Mostly Right

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

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？

https://mostlyright.md/api/mcp

claude mcp add --transport http datasets https://mostlyright.md/api/mcp

{
  "mcpServers": {
    "datasets": {
      "url": "https://mostlyright.md/api/mcp"
    }
  }
}

{
  "servers": {
    "datasets": {
      "type": "http",
      "url": "https://mostlyright.md/api/mcp"
    }
  }
}

https://mostlyright.md/api/mcp

你的选择通过 Cookie 保存在这台设备上，无需账户。

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9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_datasets

  Full-text search over every public dataset on Mostly Right. No account needed. Example: {"query": "hourly airport weather observations", "limit": 10}. Returns {datasets: [{id, slug, title, summary, topics, publisher, published_at, canonical_url}], next_cursor}. publisher is the A

• get_dataset

  One dataset's overview: title, summary, topics, publisher, publication and update dates, canonical page URL, star and table counts, and up to 50 tables with their id, title, license, immutable version_id, column count, column names and the capabilities the publisher enabled. Here

• list_tables

  The tables in one dataset, without the full column schemas — the cheap call when you only need table ids and titles. Example: {"dataset_slug": "kden-metar-hourly"}. Returns {dataset_slug, main_table, tables: [{id, slug, title, display_title?, description?, section?, license, vers

• get_table_schema

  One public table's columns (name, type, and any published profile such as null counts, distinct counts or ranges), its immutable version_id, and its capabilities: whether anonymous sampling, keyed querying and Parquet download are enabled, and, when the publisher declared text se

• sample_rows

  The publisher's materialized preview of a public table — real rows, no account, no query cost. Private workspace tables are not available here; use query_run on a successful build instead. To sample a source before anything is built, use probe_source (kind sample_rows). 20 rows b

• get_download_instructions

  The exact URL, HTTP method and header for downloading one table's current immutable Parquet snapshot, plus whether the publisher enabled it. Example: {"table_id": "0f2f6bfa-4a63-4f75-9a0b-1a7d9c5b2e10"}. Bytes are never streamed through MCP: this returns the request to make yours

• get_access_instructions

  What this server does anonymously, what needs an account, the two API key classes and what each unlocks, the OAuth option, the signup URL, and the numbered steps from 'no account' to 'querying rows'. Optionally pass {"dataset_slug": "kden-metar-hourly"} to get the link to that da

• search

  Search every public dataset on Mostly Right and return up to 20 matches as {results: [{id, title, url}]}, where `id` is the dataset slug and `url` its canonical page. No account needed. Example: {"query": "hourly airport weather observations"}. Pass a result's `id` straight to fe

• fetch

  The full public document for one dataset as Markdown: summary, facts, access instructions, and every table with its columns, types, descriptions and units. No account needed. Example: {"id": "kden-metar-hourly"} — the `id` is a slug from search, and a canonical dataset URL works

• query_table

  A bounded, structured query over one table's current version. NO SQL: send columns, filters, order_by, aggregates, group_by and limit as JSON. Example: {"table_id": "0f2f...", "columns": ["observed_at", "air_temp_f"], "filters": [{"column": "air_temp_f", "operator": "gte", "value

• search_table

  Ranked search over one public table's rows, the same search its dataset page runs: words, prefixes, typos and spelling correction, served from the table's search package. NOT a catalogue search (search_datasets finds datasets) and not SQL. Example: {"table_id": "0f2f...", "query"

• catalog_search

  Search a sealed snapshot of public data sources for feeds that might answer a question — the first move when you need a source and do not already know one. Example: {"question": "county unemployment rate monthly", "limit": 10, "format": "csv"}. READ THIS BEFORE YOU TRUST A RESULT

• list_connected_datasets

  The public datasets the authenticated workspace has used so far. Takes no arguments. query_table is not limited to this list: a key reads any public dataset (connecting it on first use) and the workspace's own tables; an OAuth connection reads any public dataset it has datasets:u

• connect_dataset

  Connects one public dataset to the authenticated workspace so its tables become queryable and downloadable. This is the gate every keyed read passes. Example: {"dataset_slug": "kden-metar-hourly"}. Idempotent: connecting an already-connected dataset succeeds and reports already_c

• create_dataset

  Creates an empty dataset in your workspace and returns its ids. A dataset is the container a recipe binds to; it exists before it has a description, a recipe or a single row, which is the point — the page opens on it and fills in. The name leads with the subject a searcher would

• configure_dataset_layout

  Save ordered sections, table display titles and descriptions, collapsed sections and a separate default opening table. Read get_my_dataset for dataset_layout and its revision; use expected_revision 0 only when absent. For first creation also send expected_main_table_id from get_m

• configure_table_view

  Saves one versioned contextual view for this table after its first successful build. Inspect its intent, actual schema and a bounded sample first. Prefer table for wide numeric/comparison data or when a specialized template does not fit. Otherwise bind exact column names to appro

• update_dataset

  main_table_id names the table the dataset page leads with: the Data tab opens on it and the other tables keep their configured sections, or are listed as helpers when no layout is configured. Use configure_dataset_layout for readable titles and custom sections/order. Set it once

