Is io.github.sralli/agendum safe to use?

io.github.sralli/agendum has no known CVEs as of the latest MCPpedia security scan. It does not require authentication, so any local process can connect — keep this in mind in shared environments.

How do I install io.github.sralli/agendum?

io.github.sralli/agendum can be installed by cloning its GitHub repository and following the setup instructions in the README.

What AI clients work with io.github.sralli/agendum?

io.github.sralli/agendum is compatible with Claude Desktop, Cursor, Claude Code, and most MCP clients that support stdio transport. It uses stdio transport.

Is io.github.sralli/agendum actively maintained?

io.github.sralli/agendum is actively maintained — last commit was 2 days ago. It has 4 GitHub stars.

io.github.sralli/agendum

AI agent memory: task tracking, scoped work packages, decisions, and cross-session continuity

ActiveNot tested

AI / ML Analytics

★ 4GitHub

28DNo CVEsactive

Quick Install

{
  "mcpServers": {
    "io-github-sralli-agendum": {
      "command": "<see-readme>",
      "args": []
    }
  }
}

Setup guide

No install config available. Check the server's README for setup instructions.

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Add this badge to your README to show your security score and help users find safe servers.

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[![MCPpedia Score](https://mcppedia.org/api/badge/io-github-sralli-agendum)](https://mcppedia.org/s/io-github-sralli-agendum)

Should you use this server?

AI agent memory: task tracking, scoped work packages, decisions, and cross-session continuity

✓

Is it safe?

No package registry to scan.

No authentication — any process on your machine can connect.

License not specified.

✓

Is it maintained?

Last commit 2 days ago. 4 stars.

Will it work with my client?

Transport: stdio. Works with Claude Desktop, Cursor, Claude Code, and most MCP clients.

README

agendum

Project memory and scoping engine for AI coding agents.

AI coding agents are stateless — they forget between sessions, lose decisions, and have no way to scope complex work. agendum is an MCP server that gives any agent (Claude Code, Cursor, Windsurf, Cline, and others) persistent project state, bounded work packages, and cross-session continuity.

Without agendum	With agendum
Agent forgets everything between sessions	Picks up exactly where it left off
No scope — agent modifies random files	Bounded work packages with file lists, acceptance criteria, constraints
Decisions lost — same mistakes repeated	Decisions and patterns persist in searchable memory
No task ordering — agent picks randomly	Dependency graph with auto-unblocking and priority scoring
Learning locked inside one project	Cross-project learnings carry patterns forward

Quick Start

pip install agendum                                    # or: uvx agendum
claude mcp add agendum -- uvx agendum --home serve     # add to Claude Code
# Done. pm_* tools are now available in your agent.

Works with any MCP client — see setup for Cursor, Windsurf, VS Code, and others.

How It Works

flowchart LR
    A["PLAN\nwrite plan file"] --> B["pm_ingest\nboard items + deps"]
    B --> C["pm_next\nwork package + context"]
    C --> D["EXECUTE\nagent implements"]
    D --> E["pm_done\ndecisions + patterns"]
    E -->|next task| C
    E -->|new session| F["pm_status\nresume context"]
    F --> C

Each pm_done records decisions and patterns that enrich future pm_next calls — context compounds across sessions.

Example session:

You: I have a plan file for the API rewrite. Ingest it.

Agent:
  → pm_ingest(project="api-rewrite", plan_file="plan.md")

  Ingested 4 board items from plan:
    item-001: Schema design [high]
    item-002: Resolver layer (depends on item-001)
    item-003: Auth middleware (depends on item-001)
    item-004: Integration tests (depends on item-002, item-003)

You: What should I work on?

Agent:
  → pm_next(project="api-rewrite")

  Work package for item-001 "Schema design":
    Context: project rules, memory from last session
    Scope: Define GraphQL schema types
    Acceptance criteria: Types for User, Product, Order

You: Done with the schema. Here's what I decided...

Agent:
  → pm_done(project="api-rewrite", item_id="item-001",
      decisions="Using code-first with Strawberry",
      patterns="N+1 queries need DataLoader",
      verified=True)

  Marked item-001 as done. Unblocked: item-002, item-003
  > Next: pm_next("api-rewrite") to continue with newly unblocked tasks

14 MCP Tools

Setup & Orientation

Tool	Purpose
`pm_init`	Initialize board directory (optional — auto-initializes on first use)
`pm_project`	Create, list, or get projects
`pm_status`	Dashboard — item counts, recent progress, memory health, suggested next task

Planning & Backlog

Tool	Purpose
`pm_add`	Add an item with type, priority, tags, dependencies, acceptance criteria
`pm_board`	View and filter the project board
`pm_ingest`	Import a Markdown plan file into bounded board items with dependencies

Execution Loop

Tool	Purpose

... View full README on GitHub

Test This Server

No automated test available for this server. Check the GitHub README for setup instructions.

