bantamkit
bantamkit is a memory-focused MCP server that gives coding agents persistent memory plus validation, planning, auditing, and shift-work orchestration tools.
Persistent memory: save durable facts (user preferences, feedback, project decisions, references), recall them by query/file/outcome, and tag lessons as errors or successes.
Memory maintenance: compact the stalest facts to stay within budget, consolidate same-named facts across project/profile layers, and attach likely-prompt queries for better recall.
JSON validation: validate model output against JSON Schema drafts and return actionable feedback.
Shift-work orchestration: draft checkpoints and briefs, inspect job status, plan dependency waves, clock units in/out with handoffs/accounting, add follow-up work, manage agreements, and enforce budgets.
Work planning: batch arbitrary dependency graphs into parallel waves with priority ordering and file-touch conflict handling.
Skill auditing: measure skill-catalogue byte costs and detect trigger collisions, malformed frontmatter, unused skills, and omissions.
Token ledger: compute actual session costs from host transcripts, including cache/input/output totals and optional model pricing.
Server diagnostics: check bantamkit health, bound memory store, build fingerprint, and whether two installs are the same build.
Provides persistent memory tools to GitHub Copilot in VS Code, enabling the agent to store and retrieve project facts and preferences across sessions.
bantamkit โ memory MCP server for Claude Code, Cursor, VS Code Copilot and Claude Desktop
Give your coding agent a memory that outlasts the session. bantamkit is an MCP server: the agent saves what is worth keeping โ a preference you stated, a decision, the fix that worked โ and recalls it next session instead of asking you again. It works with Claude Code, Claude Desktop, Cursor, GitHub Copilot in VS Code and any stdio MCP client. Install it once from npm or PyPI; after that it starts offline.
๐ Quick start
npx -y bantamkit-mcp@latest --install claude # or claude-desktop, copilot, cursorThen restart the host (in Claude Code, open a new session) and ask the agent to call
bantamkit_status. Prefer Python? pip install "bantamkit[mcp]" into a venv you keep, then
bantamkit-mcp --install claude. Every route and host is below.
Related MCP server: mnemo
โจ What you get
Memory that persists. Facts are plain Markdown files under
.bantamkit/memory, deduped and held to a byte budget.memory_saveandmemory_recalldo the work;memory_compactandmemory_dreamkeep the store tidy.Layered by default. Recall reads the project store, any stores you grant in
.bantamkit/config.yaml, and your profile store~/.bantamkit/memory. Saves go to the project store.One command per host.
--installwrites the entry for Claude Code, Claude Desktop, Cursor or VS Code Copilot, backs up any file it edits, and never prompts.Offline after one install. Both install routes record the server by absolute path, so no launch needs the network or your shell's PATH.
Claude Code hooks, if you want them.
--install-hooksputs the memory index in front of each new session, surfaces matching facts on each prompt, and nudges a save at the end of a long session that saved nothing (docs/hooks.md).More than memory. JSON Schema validation, a token ledger read off the host's transcripts, a skill-catalogue audit, and shift-work tools that plan and hand off multi-step jobs โ the fifteen tools.
Two runtimes, one store. Pure Node on npm and Python on PyPI read and write the same files, and every surface is compared between both runtimes by a conformance suite (
node tools/conformance/run.mjs --all).A Python library for small models, too.
pip install bantamkitlifts small-model agents (~1Bโ8B, any OpenAI-compatible endpoint): malformed output, unreviewed answers, forgotten context and runaway loops are absorbed by code, and a bundled eval suite measures the uplift.
๐ Contents
Topic | What you'll find |
Three ways to set it up โ pick one | |
One command; later launches need no network | |
The online form, and why it hangs offline | |
A venv you keep; a wheelhouse for no-network machines | |
The same five steps for every host | |
| |
| |
| |
| |
Codex and any stdio host | |
Every flag and environment variable, with its default | |
| |
Timeouts, Connection closed, refusals, the wrong store | |
15 tools, three prompts, two resource templates | |
| |
Which components to attach, measured on four models | |
Every page under | |
What lives where |
๐ฅ Install the MCP server
Two implementations of one server โ Node on npm, Python on PyPI โ share one memory store on disk. To just try it:
npx -y bantamkit-mcp --assets-root # Node, no Python required
pipx run --spec "bantamkit[mcp]" bantamkit-mcp --assets-root # PythonA host launches the server every session, so what matters is whether each launch needs the network. Pick one of the three ways below.
Install once, run offline (recommended)
npx -y bantamkit-mcp@latest --install claude # or claude-desktop, copilot, cursorThis runs npm install --prefix ~/.bantamkit/mcp once and records
<the node that ran it> ~/.bantamkit/mcp/node_modules/bantamkit-mcp/dist/cli.js, both absolute.
No later launch needs npx, the registry or your PATH, and it never downgrades a newer kept
install.
