io.github.batchwatch/batchwatch-mcp
NPM · BATCHWATCH-MCP
Ask batchwatch whether to batch a job and get the real measured queue evidence, not a guess.
Available components
Last scanned (18 hours ago)
Scores higher than 66% of npm servers
Category AI and Machine Learning
How this component scores in each security and reliability category. Every signal is checked automatically from public evidence about the published package, including repeated runs of it in an isolated sandbox, and we only credit what we can confirm. How we score → Why this is hard to score →
Supply Chain Security98
- No malware found by supply-chain analysis.Pass
- No known CVEs affecting this package version or its production dependencies.Pass
- No install/post-install scripts declared.Pass
- 30 of 92 dependencies flagged as unhealthy. View diagnostics → Partial
Provenance & Transparency45
- Source repository is publicly reachable at the declared URL. View diagnostics → Pass
- Provenance check failed: no build-provenance attestation is published. See how to fix → View diagnostics → Fail
- Clear OSI-approved license (MIT).Pass
- Actively maintained (last published 64 days ago).Pass
- Disclosure check failed: no security disclosure policy was found in the source repository. See how to fix → Fail
Schema Quality & AI Usability59
- AI-judged instruction clarity (good).Pass
- Context-footprint check failed: tool/resource definitions use about 1470 tokens (~245/item across 6 items; 6 tools + 0 resources), over budget; trim descriptions and params. See how to fix → Fail
- Usage-examples check failed: none of the tools include examples. See how to fix → Fail
Stability & Change Management100
- No destabilizing schema changes in the last 30 days.Pass
Tool Coverage89
- 100% of tools have a non-trivial description (not blank, and not just the tool's name).Pass
- 67% of tool parameters carry a description.Partial
Tool Safety100
- No prompt-injection markers were found in the server instructions, tool names or descriptions we captured.Pass
- We read all 6 captured tool definition(s), and no name or description among them implies an irreversible operation.Pass
- An AI judge read all 6 captured unit(s) of tool text and found none that tries to manipulate the model reading it.Pass
Capabilities100
- Implements a supported MCP spec version (2025-11-25); the latest is 2026-07-28.Pass
Every change we have recorded for this component, newest first. Security-relevant changes are always shown. ▲ marks a change for the better, ▼ a change for the worse; unmarked changes are neutral.
- 4 Nov 26 0
- Stability: 0.97 → pass security
- 3 Nov 26 0
- Stability: pass → 0.97 functional
- 28 Oct 26 0
- Stability: 0.97 → pass security
- 27 Oct 26 0
- Stability: pass → 0.97 functional
- 17 Oct 26 +2
- Stability: 0.83 → pass security
- 16 Oct 26 −2
- Stability: pass → 0.83 functional
- 15 Oct 26 0
- Stability: 0.97 → pass security
- 14 Oct 26 +1
No change was recorded against any check on this day. Stability & Change Management went from 93 to 97. That category is still filling its 30-day observation window: 28 days of observed history at the previous scan, 29 at this one. The score rises as the window fills, whether or not the server changes.
Diagnostic detail from the automated scan of this channel: what the scanner observed at each step, so you can see exactly where a check passed or failed. It is informational only and never changes the trust score.
Captured 4 Nov 2026 · Analysed npm/batchwatch-mcp@0.1.1
Provenance No attestation
The registry publishes no build provenance for this version, so there is nothing to verify.
