Agent Tasks vs PAPI: Persistent Adaptive Planning Intelligence
A side-by-side look at the Agent Tasks and PAPI: Persistent Adaptive Planning Intelligence MCP servers: transport, auth, tools, quality signals, and the config to install either one in Claude, Cursor, Windsurf or VS Code.
Verdict: choose Agent Tasks for coding agents work as a local process; choose PAPI: Persistent Adaptive Planning Intelligence for coding agents over a hosted endpoint. You can also run both side by side in one mcpServers block.
Which MCP server should you choose?
Choose Agent Tasks when
- You need dedicated capabilities in the Coding Agents domain.
- You prefer a local stdio subprocess transport.
- You have the keys it needs:
AGENT_TASKS_DB, AGENT_TASKS_PORT, AGENT_TASKS_HOST, AGENT_TASKS_INSTRUCTIONS, AGENT_COMM_URL, AGENT_KNOWLEDGE_URL. - You want tools like
task_create, task_get, task_list.
Choose PAPI: Persistent Adaptive Planning Intelligence when
- You need dedicated capabilities in the Coding Agents domain.
- You prefer a remote HTTP/SSE transport.
- You want tools like
orient, setup, plan.
Feature & specification comparison
Rows where the two servers differ are marked.
| Spec | ||
|---|---|---|
| Category | Coding Agents | Coding Agents |
| Quality signal | 64/100 · Good | 55/100 · Good |
| Install command | Confirmed | Confirmed |
| Transport | Local subprocess (stdio) | Remote HTTP/SSE |
| Auth | Not declared | Not declared |
| Pricing | Not declared | Not declared |
| Required env vars | AGENT_TASKS_DBAGENT_TASKS_PORTAGENT_TASKS_HOSTAGENT_TASKS_INSTRUCTIONSAGENT_COMM_URLAGENT_KNOWLEDGE_URL | None listed |
| Verified owner | Community listing | Community listing |
| GitHub stars | 22 | 10 |
| Directory activity | 1 views 0 installs 0 upvotes | 1 views 0 installs 0 upvotes |
| Tools | 8 | 15 |
| Full listing | View Agent Tasks | View PAPI: Persistent Adaptive Planning Intelligence |
Tools & capabilities
Agent Tasks tools 8
task_createCreate a pipeline task. Tasks start in "backlog" and move through stages: backlog → spec → plan → implement → test → review → done. Use parent_id to create subtasks under an existing task.task_getGet a single task by ID. By default returns task with artifact count, comment count, dependencies, and collaborators. Use "include" to inline full subtasks, artifacts (filterable by stage), comments (with limit), or transitive_deps (full upstream + downstream closure of the dependency graph in one call — answers questions like "what depends on this task transitively?" and "what is its critical path depth?" without forcing the caller to BFS the graph).task_listList, search, or pick tasks. Without params: returns all tasks. With filters: narrow by status/stage/project/assignee. With "query": full-text search across titles and descriptions. With "next": true: returns the single highest-priority unassigned task with all dependencies met (uses affinity scoring when agent is provided).task_updateUpdate task metadata — title, description, priority, tags, project, assignment, or dependencies. Does not change stage or status (use task_stage for that).task_deleteDelete a task and all its artifacts, comments, and dependencies (cascading delete). Cannot be undone.task_stageMove a task through its lifecycle. Actions: "claim" → assign to you and advance from backlog to spec, "assign" → assign to you WITHOUT advancing the stage (use for custom pipelines where the first stage is itself a do-work stage), "advance" → next stage (or jump to a specific one), "regress" → earlier stage (requires reason), "complete" → marks done with result, "fail" → marks failed with error, "cancel" → cancels with reason.task_artifactAttach artifacts or comments to a task. Types: "general" → document (spec, test-results, review-notes), "decision" → structured decision record (chose/over/because), "learning" → insight that auto-propagates to parent and sibling tasks on completion, "comment" → discussion comment (supports threading via parent_comment_id, satisfies stage-gate require_comment checks).task_configConfiguration and admin. Actions: "session" → set agent identity (call this first), "pipeline" → get/set pipeline stages and gate config for a project, "cleanup" → purge old completed tasks, "rules" → generate IDE rule files (.mdc or CLAUDE.md).
PAPI: Persistent Adaptive Planning Intelligence tools 15
orientone call returns the current cycle, what's in flight, and the recommended next action. Run it at the start of every session.setupscaffold PAPI onto a new project.planbreak goals into a cycle of right-sized tasks, each with a build handoff your assistant can execute directly.build_listlist the current cycle's tasks and their handoffs.build_executestart a task (creates a branch and handoff) and complete it (records the build report).tutorial_modeturn on short explanations and one clear next step in every reply. Handy for your first few cycles.board_viewread the project board and any task.board_editchange a task's status, cycle, priority, or notes.ad_hocrecord quick work done outside the cycle so it shows in project history.ideacapture a feature, bug, or research note into the backlog.bugfile a bug against the board.strategy_reviewstep back every few cycles to check direction, not just velocity.- +3 more on the listing page
Ready-to-paste client configs
Paste either block (or both) into your client config, e.g. claude_desktop_config.json or ~/.cursor/mcp.json. We only show a config when the install command is confirmed.
Agent Tasks
{
"mcpServers": {
"agent-tasks": {
"command": "npx",
"args": [
"agent-tasks"
],
"env": {
"AGENT_TASKS_DB": "YOUR_AGENT_TASKS_DB_HERE",
"AGENT_TASKS_PORT": "YOUR_AGENT_TASKS_PORT_HERE",
"AGENT_TASKS_HOST": "YOUR_AGENT_TASKS_HOST_HERE",
"AGENT_TASKS_INSTRUCTIONS": "YOUR_AGENT_TASKS_INSTRUCTIONS_HERE",
"AGENT_COMM_URL": "YOUR_AGENT_COMM_URL_HERE",
"AGENT_KNOWLEDGE_URL": "YOUR_AGENT_KNOWLEDGE_URL_HERE"
}
}
}
}
PAPI: Persistent Adaptive Planning Intelligence
{
"mcpServers": {
"papi-persistent-adaptive-planning-intelligence": {
"url": "https://mcp.getpapi.ai/mcp"
}
}
}
Frequently asked questions
Agent Tasks is categorized under Coding Agents and uses a local stdio subprocess. PAPI: Persistent Adaptive Planning Intelligence belongs to Coding Agents and uses a remote HTTP/SSE transport. Pick Agent Tasks for coding agents work and PAPI: Persistent Adaptive Planning Intelligence for coding agents.
Copy the mcpServers block from the config section on this page into your client's config (for example claude_desktop_config.json or ~/.cursor/mcp.json), then fully restart the client.
Agent Tasks has no declared pricing — check the project's own site. PAPI: Persistent Adaptive Planning Intelligence has no declared pricing — check the project's own site. If a server needs environment variables such as API keys, define them under the env key in your client's MCP config.
Yes. MCP clients support multiple servers: add agent-tasks and papi-persistent-adaptive-planning-intelligence as separate keys in the same mcpServers object, and your assistant will call tools from either one.
On AllMCPs, Agent Tasks has a quality signal of 64/100 (Good) with 1 views, 0 installs and 22 GitHub stars. PAPI: Persistent Adaptive Planning Intelligence has 55/100 (Good) with 1 views, 0 installs and 10 GitHub stars.