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ZeroWidth MCP server

Which servers in the official MCP registry are ZeroWidth's own, whether they answer, and what they offer. A program checked each of these, nobody copied them from a product page.

read 2026-10-24

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Whose server it is
  • Registry nameai.zerowidth/caliperverified in the MCP registry

The registry name sits under zerowidth.ai. The registry only lets a publisher use that namespace after proving control of the domain through DNS or HTTP.

When we asked itchecked 2026-10-24
  • Stateanswerschecked 2026-10-24
  • Tools45
  • Server calls itselfzerowidth-mcp
  • Server version1.0.0
  • Protocol version2025-06-18

On 2026-10-24 the server answered initialize and tools/list without a sign-in. First answer in 213 ms.

Should an agent connect itNot proven either way yet
  • Whether its tools do the product's job has not been judged yet.
  • Checked on 1 of the 7 days it takes to judge reliability.

Once connected

  • comments_create change data without a sign-in: anyone with the address can call them.
  • Its definitions take about 15643 tokens of context: enable only the tools a task needs.

Said by rules from the measurements above. The likely first call is a model's pick from the tool list; read its description before relying on it.

Measured dailydaily checks: 1, the first on 2026-10-24
  • INITE MCP scoreafter a weeka score needs 7 daily checks: 1 so far
  • Answeredmeasuring1 of the 7 daily checks it takes to print a share
  • Median first answer264 ms
  • Space in an agent's context≈ 15,643 tokensthe definitions of all its tools (45), which an agent carries on every turn
  • Tool definitionsA · 97 of 100
  • Typed results0 of 45 toolsdeclared outputSchema: results come typed, read once
  • Parameters without a description: in 8 of 45

Worth a look

  • Tools that change data, on a server that answers without a sign-in: comments_create

A program checks the server once a day: initialize and tools/list, no model. Tokens are the definitions' length at about four characters a token. The grade averages six checks of every tool's name, description, schema and annotations; it says how well the tools are described, not whether they work. The score weighs what was measured and nothing else: answering (30%), doing the product's job (25%), definitions (20%), context (10%) and use (15%). A part that was not measured is left out, and a hidden instruction caps the score at 40. The state of vendors' MCP servers: method and numbers →

Tools it offers45 tools · read 2026-10-24
  • 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…

  • 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)…

  • 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…

  • 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…

  • 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…

  • 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…

  • 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…

  • 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_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…

  • 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…

  • 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…

  • 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…

  • 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…

  • 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…

  • 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…

  • 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…

  • 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…

  • 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…

  • 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…

  • 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_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…

  • 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…

  • 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…

  • 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…

  • 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…

  • 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)…

  • 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…

  • 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'…

  • 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…

  • 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…

  • 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…

  • 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…

  • 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…

How to connect

Add the server to your MCP client config. Claude Desktop, Cursor and most other clients read this shape:

{
  "mcpServers": {
    "caliper": {
      "type": "http",
      "url": "https://api.zerowidth.ai/mcp/x/caliper"
    }
  }
}

Add it

Claude Code, in a terminal

claude mcp add --transport http zerowidth https://api.zerowidth.ai/mcp/x/caliper

Add to CursorAdd to VS Code

claude.ai, ChatGPT and other clients: add https://api.zerowidth.ai/mcp/x/caliper as a custom connector in the settings.

Other servers listed as ZeroWidth's
  • ai.zerowidth/compassanswers, 62 toolsverified in the MCP registry
  • ai.zerowidth/ledgeranswers, 40 toolsverified in the MCP registry
  • ai.zerowidth/napkinnot asked yetverified in the MCP registry
  • ai.zerowidth/prismnot asked yetverified in the MCP registry
  • ai.zerowidth/workbenchnot asked yetverified in the MCP registry
  • ai.zerowidth/zerowidthnot asked yetverified in the MCP registry

Each line is something a program found when it went looking: the registry entry, the server's own answer, a spec that parses. A missing line means we did not find it, not that it does not exist.