Help improve this page

This server is missing a description. Tools and install config are also missing.If you've used it, help the community.

Add information

Score Breakdown

MCPpedia Score

Click each category to see evidence

28D

Last scored 7h ago

How we score →

Security

3/30

No known vulnerabilities.

○

Known CVEs — No package registry to scan

○

Tool safety — No tools to analyze

○

Tool poisoning — No tools to analyze

○

Injection vectors — No tools to analyze

✓

Tool stability — Tool definitions stable

○

Dependency health — No package to analyze

✗

License — No license specified

○

Authentication — No authentication required

○

Repository — active repo

CVEs checked daily via OSV.dev. Score algorithm is open source.

Reviews

Have you used this server?

Share your experience — it helps other developers decide.

How easy was setup?Did it work reliably?How was the documentation?

Frequently Asked Questions

Is io.github.sralli/agendum safe to use?: io.github.sralli/agendum has no known CVEs as of the latest MCPpedia security scan. It does not require authentication, so any local process can connect — keep this in mind in shared environments.
How do I install io.github.sralli/agendum?: io.github.sralli/agendum can be installed by cloning its GitHub repository and following the setup instructions in the README.
What AI clients work with io.github.sralli/agendum?: io.github.sralli/agendum is compatible with Claude Desktop, Cursor, Claude Code, and most MCP clients that support stdio transport. It uses stdio transport.
Is io.github.sralli/agendum actively maintained?: io.github.sralli/agendum is actively maintained — last commit was 2 days ago. It has 4 GitHub stars.

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Discussion

Start a conversation

Ask a question, share a tip, or report an issue.

Has anyone used this with Cursor?How do you handle auth?Any alternatives?

agendum

Project memory and scoping engine for AI coding agents.

Without agendum	With agendum
Agent forgets everything between sessions	Picks up exactly where it left off
No scope — agent modifies random files	Bounded work packages with file lists, acceptance criteria, constraints
Decisions lost — same mistakes repeated	Decisions and patterns persist in searchable memory
No task ordering — agent picks randomly	Dependency graph with auto-unblocking and priority scoring
Learning locked inside one project	Cross-project learnings carry patterns forward

Quick Start

pip install agendum                                    # or: uvx agendum
claude mcp add agendum -- uvx agendum --home serve     # add to Claude Code
# Done. pm_* tools are now available in your agent.

Works with any MCP client — see setup for Cursor, Windsurf, VS Code, and others.

How It Works

flowchart LR
    A["PLAN\nwrite plan file"] --> B["pm_ingest\nboard items + deps"]
    B --> C["pm_next\nwork package + context"]
    C --> D["EXECUTE\nagent implements"]
    D --> E["pm_done\ndecisions + patterns"]
    E -->|next task| C
    E -->|new session| F["pm_status\nresume context"]
    F --> C

Each pm_done records decisions and patterns that enrich future pm_next calls — context compounds across sessions.

Example session:

You: I have a plan file for the API rewrite. Ingest it.

Agent:
  → pm_ingest(project="api-rewrite", plan_file="plan.md")

  Ingested 4 board items from plan:
    item-001: Schema design [high]
    item-002: Resolver layer (depends on item-001)
    item-003: Auth middleware (depends on item-001)
    item-004: Integration tests (depends on item-002, item-003)

You: What should I work on?

Agent:
  → pm_next(project="api-rewrite")

  Work package for item-001 "Schema design":
    Context: project rules, memory from last session
    Scope: Define GraphQL schema types
    Acceptance criteria: Types for User, Product, Order

You: Done with the schema. Here's what I decided...

Agent:
  → pm_done(project="api-rewrite", item_id="item-001",
      decisions="Using code-first with Strawberry",
      patterns="N+1 queries need DataLoader",
      verified=True)

  Marked item-001 as done. Unblocked: item-002, item-003
  > Next: pm_next("api-rewrite") to continue with newly unblocked tasks

14 MCP Tools

Setup & Orientation

Tool	Purpose
`pm_init`	Initialize board directory (optional — auto-initializes on first use)
`pm_project`	Create, list, or get projects
`pm_status`	Dashboard — item counts, recent progress, memory health, suggested next task

Planning & Backlog

Tool	Purpose
`pm_add`	Add an item with type, priority, tags, dependencies, acceptance criteria
`pm_board`	View and filter the project board
`pm_ingest`	Import a Markdown plan file into bounded board items with dependencies

Execution Loop

Tool	Purpose

... View full README on GitHub