The recorded node is one version of node. Switch away from it and re-run with
--force.Replacing an old
npxentry needs--force(Claude Code:claude mcp removefirst).Installing into another host while offline: plain
npx -yhangs before bantamkit starts. Usenpx --offline -y bantamkit-mcp@<version> --install <host>, or the kept install's own CLI:<node> ~/.bantamkit/mcp/node_modules/bantamkit-mcp/dist/cli.js --install <host>.
Measurements: docs/install.md โ MCP server: measured install detail.
Run with npx at every launch
{"mcpServers": {"bantamkit": {"command": "npx", "args": ["-y", "bantamkit-mcp"]}}}This asks the registry on every launch. Offline it fails silently: with a warm cache,
npx -y bantamkit-mcp, npx -y bantamkit-mcp@latest and a pinned version each hung to the 45 s
bound with 0 bytes on stdout, and the host reports only its own handshake timeout.
npx also caches what latest resolved to, so add @latest or pin a version
(why).
Install with Python (PyPI)
pip install "bantamkit[mcp]" # into a venv you keep, not a pipx run
bantamkit-mcp --install claude # or claude-desktop, copilot, cursor--install records the venv's console script by absolute path with "args": [], so no launch
needs the network. With no network at all, pip download a wheelhouse on a machine with the
same OS, CPU architecture and Python version, then pip install --no-index --find-links:
steps.
๐ Connect to a host
--install <host> (claude, claude-desktop, copilot, cursor) writes the entry. It backs
the file up first, refuses an entry that differs unless you pass --force, and never prompts โ
so it behaves the same in a terminal, in CI and inside another agent. With Python, run
bantamkit-mcp --install <host> from your venv instead of the npx line.
Each host below has the same steps: 1 command, 2 file, 3 entry, 4 confirm,
5 undo or re-run. The entry's command and args depend on the install route:
Route |
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npm, install once |
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Python venv |
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npx every launch |
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For JSON hosts, a quick check of the recorded command: run it in a terminal โ with nothing on
stdin it prints usage: bantamkit-mcp โฆ and exits 0.
Connect to Claude Code
Command:
npx -y bantamkit-mcp@latest --install claudeFile: none edited directly โ it runs
claude mcp add bantamkit -s user -- <command> <args>(user scope, every project), because~/.claude.jsonalso holds host state that is not MCP config.Entry: printed as
ran : claude mcp add bantamkit -s user -- <node> <cli.js>.Confirm:
claude mcp listprintsbantamkit: <node> <cli.js> - โ Connected(Claude Code 2.1.272; run it outside a project whose.mcp.jsonalso names bantamkit, or it prints[Conflicting scopes]). In a session, ask the agent to callbantamkit_status.Undo / re-run:
--forcedoes not reach Claude Code, and a second add fails withMCP server bantamkit already exists in user config. Remove first:claude mcp remove bantamkit -s user npx -y bantamkit-mcp@latest --install claude
Connect to Claude Desktop
Command:
npx -y bantamkit-mcp@latest --install claude-desktopFile: macOS
~/Library/Application Support/Claude/claude_desktop_config.jsonยท Windows%APPDATA%\Claude\claude_desktop_config.jsonยท Linux~/.config/Claude/claude_desktop_config.jsonEntry (key
mcpServers):{"mcpServers": {"bantamkit": {"command": "/absolute/path/to/node", "args": ["/Users/you/.bantamkit/mcp/node_modules/bantamkit-mcp/dist/cli.js"]}}}Confirm: it prints
installed bantamkit into claude-desktopwith file, key and command. Fully quit and reopen Claude Desktop, then ask it to callbantamkit_status.Undo / re-run: there is no uninstall flag โ delete the
bantamkitentry or restoreclaude_desktop_config.json.backup-<date>. A matching re-run printsbantamkit is already installed in claude-desktop and matches; a differing entry needs--force.
Connect to Cursor
Command:
npx -y bantamkit-mcp@latest --install cursorFile:
~/.cursor/mcp.jsonon every OS (%USERPROFILE%\.cursor\mcp.jsonon Windows);.cursor/mcp.jsonfor one project, by hand.Entry (key
mcpServers):{"mcpServers": {"bantamkit": {"command": "/absolute/path/to/node", "args": ["/Users/you/.bantamkit/mcp/node_modules/bantamkit-mcp/dist/cli.js"]}}}Confirm: it prints
installed bantamkit into cursorandkey : mcpServers. Restart Cursor and ask the agent to callbantamkit_status.Undo / re-run: delete the entry or restore
mcp.json.backup-<date>. To replace a differing entry, such as an oldnpxone:npx -y bantamkit-mcp@latest --install cursor --force
Connect to VS Code (GitHub Copilot)
Command:
npx -y bantamkit-mcp@latest --install copilotFile: macOS
~/Library/Application Support/Code/User/mcp.jsonยท Windows%APPDATA%\Code\User\mcp.jsonยท Linux~/.config/Code/User/mcp.json;.vscode/mcp.jsonfor one workspace, or MCP: Open User Configuration, by hand.Entry โ the key is
servers, notmcpServers, plus"type": "stdio". This is the detail that catches people out:{"servers": {"bantamkit": {"type": "stdio", "command": "/absolute/path/to/node", "args": ["/Users/you/.bantamkit/mcp/node_modules/bantamkit-mcp/dist/cli.js"]}}}Confirm: it prints
installed bantamkit into copilotandkey : servers. Restart VS Code and ask Copilot to callbantamkit_status.Undo / re-run: delete the entry or restore
mcp.json.backup-<date>;--forcereplaces a differing entry.
