ZeroWidth Caliper

它能帮你做什么

我们还没有为它写通俗的介绍。下面是我们从注册中心读取到的事实。

• 连接方式: 在云端运行
• 传输方式: Streamable HTTP
• 分类: 数据库与数据集
• 你需要填写：Authorization

安装

选择你使用的 AI 应用，然后复制设置。

你需要

• 在云端运行无需安装任何东西，通过互联网连接

你用的是哪个应用？

claude mcp add --transport http caliper https://api.zerowidth.ai/mcp/x/caliper --header 'Authorization: your-value'

{
  "mcpServers": {
    "caliper": {
      "url": "https://api.zerowidth.ai/mcp/x/caliper",
      "headers": {
        "Authorization": "your-value"
      }
    }
  }
}

{
  "servers": {
    "caliper": {
      "type": "http",
      "url": "https://api.zerowidth.ai/mcp/x/caliper",
      "headers": {
        "Authorization": "your-value"
      }
    }
  }
}

你的选择通过 Cookie 保存在这台设备上，无需账户。

需要你填写的值

• Authorization 机密 HTTP header
  Bearer token: a workspace API key or personal access token from accounts.zerowidth.ai

这些设置是根据作者在注册中心的条目生成的。如果 README 的写法不同，请以 README 为准。

它能做什么

工具名称和描述与服务器在 2026年11月8日 报告的完全一致。这些内容由作者撰写，不是我们写的；我们以纯文本形式显示。

• search_docs

  Search ZeroWidth product documentation. Returns matching pages with title, slug, public URL, and a query-relevant snippet. Use this when the user asks about a ZeroWidth product (Compass, Workbench, Caliper, Prism, Ledger, Napkin, the assistant), an API behavior, or a policy. No a

• get_doc

  Fetch the full Markdown body of a specific docs page by its slug. Use this after `search_docs` when the user needs the complete content of a page. No authentication required.

• list_docs

  Enumerate all available docs pages, optionally filtered by product (e.g. 'compass', 'legal', 'overview'). Use this to discover what slugs exist before calling `get_doc`. No authentication required.

• caliper_datasets_get

  One dataset with a page of its items — read this BEFORE editing items (caliper_datasets_update_item needs the item id) and before extending coverage, so you don't add cases that already exist. Items come `limit` at a time (default 25) from `offset`; `total` is the full count. Lon

• caliper_datasets_update

  Changes a dataset's title, description, or visibility. Pass only what changes; items are untouched (use caliper_datasets_add_items / caliper_datasets_update_item / caliper_datasets_remove_item for those). May return `needs_confirmation`.

• caliper_datasets_delete

  Deletes a dataset. Evals bound to it stop being runnable (their binding shows datasetOk: false), and reviews/specs over its items lose their source — check caliper_evals_list for evals that reference it and say so before proposing. May return `needs_confirmation`.

• caliper_datasets_update_item

  Rewrites one item's content, keeping its id (ratings and eval results keyed to it stay attached). The item's KIND can't change — a Q&A item stays Q&A, a chat item stays chat, a sequence stays a sequence — so pass the same shape you read from caliper_datasets_get. Raw items, and c

• caliper_datasets_remove_item

  Drops one item. Ratings and run results keyed to it are orphaned (kept in history, gone from the dataset). A dataset must keep at least one item. May return `needs_confirmation`.

• caliper_datasets_generate

  Creates a NEW dataset of model-written items — Q&A by default, or scripted sequences / simulated people via `shape` — pass flowId and the generator reads the flow's prompt, mode, and schema to write realistic cases for THAT flow; `description` adds guidance (or stands alone when

• caliper_datasets_generate_items

  Appends model-written items to an EXISTING dataset, in the style of what's already there (existing items are the few-shot examples; the output shape matches theirs unless overridden). Use to widen coverage when the user says 'more like these' or 'add edge cases'; write them by ha

• caliper_rubrics_list

  Lists the rubrics in the active workspace (id, name, description, criterion count). Check here BEFORE caliper_rubrics_create — reuse an existing rubric's id in caliper_evals_create when one already scores the same job. Read a rubric's criteria with caliper_rubrics_get.

• caliper_rubrics_get

  One rubric with its criteria — each criterion's name, what the judge looks for, and the score scale. Read this to explain a score (which criterion slipped and what it asks for) or before caliper_rubrics_update. Get the rubricId from caliper_rubrics_list or an eval's rubricId.

• caliper_rubrics_update

  Changes a rubric's title, description, visibility, or replaces its criteria wholesale (pass the FULL list — criteria get fresh ids). Evals snapshot the rubric when they're created, so an existing eval keeps scoring with the criteria it started with; say so, and offer to create a

• caliper_rubrics_delete

  Deletes a rubric. Existing evals keep their snapshot of it and keep running; nothing new can bind to it. May return `needs_confirmation`.