• generate_cover

  Generates the dataset's branded 1200×630 cover and attaches it: the page, the Community tile and the share card all wear it. Every dataset gets exactly one, generated once its description is written. Call it only for a dataset that has no cover (get_my_dataset says has_cover). A

• set_dataset_visibility

  Changes who can read a dataset: private (this workspace only), link (anyone holding an unlisted address, which this returns) or public (listed on Mostly Right for anyone). The same change as Share on the dataset's page and `mr-data dataset publish`, with the same checks and audit

• archive_dataset

  Retires a whole dataset: every reader loses access, its public page and unlisted link are withdrawn, and its name is free for a new dataset. Nothing it built is deleted, but ONLY Mostly Right support can restore it. The same act as the settings page's delete and `mr-data dataset

• list_my_datasets

  Every dataset this workspace owns, most recently updated first — not the public catalog. Use it to find the dataset_id for a dataset you or a colleague created earlier. Example: {"limit": 25}. Returns {workspace_id, datasets: [{dataset_id, cloud_dataset_id, name, description, upd

• get_my_dataset

  One workspace dataset in build terms: name, description, status, each table with its build state, promotion state and live version, the latest run, and any run held waiting for a confirmation. Example: {"dataset_id": "…"}. Returns {dataset_id, name, description, category_id (its

• list_dataset_events

  The dataset's own durable log, newest first: created, renamed or re-described, a recipe registered, each run queued, held, started, progressing and finished, a cover attached, a table's state changing, a table renamed or archived, a stream's state moving, a note written and each

• list_recipes

  Lists immutable recipe coordinates in this workspace, optionally for one table_id from get_my_dataset. current_only defaults to true; false includes older revisions. Returns {recipes: [{recipe_id, recipe_digest, dataset_id, table_id, is_current}], next_cursor}. Follow next_cursor

• get_recipe

  Reads the actual immutable registered recipe, including sources, SQL and checks. Use list_recipes for current table revisions, or recipe_id from get_run for the exact executed revision. Returns {recipe_id, recipe_digest, dataset_id, table_id, pointer, value, complete: true} when

• register_recipe

  Registers the JSON recipe that says where the data comes from, how it is shaped and what must be true of it. The server canonicalizes the document and computes its digest — you cannot and must not state one. Identical bytes register once: a repeat returns the same ids. Example: {

• explain_recipe

  Checks a recipe document without registering, writing or running anything — the same evaluator that admits refreshes, plus the fast-path features the harness's `recipe explain` reports. Example: {"recipe": { …the whole recipe document… }}. Returns {refresh_ready, first_blocker, a

• forecast_recipe

  Says what one full or refresh run of a registered recipe would ask of its sources, before anything is started: per-source request ceilings, runtime at the ceiling, platform spend, and the projected bytes and cost. Warnings name what would slow or block the run. Example: {"recipe_

• test_recipe

  Starts a recipe preflight: a bounded rehearsal of a full run that fetches at most max_pages_per_source pages of each listing (default 1, max 5) and max_upstream_rows rows of each per-row source (default 25), runs the transform and the checks a slice can answer, and reports. Sourc

• propose_revision

  Registers a proposed revision and returns its immutable recipe coordinates plus a review link. It then starts EXACTLY ONE run: a replay of the named successful run's retained inputs, which reads no upstream source and never becomes live. Nothing else runs — it confirms no run, pr

• replay_run

  Runs a registered recipe revision against the retained raw inputs of a named successful run of the same table. No upstream acquisition is performed. Replay must be enabled and the inputs must still be retained. Returns {run_id, status, mode, version, dashboard_url}. Replay never

• start_run

  Runs a registered recipe. THIS IS THE TOOL THAT DOES THE REAL WORK: it reads the sources and can run for a long time. Start builds in full mode: full runs use one progressive acquisition with an inspection checkpoint after about five minutes and continue automatically. The checkp

• confirm_run

  Authorizes a held run and starts it. The numbers are the ones already stored on the run — you cannot restate them, which is the point: what is authorized is the server's own projection. Example: {"run_id": "…", "expected_version": 3}. Pass expected_version from the get_run that s

• approve_full_run

  Compatibility for an existing legacy full run paired with a bounded preview, in awaiting_sample_approval. New full builds use progressive acquisition and never need this tool for their inspection checkpoint. Call it ONLY once the legacy preview has sealed a table you have checked

• cancel_run

  Stops a run that is queued, held at the confirmation gate, or running. A run cancelled at the gate never ran. Example: {"run_id": "…"}. Returns the run receipt with status cancelled. A run that already finished cannot be cancelled and says so — read get_run rather than retrying.