Connect other MCP clients
Any stdio MCP client runs the same command/args; nothing is host-specific. Codex and a
generic JSON config: docs/mcp.md โ Client setup. Hand-written forms
for every host: npm README ยท
Python README.
๐ง Configuration
Flags go in the entry's args; environment variables in its env block (or
claude mcp add -e NAME=value). Both runtimes print the same --help.
Setting | What it does | Default | Example |
| Pins the store to one absolute path; a missing or relative path refuses at startup | unset: nearest existing |
|
| One store, layering off; outranks | off (layered) |
|
| Where store discovery starts; not with | cwd |
|
Layered memory | Recall reads the project store, stores granted in | on | |
| Default recall budget |
|
|
| Memory index byte budget |
|
|
| Logs tool outcomes as JSONL | off; |
|
| The daily Retention job: deletes session read ledgers older than 7 days, folds log lines older than 30 days, archives closed | on; |
|
| The daily skill audit (#205): the same once-a-day child audits your skills and writes | on; |
|
| Skills from experience (#206): lets the daily run INSTALL a skill it mined (twice the bar, a gated draft, one a day, five live, 30 days of probation) and make the low-risk EDITS to installed skills (keyword widening, a Pitfalls line, a drafted description) and PROMOTE a skill used 3 times in 30 days to a native link, on its own. Off, the same work waits for you as proposals in the | off in 0.37.0; |
|
| Skills from experience (#301): lets recall append ONE | off until the #301 ship bar is measured; |
|
| Reply-native (#292), opt-in: the hook tells the model to think in English and reply in your language (Thai, Japanese). | off |
|
| Reply-native (#292): a two-letter code ( | unset |
|
| Skills from experience (#206): an OpenAI-compatible endpoint ( | unset: no endpoint |
|
| Skills from experience (#206): the catalogue budget in bytes (the summed |
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| Where the derived recall indexes live (hot and cold); deleting them loses nothing |
|
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| Your own asset pack (skills, rubrics, schemas) | the pack inside the package |
|
| Where | macOS |
|
| Price table for the token ledger |
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| Where a checkout launcher resolved its halves โ only | โ |
|
Don't add --store by reflex: the layering is most of the value. One-shot commands that print
and exit: --install {claude,claude-desktop,copilot,cursor} (with --force),
--install-hooks / --remove-hooks (with --yes), --hook, --update,
--assets-root, --mcp-report, --statusline.
More: store binding ยท
pinning ยท
event log ยท prices.
๐ Update
npx -y bantamkit-mcp@latest --update # patches the kept install at ~/.bantamkit/mcp
npx -y bantamkit-mcp@latest --assets-root # npx CACHES; without @latest you get an old resolve
npm i -g bantamkit-mcp@latest # global npm install
pip install -U "bantamkit[mcp]" # PyPI (pipx upgrade bantamkit ยท uv tool upgrade bantamkit)
git pull && npm ci --prefix runtime-ts && npm run build --prefix runtime-ts # a checkout: dist/ is build output--update (check the package index and update this install if it differs, then exit) patches a
registry install or a kept install to exactly the version it compared, and reports success only
after the install says so; any other shape it refuses with exit 1 and names the manual route.
It is the only network access here, and only when you type it.
Then restart the server in the host (/mcp โ reconnect in Claude Code; a full restart of
Claude Desktop) โ a running server keeps the code it started with. bantamkit_status prints the
version and the build_id of the code answering you; a new version with an old build_id
means an old process. Per-install table: npm README โ Updating.
๐ฉบ Troubleshooting
Symptom | Fix |
The host times out; the server never answers | The entry uses |
| A GUI host does not read your shell rc, so |
Connection closed at startup | Older builds crashed when a GUI host started them in |
| Re-run with |
|
|
The server stopped launching after a node change |
|
Recall finds nothing, or the wrong store | Set |
๐งฐ What it serves
Both servers serve the same 16 tools, three prompts and two resource templates
(bantamkit://skills/{name}, bantamkit://rubrics/{name}). The prompts are what a person invokes from
the host's slash menu: bantamkit_status (the status report), skill_audit (#205: gathers the
skills this host loads, audits them, and inserts the report plus a proposed settings.json change
that is never applied for you; in Claude Code it is /mcp__bantamkit__skill_audit) and
skill_propose (#206: lists the skills bantamkit proposes from your repeated, successful work and
the ones it installed, and approves, rejects, undoes, promotes or demotes one; in Claude Code it is
/mcp__bantamkit__skill_propose, see docs/skills.md):
Tool | What it does |
| store one durable fact, deduped and budgeted |
| retrieve facts matching a query |
| archive the stalest facts to fit the index budget |
| consolidate facts the project and profile layers hold under the same name |
| list the facts that have no likely-prompt queries yet, and store the queries written for them; recall matches a prompt against them |
| validate a document against a JSON Schema |
| audit a skill catalogue for findings |
| open a unit of work and get its brief |
| close a unit with status and accounting |
| read-only progress summary of a checkpoint |
| read-only: which units of a checkpoint its |
| turn any |
| what a session cost, read off the host's transcripts |
| is bantamkit working, and which build: version, |
| report the fingerprint of the source on disk, not the executing code โ useful when a machine carries two installs under one name |
๐ Use it as a Python library
pip install bantamkitEditable install and runtime differences: docs/install.md,
docs/porting.md. Copy-paste start: examples/.