• caliper_evals_get

  One eval: what it targets (Workbench flow + stage, or external), its dataset and rubric ids, the rubric criteria it scores with (the snapshot taken at creation), schedule + runOnPublish + regression settings, run counts, latest score, and `binding` health (datasetOk / flowOk fals

• caliper_evals_runs_item_execution

  What the flow actually DID on one run item: every step in order (what it said, which tools it called with what arguments, what came back) and the final answer, plus status, duration, and cost. Read this when a low score needs explaining beyond the judge's reasoning — a wrong tool

• caliper_evals_update

  Changes an eval's title, description, visibility, schedule (interval + time anchor), runOnPublish (queue a run whenever the flow publishes), regressionThreshold (score drop vs the previous run that triggers an alert; null = off), or scenarioId (the scenario runs use). Pass only w

• caliper_evals_delete

  Deletes an eval and stops its schedule. Run history is kept but no longer reachable from the eval. May return `needs_confirmation`.

• caliper_evals_runs_cancel

  Stops a run that is still PENDING / RUNNING / SCORING — the brake on a run that's spending more than expected or was started by mistake. The queue stops at once; an item already handed to the flow finishes on its own timeout. The run settles as FAILED with a 'cancelled' reason an

• caliper_sources_list

  Apps sending their agent's traces to Caliper — from their own code, OpenTelemetry, or a published Workbench flow — busiest first: name, how it sends, traces in the last 14 days (per day), and the tools its agent calls most with the share of traces using each. Start here when the

• caliper_sources_over_time

  One source day by day: traces, failures, tool calls, lookups that found nothing, tokens, cost, and speed (meanMs; p50/p95 as 'answered within' bucket edges); each tool with its calls and the share of each day's traces that used it; the documents lookups landed on most; models use

• caliper_traces_list

  The traces behind a point on a source's chart, newest first, 50 a page: the question and answer (cut short), tools called, time, tokens, cost, status. Narrow by day, tool, document, or failures; page with `before` (the nextBefore from the last page). Open one with caliper_traces_

• caliper_traces_get

  One trace: what came in, what went out, and every step in order — model calls (model, tokens, cost, reasoning), tool calls (arguments and result), lookups (query and documents with scores), anything else — each with timing and status. Long values are cut at 2,000 characters. Use

• caliper_source_feeds_list

  Datasets this source keeps adding to as conversations arrive: which dataset, for what (review, eval, spec), the pick it matches, sampling, how many have landed, and whether it's still running or why it stopped.

• caliper_sources_to_dataset

  Saves the conversations behind a pick (a day, a tool, a document, failures — the same narrowing as caliper_traces_list) into a new dataset (newDatasetName) or an existing one (datasetId), newest first up to `limit`. `purpose` decides what each item keeps: review = the agent's rep

• caliper_source_feeds_stop

  Stops a source from adding new conversations to a dataset. What it already added stays. Get the feedId from caliper_source_feeds_list. May return `needs_confirmation`.

• caliper_starters_list

  The starter sets Caliper ships: prompt injection, system-prompt leaking, over-refusal, personal-data handling, bias under ambiguity. Each is a small original dataset (single messages AND multi-turn build-ups: false memory, fabricated earlier turns, slow escalation) paired with an

• caliper_starters_install

  Copies one starter into the workspace as a new dataset and a new rubric the user owns and can edit. Returns both ids; the next step is caliper_evals_create binding them to the flow (or target 'external'). Needs caliper:datasets:write and caliper:evals:write.

• caliper_datasets_list

  Lists datasets in the active workspace. Returns summaries (id, name, item count); items are not included.

• caliper_flow_performance

  How a Workbench flow is ACTUALLY doing, with receipts: every Caliper eval targeting the flow, recent runs with scores, the latest run decomposed into per-criterion averages, the score delta vs the previous run, and the worst-scoring items WITH the judge's reasoning. Use this BEFO

• caliper_evals_runs_list

  Recent runs for one eval, newest first: status (PENDING/RUNNING/SCORING/DONE/FAILED), overall score once DONE, label, and timestamps. THE CHECK-BACK for caliper_evals_run: when the user asks how the run went, read this — cite the run id and score, and compare against the PREVIOUS

• caliper_evals_runs_get

  One run: status, overall score, and by default the ten lowest-scoring items with their per-criterion scores and the judge's reasoning (`items: all` for every item, `none` for totals only). THE ANSWER to 'why did the score drop' — read this, then name the criterion that slipped an

• caliper_evals_list

  Lists evals in the active workspace with their target config (which Workbench flow, which dataset/rubric). Use to find the evalId for caliper_evals_run.