| Result | No attestation |
|---|---|
| Ecosystem | npm |
Background: How many MCP packages publish verified provenance →
Dependencies 92 packages
| Packages resolved | 92 |
|---|---|
| Stale | 30 |
| Tree resolution | Complete |
Background: SBOMs and build attestations, explained →
The tools this component advertises to a client, with an estimated token cost for each. Expand a tool to see its parameters and schema. The per-tool counts are indicative and are not scored directly; the schema's total context footprint is one signal in Schema Quality & AI Usability. A tool's description is untrusted text the model reads on every call, which is what makes this list a security surface and not just an inventory: how tool poisoning works →
conditions ~210
Current queue conditions for a model right now vs its recent norm — "is it me or them?". Traffic report, not arrival time. Carries n, confidence, freshness. batchwatch measures how long jobs ACTUALLY wait in LLM providers' async batch queues. It reports distributions and current conditions, never a point ETA — a personal history of 3-minute medians once produced a 480-minute job, so "your job finishes at 14:32" is a lie the one time it matters. batchwatch does NOT decide for you: you pass your own max_wait and it says whether the queue currently honours it. Every answer carries n (how many measurements), a graded confidence with a plain-English why, a basis naming the evidence, and freshness (how old the newest measurement is). Without an API key the data is delayed, not absent. Absence is reported as absence, never as zero.
| Name | Type | Req | Description |
|---|---|---|---|
| model | string | yes | Exact model name. Required. |
| provider | string | – | – |
No output schema declared.
No examples provided.
coverage ~194
Which provider+model combinations batchwatch currently has data for, and how answerable each is (n, contributors, confidence). Call this first if you are unsure whether a model is measured. batchwatch measures how long jobs ACTUALLY wait in LLM providers' async batch queues. It reports distributions and current conditions, never a point ETA — a personal history of 3-minute medians once produced a 480-minute job, so "your job finishes at 14:32" is a lie the one time it matters. batchwatch does NOT decide for you: you pass your own max_wait and it says whether the queue currently honours it. Every answer carries n (how many measurements), a graded confidence with a plain-English why, a basis naming the evidence, and freshness (how old the newest measurement is). Without an API key the data is delayed, not absent. Absence is reported as absence, never as zero.
Input schema present but exposes no named parameters.
No output schema declared.
No examples provided.
distribution ~239
The full measured wait distribution for a model + mode over a window. The raw evidence behind the verdict. Returns absence as absence (never zero) when there is no data. batchwatch measures how long jobs ACTUALLY wait in LLM providers' async batch queues. It reports distributions and current conditions, never a point ETA — a personal history of 3-minute medians once produced a 480-minute job, so "your job finishes at 14:32" is a lie the one time it matters. batchwatch does NOT decide for you: you pass your own max_wait and it says whether the queue currently honours it. Every answer carries n (how many measurements), a graded confidence with a plain-English why, a basis naming the evidence, and freshness (how old the newest measurement is). Without an API key the data is delayed, not absent. Absence is reported as absence, never as zero.
| Name | Type | Req | Description |
|---|---|---|---|
| mode | string | – | – |
| model | string | yes | Exact model name. Required. |
| provider | string | – | – |
| window | string | – | Time window, e.g. "30d", "7d". Optional. |
No output schema declared.
No examples provided.
estimate_batchtime ~244
How long is MY job likely to wait? Returns the batch-wait distribution (p50/p90) for the model, with n, confidence and freshness. A distribution, never a single ETA. batchwatch measures how long jobs ACTUALLY wait in LLM providers' async batch queues. It reports distributions and current conditions, never a point ETA — a personal history of 3-minute medians once produced a 480-minute job, so "your job finishes at 14:32" is a lie the one time it matters. batchwatch does NOT decide for you: you pass your own max_wait and it says whether the queue currently honours it. Every answer carries n (how many measurements), a graded confidence with a plain-English why, a basis naming the evidence, and freshness (how old the newest measurement is). Without an API key the data is delayed, not absent. Absence is reported as absence, never as zero.
| Name | Type | Req | Description |
|---|---|---|---|
| input_tokens | integer | – | Optional. Omit if unknown — do not send 0. |
| model | string | yes | Exact model name. Required. |
| provider | string | – | – |
| risk | string | – | – |
No output schema declared.