from bantamkit import Agent, CritiqueGate, Memory, OpenAICompatible, Tool, ToolDef
client = OpenAICompatible(base_url="http://localhost:11434/v1", model="qwen2.5:7b-instruct")
price_lookup = ToolDef(
tool=Tool(
name="price_lookup",
description="Get the unit price of an item",
parameters={
"type": "object",
"required": ["item"],
"properties": {"item": {"type": "string"}},
},
),
handler=lambda item: f"{item} price: 25",
)
agent = Agent(client=client, tools=[price_lookup]).use(
Memory(store="./.bantam-memory"),
CritiqueGate("task-completion"),
)
result = agent.run("What does a widget cost? Remember it for next time.")
print(result.output, result.usage.total)๐ Recommended defaults
Measured on the bundled 22-task suite across four models, 528 runs each. Numbers and caveats: docs/usage.md โ Recommended defaults.
Always attach
Memoryโ the biggest single mover on every model measured.Skip the blind
CritiqueGateon small instruct models โ โค6 passes at 2โ3.5ร bare's tokens.Don't credit a rubric edit without a bar โ the non-fragile anchor set is the floor.
Use
structured()for schema'd output โ zero schema retries in 2,112 runs.Attach
FileAccessGraphwhen the agent reads files, on ~4B-class models.full(memory + schema + grounded critique) is a 4b-reference result โ 66/66 there only.Prefer
GroundedCritiqueGateoverCritiqueGatewhen the agent has tools โ same 4b scope.
๐ Documentation
Page | Covers |
Requirements, editable install, endpoints, | |
The 5-layer model and the no-mixing rule | |
Client, agent, tools, components, | |
Store layout, the four ops, dedupe and budget, layers, the operator CLI ( | |
The read ledger and the | |
Running the suite, the config matrix, current results | |
| |
The JSONL outcome log, its vocabulary and the 1 MiB cap | |
| |
| |
| |
| |
| |
| |
| |
Checkpoint contract and clock-in/clock-out driver (experimental) | |
The Node port, | |
The two-runtime diff harness and what a | |
One TypeScript source, npm and PyPI targets, and release criteria | |
What ships and the pre-publish checklist |
๐ Repo layout
Path | What |
| The Python runtime ( |
| The pure-Node MCP server ( |
| Language-agnostic asset pack: skills, rubrics, tool schemas, eval tasks |
| Runnable starter scripts (quickstart, structured output, layered memory) |
| Repo tools that ship outside the wheel (e.g. the shift-work driver) |
| This runbook |
Design notes live in docs/superpowers/specs/.
Available Tools
16 toolsbantamkit_statusbantamkit statusARead-only
Is bantamkit working, and which bantamkit? Returns a short report a person can read in the transcript: bantamkit Active ๐ข or bantamkit Degraded ๐ on the first line, then the version and build_id of the code answering, the surfaces it serves, the memory store it is bound to with its index size against its budget, whether the event log is on, and โ when degraded โ every condition in full with what to do about it. Call it when the user asks whether the server is alive or working, when a bantamkit tool answered oddly and you need to know whether the server is the reason, or when a reply arrived carrying a bantamkit degraded footer and you want the rest of it. Every fact is observed at call time from the server's own state; nothing is a heartbeat or a cached verdict. Pass detail: true to list every usage and touch ledger on its own line instead of one summary line per family. Writes nothing, and never echoes another call's arguments. build_identity answers only the build_id fingerprint, as JSON, when all you need is to compare two installs; shiftwork_status reports a job, not the server.
Inputs: detail. Returns: result
build_identityBuild identityARead-only
Which bantamkit build is answering you, readable mid-call. Returns build_id โ a content fingerprint of the running source and the asset pack it loads โ plus the paths it was computed over. Call it when one MCP server name may resolve to more than one install: two endpoints reporting the same version are the SAME build only if their build_id matches, because a version string moves on release bumps and not on builds. Any fact that cannot be derived comes back as {"unavailable": ""} and is named in the unavailable list โ never omitted, never a placeholder that reads as a value. Takes no arguments and reads no state. Use bantamkit_status when the question is whether the server is healthy or which store it is bound to โ that report includes this fingerprint; call this tool when you need only the fingerprint, as JSON, to compare two endpoints.