• caliper_evals_run

  Queues a new run of an eval — inference over the dataset, then LLM-judge scoring. THE VERIFY STEP of the improvement loop: after an approved workbench_flows_edit_text, run the eval again and report the score delta vs the previous run. Runs take a while — but you're brought back i

• caliper_datasets_create

  Creates a dataset of test items — THE FIRST STEP of setting up evaluation for a flow. Four item shapes: Q&A (input + optional expectedOutput, the golden answer); CHAT (messages: the conversation so far as role/content user and assistant messages you write, ending on a user messag

• caliper_datasets_add_items

  Appends items to an existing dataset — use this to grow coverage (new edge cases, scenarios from a completed interview) instead of creating a parallel dataset. Same four shapes as caliper_datasets_create (Q&A, chat, sequence, simulated). Existing items and their ratings are untou

• caliper_rubrics_create

  Creates the scoring rubric an eval's LLM judge uses — 1-10 criteria, each scored on a numeric scale (default 1-5), judged pass/fail (kind pass_fail), or checked in code with no judge (kind check: contains, not_contains, matches a regex, valid_json, max_chars, equals_expected). Wr

• caliper_evals_create

  Binds a dataset + rubric to something under test as a repeatable eval — THE LAST SETUP STEP before scoring. Two targets: a Workbench flow (pass flowId; Caliper runs inference itself, then caliper_evals_run scores it), or 'external' (target: 'external'; the user's own model runs e

• comments_list

  Lists the comment threads on one workspace entity (open first, then resolved) with authors and timestamps. Read this before weighing in on contested work — the threads are where disagreement lives before it becomes a decision.

• comments_create

  Posts a comment on a workspace entity — a new thread, or a reply when rootId is given. Use it to leave findings where the discussion already lives (an eval result on the flow being debated, a summary on a long thread). Mention people via mentionedUserIds (from workspace member id

• comments_resolve

  Sets a comment thread's resolved state (rootId = the thread's root comment id). Resolve ONLY when the human asked or the thread's question is demonstrably settled — and say what settled it in a reply first. Reopening is for new evidence.

• search_workspace

  Finds anything in the workspace in one call — uploaded files (searching the text inside them), folders, Workbench flows, shims, knowledge bases and tasks, Compass pages and changes, Caliper datasets, evals, rubrics, reviews, specs and sources, Ledger entries, Napkin sketches, dec

• entity_tags_get

  Returns the tags on a batch of entities of one kind — the labels galleries organize by. Ids come from the kind's list/get tool or from search_workspace. Use it before entity_tags_set so you replace the full set knowingly, and to answer 'what is this filed under'. Entities the use

• entity_tags_browse

  Without a tag: every tag in use across the workspace with how many entities carry it, most-used first — the vocabulary the team already organizes by. With a tag: everything filed under it across every tool, each with its kind, id, title, and path. Use it to reuse existing labels

• entity_tags_set

  Replaces the FULL tag set on one entity (an empty list clears it). Read the current tags with entity_tags_get first and pass the merged list — this is not additive. Tags are lowercase letters, numbers, spaces, and hyphens; prefer labels already in use (entity_tags_browse) so the

基础检查i

我们检查的是任何人都能看到的信息；我们没有阅读代码。不适用的检查项不计入总数。 · 检查于 2026年11月8日

• ! 注意

  作者与仓库一致

  注册中心没有列出源代码仓库，因此无法将发布者（zerowidth.ai）与代码对应起来

• … 尚未检查

  仍在维护

  我们还没有从 GitHub 读取这个仓库

• … 尚未检查

  有许可证

  我们还没有从 GitHub 读取这个仓库

• ✓ 通过

  端点有响应

  在 1645 毫秒内响应（HTTP 200）

• – 不适用

  使用 OAuth 登录

  无需登录

• ✓ 通过

  工具描述中没有隐藏指令

  已读取 45 个工具描述，未发现针对 AI 的隐藏指令

与“数据库与数据集”中的其他服务器比较i

• 星标数多于此分类中其他 1144 个服务器的 0%
• 发布到注册中心的时间比此分类中其他 1144 个服务器的 61% 更新
• 基础检查结果等于或优于此分类中其他 1144 个服务器的 40%

与注册中心里这个分类中所有活跃的服务器比较。只有当发布者拥有该仓库时，星标数才计入。

替代选择

同一分类中其他作者的服务器，按星标数排序。

如何看卡片：↓ 每周下载量i★ GitHub 星标i三大支柱：评价 · 检查 · 使用情况i更新 = 最新版本发布到注册中心的时间i

作者的 README

仓库中没有找到 README