Skip to main content

Stdio MCP server for searching, checkpointing, and forking coding-agent chat history across tools.

Project description

chat-mother-forker

A small stdio MCP server that lets a coding agent search, checkpoint, and fork chat history — to subagents, different tools, different workspaces, and across time. Chat context from any parent (a.k.a. "mother") can be forked into as many child chats as needed, instead of relying on lossy summarization or copy-pasting each time.

Setup

Add it to your MCP client's config, e.g.:

{
  "mcpServers": {
    "chat-mother-forker": {
      "command": "uvx",
      "args": ["chat-mother-forker"],
      "autoApprove": ["*"]
    }
  }
}

Why

Coding agents constantly lose context that already exists on disk, just in the wrong conversation:

  • Cross-tool continuation. Started planning in one tool, want to keep going in another? chat_fork pulls the original conversation straight into the new one instead of you re-explaining everything.
  • Cross-workspace continuation, same tool. A decision made in workspace A's chat is invisible to a session in workspace B, even though it's the same tool and the same person. Every provider here scans all of a tool's stored conversations, not just the current workspace's, so this falls out for free.
  • Cheap context handoff to subagents. A hand-written summary for a subagent is expensive to write and inherently lossy. Handing it a chat_fork search string instead costs a couple of tokens and gets the real transcript.
  • Ad hoc recall. "Apply what we learned in yesterday's chat about X" only works if the agent can actually go find yesterday's chat.

Tools

chat_search(search=None)

Lists the 50 most recent conversations across every configured provider (see Status), merged and sorted by recency. If search is given, only conversations containing it as a substring are returned — matched against the conversation id, any checkpoint slug/uuid found in the conversation, or the raw transcript text.

For each matching conversation, returns the last-modified date, a provider:conversation_id identifier, the project/workspace directory name (when the provider could determine one — useful for telling apart conversations from different projects), the first ~128 characters of the initial user prompt, and every checkpoint slug/uuid found anywhere in it. When search is given, results also show which field(s) it matched, plus a hit count and ~128 characters of context around the first and last transcript match.

chat_checkpoint(slug)

Drops a named landmark in the current conversation so it can be found and sliced out later. Returns a line of the form:

CHAT CHECKPOINT UUID=<random uuid> SLUG=<slug>

slug is a short label up to 256 characters; it doesn't need to be unique. Pass the returned UUID to chat_fork when you need to target this exact spot later, e.g. when handing off to a subagent.

chat_fork(search, start_checkpoint=None, end_checkpoint=None)

Finds the newest conversation matching search (a checkpoint slug, a checkpoint or conversation uuid, or any substring of the transcript) and returns it as an annotated, truncated transcript — one you can hand directly to yourself, another agent, or a subagent as background context.

Matching is tiered — a match on conversation id or checkpoint always beats a match that's merely somewhere in the transcript text, regardless of recency. Within the same tier, the newest conversation wins. If you want to target one conversation unambiguously, search by its provider:id or a checkpoint UUID.

If start_checkpoint and/or end_checkpoint are given, only the message range between them (inclusive) is returned, falling back to the whole conversation on either side if a checkpoint is omitted or not found.

The response always ends with a footer noting it's historical reference material and not an instruction to act on, plus the exact provider:conversation_id in case you need to fork or slice it again.

How a conversation is rendered

Messages are grouped into turns — a run of consecutive user messages, or a run of consecutive non-user messages (assistant text, tool calls, tool results). Each turn gets a ## USER / ## ASSISTANT header, with individual messages labeled (USER, ASSISTANT, TOOL_CALL: <name>, TOOL_RESULT) and quoted as markdown.

To keep responses a manageable size, both an individual turn's text (2000 characters) and the number of turns in a conversation (50) are capped — when over the limit, the middle is dropped in favor of a [N truncated] marker, on the idea that the beginning (intent) and end (conclusion) matter more than the middle.

Status

Three providers are implemented, one per tool:

  • kiro_cli — Kiro CLI (~/.kiro/sessions/cli/*.jsonl)
  • kiro_ide — Kiro IDE (execution logs under the extension's globalStorage directory)
  • claude_code — Claude Code CLI (~/.claude/projects/<encoded-workspace-path>/*.jsonl)

License

MIT, see LICENSE.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

chat_mother_forker-0.1.5.tar.gz (120.3 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

chat_mother_forker-0.1.5-py3-none-any.whl (43.8 kB view details)

Uploaded Python 3

File details

Details for the file chat_mother_forker-0.1.5.tar.gz.

File metadata

  • Download URL: chat_mother_forker-0.1.5.tar.gz
  • Upload date:
  • Size: 120.3 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.13.14

File hashes

Hashes for chat_mother_forker-0.1.5.tar.gz
Algorithm Hash digest
SHA256 9967e74017a33bc0394ac46f7d66df291181d7717ee956a670827d4713d2c898
MD5 91d202fabd168f156809719892729436
BLAKE2b-256 e313cac900ba4017d863493d93c3db9baf496407b5bba81657805473c8120481

See more details on using hashes here.

File details

Details for the file chat_mother_forker-0.1.5-py3-none-any.whl.

File metadata

File hashes

Hashes for chat_mother_forker-0.1.5-py3-none-any.whl
Algorithm Hash digest
SHA256 94ca2324c5f97625929d29187f06841086a0d485c38335abc880788ee04ae3b6
MD5 4aac5c586a2187400413d8fe2eaaa5b9
BLAKE2b-256 025a79cf0b921549ace808cc59c7c2568d00396267f989636f1173adb79d8d66

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page