Inputs: no named parameters. Returns: object
memory_compactCompact the memory storeADestructive
Free room in persistent memory when memory_save was refused for budget. Archives the stalest facts of the writable project store until a fact as large as the biggest one kept will fit, and returns what moved โ every archived name with its type and description. Nothing is deleted: archived facts leave the index but stay on disk under archive/, memory_recall with include_archived still finds them, and bringing one back is an operator action rather than a tool โ bantamkit-memory restore <name> (Node) or python -m bantamkit.memory restore <name> (Python) on the store; no tool deletes a fact. Read-only grants and the profile layer are never touched. Call it after a budget refusal, then retry the save; calling it under budget archives nothing and says so. It is not the pass for two copies of one name across layers โ that is memory_dream.
Inputs: reserve. Returns: result
memory_dreamDream: consolidate the memory storeADestructive
Consolidate the memories the project layer and the machine-wide profile layer hold under the SAME NAME, and say exactly what changed. Byte-identical copies collapse to one; a pair that has diverged is UNIONED โ every claim from both copies survives, never a winner-takes-all โ and a relative date in a body is annotated with the day it resolves to against that fact's own mtime, never against today. The survivor stays in the writable project layer and the profile copy MOVES to that store's archive/: nothing is deleted, and bringing a consumed copy back is an operator action, not a tool โ bantamkit-memory restore <name> or python -m bantamkit.memory restore <name> on the profile store. This is a correctness pass, not a token saving: the profile index is derived at read time and is not loaded from a prompt, so consolidating it frees approximately no bytes โ what it buys is one copy of a ruling instead of two that can disagree. It defaults to a DRY RUN because the profile store is machine-wide: a memory archived out of it stops answering for every other project on this machine that has no store of its own. Read the plan first, then call it again with dry_run false to apply it. For room in the index call memory_compact instead; for the facts themselves, memory_recall.
Inputs: dry_run. Returns: result
memory_queriesLikely-prompt queries for saved factsA
Write, or list the facts that still lack, the likely-prompt queries recall matches a user's prompt against โ for each fact, the prompts a user would type when that fact is what they need. Call it with {} at the end of a session that saved facts, or when bantamkit_status reports queries: n of m facts with n below m: it answers up to 10 facts with no queries yet (layer, name, description, a body excerpt), the writing instructions, and remaining. Then call it with {"facts": [{name, layer?, queries}]} to store what you wrote. Every item is checked before anything is written and the first bad one refuses the whole call โ an unknown name or layer, more than 8 queries, one over 200 characters; queries: [] removes a fact's entry. No fact file changes: queries live in a per-layer sidecar under ~/.bantamkit/queries/, keyed to the fact's content, so a fact edited later is listed as missing again. memory_save takes queries directly for a fact you are saving now; this tool is for facts already on disk.
Inputs: facts. Returns: facts, reason, result, written, +2 more
memory_recallRecall memoriesA
Search persistent memory for facts a past session saved. Call it BEFORE starting a task that resembles past work, and when the user refers to something decided earlier; use memory_save to add a fact, and memory_compact or memory_dream only to tidy the store โ this tool never changes what a fact says. Reads every layer in order โ the project store, any read-only grants in .bantamkit/config.yaml, then the machine-wide profile โ and returns the top word matches, never fewer than the store default of 3 when that many match; a query that is exactly a fact's name returns that fact alone. Pass file instead of query to see the facts linked to a file. Why it is not read-only: every hit is appended to the usage ledger (~/.bantamkit/usage/.jsonl) as a recall event โ the staleness order memory_compact archives by โ and a project fact file still in the 0.35.x format is rewritten once into the current one; both are best-effort and never fail a recall. A fact file that cannot be read is named in the reply, never silently skipped. Pass outcome: error to see only recorded failures.
Inputs: k, file, query, outcome, +1 more. Returns: result
memory_saveSave a memoryADestructive
Save ONE durable fact to persistent memory โ a preference the user stated, a decision, the fix that worked โ so a future session can recall it. Use it only for facts a future session needs; never for anything derivable from the repo, git history or docs, nor for one-off state of this conversation. To find a fact use memory_recall; to change one, save again under its EXACT name, which replaces it in place; to free room call memory_compact. Refused with nothing written, each with its own sentence: a description too close to an existing fact's (the reply names that fact โ save under that SAME name or skip, never rename to force a copy), a spelling that normalises onto an existing name (Deploy Command onto deploy-command), and a save that would push the always-loaded index over its byte budget (the reply names the remedy). Every save lands in the writable project store (.bantamkit/memory), never in the profile or a read-only grant, and is appended to the usage ledger. Optional outcome (error|success) tags a lesson and status: done closes a project fact; nothing here archives โ that is the operator's bantamkit-memory candidates/merge.
