the AI help with this sprint?"

You have: scattered chat logs, fuzzy memories, no structured data.

Impact:

  • blockCan't justify AI tool costs
  • blockNo visibility into AI productivity
  • blockNo metrics for improvement

Feature Highlights

Complete Piece Context

What AI Receives:

  • check_circlePiece name and full description
  • check_circleParent epic/story context
  • check_circleAcceptance criteria
  • check_circleRelated pieces and dependencies
  • check_circleAll comments and discussion
  • check_circleAttached files (specs, designs)
  • check_circleWork history

Benefit:

AI understands the full picture, not just a snippet.

Automatic Work Logging

AI Logs (when instructed, or via mcptask_runner):

  • check_circlePiece worked on
  • check_circleDuration (estimated from session)
  • check_circleWhat was implemented
  • check_circleProgress percentage
  • check_circleAny blockers encountered

Human Effort:

Zero — once you tell the agent (or runner) to log, it logs. Plain MCP reads do not log on their own.

Consistent Workflow

Every Session:

  1. 1
    AI reads piece from MCP
  2. 2
    AI works on piece
  3. 3
    AI logs completion via MCP
  4. 4
    Human reviews when convenient

No more:

Copy-paste ceremonies, forgotten logging, inconsistent context.

Clear Attribution

Every Action Tagged:

"Effort logged by Claude-Code-1"

"Status changed by AI Agent"

"Comment added by AI Assistant"

Benefit:

Always know what AI did vs. what human did.

Workflow Examples

Bug Fix Session

close

Before mcptask.online:

  1. 1
    Open Jira, find bug ticket (2 min)
  2. 2
    Copy description, paste to Claude (1 min)
  3. 3
    Copy steps to reproduce (1 min)
  4. 4
    Claude investigates and fixes (15 min)
  5. 5
    Manually log time in Jira (2 min)
  6. 6
    Update ticket status (1 min)
Total:22 minutes
Overhead:7 minutes (32%)
check_circle

With mcptask.online:

  1. 1
    Tell Claude "work on bug #47" (10 sec)
  2. 2
    Claude reads piece via MCP automatically
  3. 3
    Claude investigates and fixes (15 min)
  4. 4
    Claude logs work via MCP automatically
Total:15 minutes
Overhead:10 seconds (<1%)

Feature Implementation

close

Before mcptask.online:

  1. 1
    Read epic and stories in Jira (5 min)
  2. 2
    Copy relevant context to Claude (3 min)
  3. 3
    Discuss approach with Claude (5 min)
  4. 4
    Claude implements feature (30 min)
  5. 5
    Manually document what was done (5 min)
  6. 6
    Log time across multiple tasks (3 min)
Total:51 minutes
Overhead:16 minutes (31%)
check_circle

With mcptask.online:

  1. 1
    Tell Claude "implement story #23" (10 sec)
  2. 2
    Claude reads story and all subtasks via MCP
  3. 3
    Discuss approach with Claude (5 min)
  4. 4
    Claude implements feature (30 min)
  5. 5
    Claude logs work automatically via MCP
Total:35 minutes
Overhead:10 seconds (<1%)

Illustrative Example

Note: The quote and numbers below are illustrative. We use them to show what an MCP-native workflow looks like in practice — we have not collected this testimonial from a named user.

format_quote
Claude Code is much more useful when it can read the full task context, understand the requirements, and log its own work. I haven't copy-pasted a task description in months.
person

Illustrative — full-stack developer

Full-Stack Developer

Using Claude Code with mcptask.online

What the workflow looks like

Context setup time:

5 min → 10 seconds (example)

Work logging time:

30 min/day → 0 (when the agent logs)

AI productivity:

Better context → better output (qualitative)

Audit trail:

Every MCP action is logged with full attribution

Technical Details

smart_toy

Supported AI Tools

Fully Compatible (MCP-native):

  • check_circleClaude Code (Anthropic)
  • check_circleClaude Desktop
  • check_circleCursor, Cline, Continue, and any MCP-compatible assistant

Via REST API:

  • apiCustom AI agents
  • apiTools that do not speak MCP yet
integration_instructions

MCP Capabilities

Read:

  • visibilityPieces (tasks, stories, epics, recurents, projects)
  • visibilityComments and attachments
  • visibilityProject structure
  • visibilityWork history

Write (when the agent decides to):

  • editLog efforts
  • editUpdate status
  • editAdd comments
  • editCreate or complete pieces
security

Security

Access Control:

  • shieldOne MCPTASK_TOKEN per agent or per user
  • shieldScoped permissions (read-only tokens available)
  • shieldProject-level access reflects your account membership
  • shieldToken rate limits per token

Audit:

  • fact_checkEvery MCP action is logged
  • fact_checkFull attribution (which token, when, what)
  • fact_checkExportable history

Pricing for AI Teams

Live prices are loaded from the Tariff model (Settings → Tariffs), so this page never drifts from what checkout charges.

Starter

$15/month
  • check_circle5 projects
  • check_circleUnlimited pieces
  • check_circleMCP server access
  • check_circleGitHub/GitLab webhooks
  • check_circle1 user + 1 AI agent

Best For:

Solo developers using AI daily

Professional

$12/user/month
  • check_circleUnlimited projects
  • check_circleUnlimited team members
  • check_circleAdvanced MCP features
  • check_circleSprint management
  • check_circleAI work approval workflow
  • check_circleAdvanced reporting

Best For:

Teams where everyone uses AI assistants

MCP access for AI agents is free on every plan. You only pay for account users.

FAQ

Q

Where do I find my MCPTASK_TOKEN?

Log in to mcptask.online, open Settings → API Access, and click "Generate token". Copy the long string. Treat it like a password — anyone with it can act as you on mcptask.online. You can revoke it the same way.

Q

What is MCP?

MCP — Model Context Protocol — is the open standard Claude Code (and other MCP-native clients) use to connect to external tools. mcptask.online runs an MCP server at https://mcptask.online/mcp/sse, so any MCP-compatible client can read and write your project data with one config entry.

Q

Do I need to install anything?

For the basic read/write flow: no. A free mcptask.online account, an MCP-compatible client, and a token are enough. For the fully-autonomous daily loop (an agent picking up today's tasks on its own), the optional `mcptask_runner` gem is a one-line addition in a Rails project — see the install guide.

Q

Which AI tools support MCP?

Claude Code, Claude Desktop, Cursor, Cline, Continue, and a growing list of MCP-native clients. If your tool speaks MCP, mcptask.online works with it. If not, use the REST API.

Q

Is the mcptask_runner required?

No. The runner is a convenience wrapper for the fully-autonomous loop in a Rails project. Plain MCP read and write works without it, on any stack.

Q

What is a "piece"?

A piece is the generic name for everything in mcptask.online: tasks, stories, epics, recurents, and projects. The MCP URI is always `mcptask://pieces/{account_code}/{piece_id}` — there are no separate `/tasks/` or `/stories/` paths.

Q

Can AI create pieces?

Yes, if you grant that permission. The MCP write tools let the agent create pieces, subtasks, and comments. You can scope tokens to read-only if you do not want that.

Q

Is AI access logged?

Every MCP action is logged with full attribution — which token, when, what changed. You always know what AI did.

Q

Do I need to change my workflow?

Minimally. You ask the AI to read pieces instead of copy-pasting, and (optionally) to log work when done. Everything else improves automatically.

Give Your AI Assistant the Context It Needs

30-day free Professional trial. MCP server included. Connect Claude Code in five minutes.

check_circleNo credit card. Unlimited MCP actions. Cancel anytime.