No examples provided.
should_i_batch ~366
The verdict: given a model and your own max_wait, does the batch queue currently honour it? Returns the batch p50/p90 wait, the sync comparison if known, the cost saving basis, and a graded confidence + why + freshness. It does not decide for you — it reports whether the queue fits YOUR tolerance. batchwatch measures how long jobs ACTUALLY wait in LLM providers' async batch queues. It reports distributions and current conditions, never a point ETA — a personal history of 3-minute medians once produced a 480-minute job, so "your job finishes at 14:32" is a lie the one time it matters. batchwatch does NOT decide for you: you pass your own max_wait and it says whether the queue currently honours it. Every answer carries n (how many measurements), a graded confidence with a plain-English why, a basis naming the evidence, and freshness (how old the newest measurement is). Without an API key the data is delayed, not absent. Absence is reported as absence, never as zero.
| Name | Type | Req | Description |
|---|---|---|---|
| input_tokens | integer | – | Optional. Prompt tokens, used to compute the cost saving. Omit if unknown — do NOT send 0. |
| max_wait | string | – | How long you can tolerate waiting, e.g. "15m", "2h", "24h". This is the whole question — send it. |
| model | string | yes | Exact model name, e.g. "gpt-5.6-sol". Required. |
| output_tokens | integer | – | Optional. Completion tokens, or max_tokens as an upper bound. |
| provider | string | – | Defaults to openai. |
| risk | string | – | Which percentile to judge against. p90 is the cautious planning number. |
No output schema declared.
No examples provided.
wait ~217
The public "is the queue moving now?" reading for a model (delayed without a key). Lighter than should_i_batch — no max_wait judgement, just the current wait numbers with confidence + freshness. batchwatch measures how long jobs ACTUALLY wait in LLM providers' async batch queues. It reports distributions and current conditions, never a point ETA — a personal history of 3-minute medians once produced a 480-minute job, so "your job finishes at 14:32" is a lie the one time it matters. batchwatch does NOT decide for you: you pass your own max_wait and it says whether the queue currently honours it. Every answer carries n (how many measurements), a graded confidence with a plain-English why, a basis naming the evidence, and freshness (how old the newest measurement is). Without an API key the data is delayed, not absent. Absence is reported as absence, never as zero.
| Name | Type | Req | Description |
|---|---|---|---|
| model | string | yes | Exact model name. Required. |
| provider | string | – | – |
No output schema declared.
No examples provided.
How do I install the io.github.batchwatch/batchwatch-mcp server?
io.github.batchwatch/batchwatch-mcp runs locally as an npm package, launched with npx -y batchwatch-mcp. Ready-made configuration for Claude, Cursor, VS Code, Codex and 5 more is on this page, copied from each client's own documentation.
npm · batchwatch-mcp
claude mcp add batchwatch-batchwatch-mcp -- npx -y batchwatch-mcp
What is the io.github.batchwatch/batchwatch-mcp server?
io.github.batchwatch/batchwatch-mcp is listed in the public MCP registry as io.github.batchwatch/batchwatch-mcp. Ask batchwatch whether to batch a job and get the real measured queue evidence, not a guess. This page covers its npm package (batchwatch-mcp).
Is the io.github.batchwatch/batchwatch-mcp server safe to use?
io.github.batchwatch/batchwatch-mcp scores 80 out of 100 on VerifyMCP. We found no known CVEs affecting it as of 4 November 2026. It declares no install or post-install scripts. That is a record of what we were able to check automatically, not an endorsement. The category breakdown on this page shows every signal behind the number, including the ones we could not confirm.
What tools does the io.github.batchwatch/batchwatch-mcp server expose?
io.github.batchwatch/batchwatch-mcp exposes 6 tools: should_i_batch, estimate_batchtime, conditions, distribution, coverage, wait. Their descriptions and schemas cost roughly 1,470 tokens of context every time the server is loaded.
Is the io.github.batchwatch/batchwatch-mcp server still maintained?
io.github.batchwatch/batchwatch-mcp is still listed as active in the MCP registry. We last reached this channel on 4 November 2026. Those dates come from our own scans of the registry and the channel itself, not from anything the publisher announced.
What licence is the io.github.batchwatch/batchwatch-mcp server under?
io.github.batchwatch/batchwatch-mcp declares the MIT licence, which is OSI-approved. That covers the source only, and says nothing about the cost of any service it calls.