Inputs: body (required), name (required), type (required), description (required), +5 more. Returns: result
shiftwork_clock_inShift work: clock inA
Shift-work clock-in: validate the checkpoint against the checkpoint schema and return the brief of ONE unit โ {unit, role, invariants, handoff, do_not, files} โ to hand to the agent you spawn, verbatim. Call it once per unit, right before spawning; use shiftwork_plan first to see the ready batch when several units are to run at once, and shiftwork_clock_out, not this tool, when a unit finishes. The unit briefed is plan.cursor, or unit_id when given; either way it must be in shiftwork_plan's ready โ a done or dropped cursor, a cursor whose dependencies have not run, or a unit_id naming no unit answers {"result": "error", "reason": "unit is not ready; ready is <a, b>"}, and the batch view's own refusals (cycle, unknown dependency, unreadable checkpoint) pass through verbatim. A ready unit whose touches shares a path with a unit briefed and not yet clocked out is refused too, before any brief: unit <id> touches <path>, which <other> holds until its clock-out. A checkpoint whose reviews break review independence is refused with shiftwork_plan's sentence. With job.budget declared, a brief that does not fit is refused: budget <dim> spent: <n> of <limit>; raise job.budget or stop the job. The other outcomes, never exceptions: escalate when handoff.open_questions is non-empty (stop and ask the user) or when the cursor names no unit, success when every unit is done or dropped, error when the checkpoint fails validation. It never moves plan.cursor; clock_out does. What it WRITES: on result=brief, and only then, one line {event: brief, ts, unit, role} appended to .log.jsonl โ best-effort, so a log it cannot write costs the brief nothing. That line is what later lets clock_out accept the unit while the cursor is elsewhere and sets briefed on its accounting line. The checkpoint file itself is never modified. See docs/shiftwork.md.
Inputs: checkpoint (required), unit_id. Returns: role, unit, files, do_not, +8 more
shiftwork_clock_outShift work: clock outADestructive
Shift-work clock-out: record that a unit finished โ set its status, advance plan.cursor, merge handoff_patch into handoff, push history_entry onto the 5-entry history ring, and append one accounting line to .log.jsonl. Call it once per unit after its agent returns: shiftwork_clock_in briefs, this tool closes, and shiftwork_status reads what both wrote. The whole mutated document is validated against the checkpoint schema BEFORE one atomic write, so a failure writes nothing and answers {"result": "error", "reason": ...}; nothing is ever deleted from the checkpoint. unit_id must be plan.cursor โ which needs no brief and clocks out with briefed=false โ or a unit clock_in briefed since its own last clock-out; any other id is refused with unit X is not the cursor unit Y. handoff_patch and history_entry are both required arguments: pass {} to leave handoff unchanged, and give history_entry at least unit and outcome. plan.cursor then moves to the head of the ready batch recomputed on the new document โ plan.units order over the remaining non-terminal units when the graph cannot batch, unit_id when none remain. The accounting line carries unit, role, status, ts and briefed (written by the runtime, never taken from you: true only when a brief was issued since this unit's LAST clock-out) plus your accounting fields when given. follow_ups appends the work it discovered as todo units. agreements adopts or retires a working agreement every later brief carries. Each clock-out also appends {status, outcome, notes?, produced?} to the unit's attempts (newest 3 kept). With job.budget declared, a line reporting 0 tokens is refused unless it carries estimate: true. When job.roles names this unit's role, accounting.model must be on that role's list, spelled exactly, or nothing is written โ see the model property.
Inputs: status (required), unit_id (required), checkpoint (required), handoff_patch (required), +6 more. Returns: log, unit, cursor, reason, +2 more
shiftwork_draftShift work: draftA
Shift-work draft: turn tickets into a new job โ a checkpoint that validates against the checkpoint schema, plus one brief file per ticket โ so step 1 of the shift-work loop is not typed by hand. Call it once, when a job's tickets are settled and before the first shiftwork_clock_in; it never edits a job in progress (that is the planner's edit, or shiftwork_clock_out's follow_ups), and work_plan is the read-only answer when all you want is the batches. Every ticket needs a non-empty id, title, role, verify, data_shape (the types, fields, files or messages the unit produces or consumes โ a ticket without one is not ready to execute) and brief; the first missing one is refused by name. The graph is work_plan's: its refusals come back verbatim, batches and width are its answer, and cursor is the first ready unit, the one shiftwork_clock_out would pick. With job.roles declared, a ticket role it does not name is refused. Each ticket becomes a todo unit with brief_path <briefs_dir>/.md; any other ticket key (touches, reviews, irreversible, ...) is copied onto the unit, and the composed document must pass the checkpoint schema and shiftwork_plan's graph checks, or the schema's sentence is the refusal. write is false by default: the document and every brief's text come back and nothing is touched. With write: true the briefs and then the checkpoint are written by temp file + rename under .lock; an existing checkpoint, or an existing brief with other bytes, is refused before anything is written, and a failure part-way names what landed in written. It never runs git, never runs verify and never reads the working tree: repo is taken as given. Refusals are {"result": "error", "reason"}, never exceptions.
