Taskive MCP vs AgentRecall

A side-by-side look at the Taskive MCP and AgentRecall MCP servers: transport, auth, tools, quality signals, and the config to install either one in Claude, Cursor, Windsurf or VS Code.

T
Taskive MCPKnowledge & Memory · Remote HTTP/SSE
AI-native project management + agent memory: tasks, sprints, risk, burnout, knowledge search.42FairAuth: not declared
AgentRecall logoAgentRecallKnowledge & Memory · Local subprocess (stdio)Persistent, compounding memory for AI agents across sessions. Uses the Intelligent Distance Protocol to surface the most contextually relevant past memories. Five tools: sessionstart, remember, recall, check, sessionend. npx agent-recall-mcp68GreatAuth: no auth required

Verdict: choose Taskive MCP for knowledge & memory work over a hosted endpoint; choose AgentRecall for knowledge & memory as a local process. You can also run both side by side in one mcpServers block.

Which MCP server should you choose?

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Choose Taskive MCP when

  • You need dedicated capabilities in the Knowledge & Memory domain.
  • You prefer a remote HTTP/SSE transport.
Explore Taskive MCP

AgentRecall logoChoose AgentRecall when

  • You need dedicated capabilities in the Knowledge & Memory domain.
  • You prefer a local stdio subprocess transport.
  • Its auth model fits you: No auth required.
  • You want tools like session_start, remember, recall.
Explore AgentRecall

Feature & specification comparison

Rows where the two servers differ are marked.

Spec
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Taskive MCP
AgentRecall logoAgentRecall
CategoryKnowledge & MemoryKnowledge & Memory
Quality signal42/100 · Fair68/100 · Great
Install commandConfirmedConfirmed
TransportRemote HTTP/SSELocal subprocess (stdio)
AuthNot declaredNo auth required
PricingNot declaredFree
Required env varsNone listedNone listed
Verified ownerCommunity listingCommunity listing
GitHub stars—371
Directory activity 1 views 0 installs 0 upvotes 5 views 0 installs 0 upvotes
ToolsNot listed5

Tools & capabilities

Taskive MCP tools

No tool names declared yet. Check the project README from the listing page.

AgentRecall tools 5

  • session_startLoads corrections, insights and `watch_for` warnings for the project
  • rememberSaves one fact; auto-routes to journal / palace room / corrections / awareness
  • recallRanked search across every store (RRF), with `useful` feedback that re-weights future ranking
  • checkMatches the plan against known corrections; returns `verdict: "blocked"` when one overrides it
  • session_endWrites the journal, promotes insights, records trajectory, optionally opens/closes a pipeline phase

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.

Taskive MCP

mcpServers (Claude Desktop / Cursor)
{
  "mcpServers": {
    "mcp-32": {
      "url": "https://taskive.ai/mcp"
    }
  }
}

AgentRecall

mcpServers (Claude Desktop / Cursor)
{
  "mcpServers": {
    "goldentrii-agentrecall": {
      "command": "npx",
      "args": [
        "-y",
        "agent-recall-mcp"
      ]
    }
  }
}

Frequently asked questions

Taskive MCP is categorized under Knowledge & Memory and uses a remote HTTP/SSE transport. AgentRecall belongs to Knowledge & Memory and uses a local stdio subprocess. Pick Taskive MCP for knowledge & memory work and AgentRecall for knowledge & memory.

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.

Taskive MCP has no declared pricing — check the project's own site. AgentRecall is listed as free. Auth: no auth required. 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 mcp-32 and goldentrii-agentrecall as separate keys in the same mcpServers object, and your assistant will call tools from either one.

On AllMCPs, Taskive MCP has a quality signal of 42/100 (Fair) with 1 views, 0 installs and 0 GitHub stars. AgentRecall has 68/100 (Great) with 5 views, 0 installs and 371 GitHub stars.