Inputs: job (required), repo (required), tickets (required), write, +4 more. Returns: width, briefs, cursor, reason, +4 more
shiftwork_planShift work: planARead-only
Shift-work plan: which units of a checkpoint may run NOW, and in what waves โ the batches its depends_on graph permits, computed read-only from the file. Call it before dispatching a wave: ready (the first batch) is what shiftwork_clock_in will brief, and width is how many agents the widest wave needs. A ready member beyond the cursor is clockable only once clock-in has briefed it; without that brief, clock-out refuses it. Use shiftwork_status instead when you only want progress counts, and work_plan when the graph is yours to hand over as nodes rather than a checkpoint on disk โ this tool reads the checkpoint's own plan.units. Units already done or dropped count as satisfied and are left out of every batch; todo, in_progress and blocked stay in. Order inside a batch is plan.units order, because the checkpoint schema has no priority field. It never moves plan.cursor โ the cursor is echoed unchanged so the pointer and the batch view can be read side by side โ and never writes anything. Refusals are structured, never exceptions: work_plan's sentences for a duplicate id, an unknown dependency or a cycle, and shiftwork_status's for a checkpoint it cannot read; a plan whose units are all terminal answers an empty ready, which is an answer, not a refusal. depends_on says nothing about two units writing the same file; a unit's optional touches (repo-relative paths it will WRITE) does: no batch holds two units sharing a path, a unit held out of one is reported in held: [{id, batch, by, path}] (absent when no unit declares touches), and a bad path is refused with work_plan's sentence. A unit whose reviews names a unit that is undeclared, not in its depends_on, or in its own role is refused: unit <id> reviews <r> .... With job.budget declared it adds budget (spent, remaining, per_unit, affordable).
Inputs: checkpoint (required). Returns: held, ready, width, budget, +7 more
shiftwork_statusShift work: statusARead-only
Shift-work status: where ONE checkpoint file stands โ units counted by status, the unit plan.cursor points at, how many handoff.open_questions are waiting, and the last history entry. Call it first when resuming a job or answering 'where is job X'; it is the cheap read before any clock-in. It does not say WHICH units may run now โ that is shiftwork_plan, the dependency batches โ and it says nothing about the server itself, which is bantamkit_status. Every fact is read from the checkpoint on this call, nothing is cached, and a checkpoint that is missing, not valid JSON or failing the checkpoint schema answers {"result": "error", "reason": ""} instead of a partial summary โ the same refusals shiftwork_plan and shiftwork_clock_in give for that file. With search, it also returns the lines of jobs Retention archived under the checkpoint's .shiftwork/archive/ that hold every word of the query. Opens only the checkpoint (and that archive directory when search is given); writes nothing, not even the log shiftwork_clock_in appends to.
Inputs: checkpoint (required), search. Returns: units, cursor, reason, result, +3 more
skill_auditAudit the skill catalogueARead-only
Price the skill catalogue every session pays for, and name the collisions in it. A skill's description: frontmatter is loaded into the agent's context in EVERY session; its body is read only when the skill is invoked, so the descriptions are the standing bill. Call it before adding or retiring a skill, or when two skills keep firing on the same phrase. Walks root for <marketplace>/<plugin>/<version>/skills/<name>/SKILL.md and answers one JSON document: roots, skills (counted), catalogue_bytes (UTF-8 bytes of their description: values), findings and omissions. A degraded server adds the footer as its last key, bantamkit_degraded. Read findings first โ each is {kind, severity, skills, detail}: shared-trigger-phrase (high; two skills quote the same literal phrase, so which fires is a coin flip), catalogue-over-budget (high; only when budget is given), never-invoked (low; only when usage is given), frontmatter-malformed and name-mismatch (medium). Then omissions: counted skills plus omissions account for every SKILL.md found, each {subject, count, size, what} with subject one of plugin-not-enabled, duplicate-skill, stale-version, unreadable-file, unparsed-frontmatter. The tool reads nothing but root; the three facts a directory cannot answer come from the caller โ enabled (plugin ids the host has switched on, <plugin>@<marketplace>; the cache also holds disabled plugins, so without it the bill is inflated), usage (call counts per skill) and versions (the version directory the host serves per plugin; a plugin absent from it gets the directory name that sorts LAST in byte order, which is wrong for 10.0.0 against 9.0.0). Refused with their own sentence, never as a partial answer: an unknown check, a negative budget, an EMPTY root, a root that is missing or is a file. Full rules in docs/skill-audit.md.
Inputs: root (required), check, usage, budget, +2 more. Returns: result
token_ledgerToken ledgerARead-only
What a session actually cost, read off the host's own transcripts. Walks root for *.jsonl transcripts โ the host writes <cwd-slug>/<session>.jsonl and <cwd-slug>/<session>/subagents/*.jsonl beneath ~/.claude/projects โ and answers ONE JSON document: root, transcripts, lines, requests (distinct API requests), totals (the four classes: input_tokens, cache_creation_input_tokens, cache_read_input_tokens, output_tokens), sessions (one row each), omissions, and cost only when model is given. A degraded server adds the footer as its last key, bantamkit_degraded. Call it to answer 'what did this work cost' from the API's own usage blocks; nothing is estimated. Point root at ONE project's slug directory: there is no time window or summary mode, so a whole-corpus call answers a row per session ever recorded and blows the host's tool-result cap. The four classes are priced separately: on a real prompt 98.1 % of the tokens were cache_read. Counting is per requestId, never per line: one response is written as several records with the same usage, so the dedupe spans the whole walk and every later copy is a duplicate-request omission. A subagent's transcript carries its PARENT's sessionId, so its cost lands in the parent row as sidechain_requests. lines equals requests plus the sum of every omission's count, always. An omission is {subject, count, what}; what names the first site as <relpath>:<line> plus and N more. cost needs a rate: the shipped price table has none, so {"unavailable": "no rate recorded for model ..."} is the NORMAL answer, not an error; a rate enters through $BANTAMKIT_PRICES or prices. Refused with their own sentence: an EMPTY root, a root that is missing or is a file, an EMPTY model, and a token total above 2**53-1. The record rule and the nine omission subjects: docs/ledger.md.
Inputs: root (required), model, prices. Returns: result
validate_jsonValidate JSON against a schemaARead-only
Validate candidate output text against a JSON Schema and say what is wrong. Answers {valid, feedback}: when invalid, hand feedback back to the model and retry โ it says where the document fails and quotes the offending value, clipped to 200 characters; when valid, feedback is null. Use it on any JSON a model produced before acting on it; for a shift-work checkpoint call shiftwork_status or shiftwork_clock_in instead, which validate against the shipped checkpoint schema themselves. output may be a bare JSON value; otherwise the first fenced code block, else the first object or array in the text, is what gets validated. $schema selects draft-04, draft-06, draft-07, 2019-09 or 2020-12 (the default). $ref resolves only inside the one schema given (#, #/..., #name): a remote reference or an embedded $id is refused with its own sentence, never fetched. pattern is Python re syntax without inline flags, look-behind, backreferences, atomic, possessive or conditional groups or \N{...}, and all of a document's patterns share 500 ms: past that the answer is one refusal sentence naming the pattern that ran out, not a partial validation. Reads nothing but its two arguments and writes nothing.
Inputs: output (required), schema (required). Returns: valid, feedback
work_planWork plan: batch a dependency graphARead-only
Work plan: turn a dependency graph you hand it into the batches that may run in parallel โ pure computation over nodes: no file opened, no path taken, nothing mutated. Use it when the graph is in your hands (tasks with depends_on); use shiftwork_plan when the graph is a shift-work checkpoint on disk, which it reads for you under the same batching rules. Batch k holds every node whose dependencies all sit in batches below k; sequence is the batches flattened; width is the largest batch โ the fan-out an orchestrator must afford. Inside a batch: priority descending, then the order the nodes were given; both halves are contract, so two implementations answer byte-identically. Empty nodes is an ANSWER (no batches, width 0), not a refusal. Four refusals, in this order, one sentence each: nodes[<i>].<field> must be ... (an id that is not a string, depends_on not a list of strings, priority not an integer โ named by position, never coerced), duplicate node id <id>, node <id> depends on <dep>, which no node declares, and the graph has a cycle: <a> -> <b> -> <a>. depends_on encodes logical order, not file contention; give a node touches (repo-relative paths it will WRITE) and no batch ever holds two nodes sharing a path: inside a level, in the same order, a node whose path an admitted node already claims is held for the next batch, and the answer then carries held: [{id, batch, by, path}] (absent when no node declares touches). A path that is empty, absolute, holds a backslash or a ./../empty segment is the fifth refusal, checked with the shape: node <id> touches <json>, which is not a plain relative path.
Inputs: nodes (required). Returns: held, width, reason, result, +2 more
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
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TDQS
Scored across 16 tools
Most tools target distinct resources, but several pairs have genuinely close boundaries: shiftwork_plan vs work_plan (same batching rules, file vs. in-memory graph), bantamkit_status vs build_identity (status already includes build_id), and memory_compact vs memory_dream (both archive). The descriptions work hard to disambiguate each of those explicitly, which is why this lands at 'some overlap but descriptions help' rather than lower.
All names are snake_case and namespaced by domain (memory_*, shiftwork_*), which makes membership obvious at a glance. The ordering is mixed โ verb_noun (memory_save, validate_json, build_identity) alongside prefix_noun (shiftwork_clock_in, token_ledger, skill_audit) โ and the one-word neighbours shiftwork_plan/work_plan are a minor readability hazard, but nothing is chaotic.
Sixteen tools spread across five distinct concerns (build/health, memory, shift-work orchestration, JSON validation, cost/skill auditing) means each area is small and the total is not padded. It sits at the upper edge of comfortable and a few tools (build_identity, memory_queries) could arguably fold into their siblings, but every tool has a defensible role.
Shift-work covers the full loop (draft โ plan โ clock_in โ clock_out โ status) and memory covers save/recall/tidy/reconcile/queries. The notable gaps are deliberate but still gaps: no tool to delete or restore an archived fact, no shift-work edit/archive operation, and skill_audit/token_ledger are purely read-only with no remediation โ all of which the descriptions route to operator CLI commands instead.
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