• get_run

  One run's current state: status, mode, the clamps it ran under, rows delivered and whether a clamp truncated them, how many pages a many-page source reached, the table and table version it sealed, the failure code and stage when it failed, and the version number a confirm or canc

• run_events

  Opens the run's event stream, collects up to max_events, and returns as soon as the run reaches a terminal event or wait_seconds elapses — whichever comes first. This is how you watch a build without polling. Example: {"run_id": "…", "from_seq": 0, "max_events": 50, "wait_seconds

• list_runs

  The workspace's own runs, newest first, optionally narrowed to one dataset, one status or one mode. Example: {"dataset_id": "…", "status": "failed", "limit": 20}. Returns {runs: [{run_id, status, mode, dataset_id, table_id, created_at, completed_at, failure_code}], count, complet

• compare_runs

  Reads two runs and says what changed between them: status, mode, rows, bytes, truncated, covered_window, table_version_id, outcome, failure code and stage, created and completed times, and each declared check whose result moved. Use it to tell whether a revision or a refresh deli

• query_run

  Runs one read-only SQL statement over the parquet a run sealed, available as the relation run_table (not the recipe table name), and waits for the answer. THIS IS HOW YOU CHECK THE DATA IS RIGHT after a successful run: count the rows, look at the range, find the nulls. Example: "

• sample_table

  Reads up to limit rows (default $DEFAULT_SEARCH_READ_ROWS, at most $MAX_SEARCH_SAMPLE_ROWS) of one sealed table version, projected to columns (default: every declared column, up to 64). Use it before writing a search spec or a plan, to see real values; it needs no SQL and no run

• list_column_values

  The most frequent non-null values of one declared column of a sealed table version, each with the number of rows holding it, most frequent first. prefix keeps the values whose text starts with it, ignoring case. Use it to choose filterable columns, check a value dictionary or wri

• preview_table_search

  Runs one query (words, planned under the revision's spec as explain plans them) or one explicit table-search-plan.v2 plan as a SCAN of the sealed version: the plan's predicates and order as filters, its text as a case-insensitive substring match over the keyword columns, unranked

• run_artifacts

  The files a run sealed: the parquet, the column profile, the receipt, the preview. Works on a running, succeeded or failed run — a failed run's partial output is listed too. Example: {"run_id": "…"}. Returns {artifacts: [{artifact_id, kind, size_bytes, digest, media_type}], more}

• get_artifact_download

  Mints a short-lived signed URL for one sealed artifact. Fetch it yourself with a plain GET and no headers; it expires. Example: {"run_id": "…", "artifact_id": "…"}. Returns {url, expires_at, media_type, digest, size_bytes, kind}. Cite the digest. Artifact ids come from run_artifa

• write_note

  Appends one cell to a run's or a dataset's decision record — why a source was chosen, what a check found, what you changed and why. The record is append-only and is what a reader sees beside the data. Write one for every decision worth explaining. Example: {"run_id": "…", "headin

• list_notes

  Lists the cells of a run's or a dataset's decision record, newest first: the heading, the body, the phase, any typed blocks (source, decision, clarification) and whether a later revision superseded it. Read it before writing a note, so a decision already recorded is revised rathe

• list_table_parts

  Lists the parts (parquet files) one table version is made of: each part's index, part_id (its content digest), artifact_id, the run that sealed it, its row count, its size, its partition, and the lowest and highest value it holds per field (lower_bounds, upper_bounds, keyed by fi

• verify_run

  Checks the table one succeeded run sealed against its own manifest. Without deep it reads the manifest, checks it hashes to the digest it was sealed with and names this run's table version, and checks every part the run sealed is among the parts the run lists; no part is fetched.

• promote_table

  Promotion makes the table's newest passing version the version everyone reads, and starts the recurring refresh that keeps it current. Example: {"table_id": "…", "confirm": true}. It refuses without confirm: true. Only call it when the person you are working for has said to. Chec

• reschedule_table

  Sets a live table's refresh schedule, or replaces the one it has, without starting a run or fetching anything. A table that went live on its first run has NO schedule yet (get_table shows schedule null, the chat shows "No refresh schedule"): this is the tool that gives it one. Wh

• demote_table

  Withdraws a table's live pointer and detaches its refresh schedule: readers stop being served it and it stops refreshing. Nothing built is deleted, and promote_table later revives the same table. A live table must be demoted before archive_table will retire it. Example: {"table_i

• refresh_table

  Runs the refresh the table's schedule would run, now: forward from where its last successful run reached, under its own recipe and the ordinary size and spend gates. It never rereads every source; a table whose recipe cannot refresh incrementally answers incremental_unavailable,

• resync_table

  Rereads every source of a live table as one progressive full run of its current recipe, the deliberate escape hatch when a source cannot advance incrementally (refresh_table answered incremental_unavailable, or the person asked to rebuild from the sources). It costs a full build.

• archive_table

  Retires one table from its dataset: it leaves the dataset's table list and frees its name for a new table. The versions it built are not deleted. The service confirms the archive with the table's current stored name itself, so you never send one. Example: {"table_id": "…"}. A liv

• rename_table

  Changes one table's name within its dataset. Nothing is rebuilt, re-served or republished, and the table keeps its id, versions and schedule; every reader of the dataset sees the new name. The old name is released, so another table may take it, and a later recipe revision must na

• get_table

  One table as the builder sees it: whether it is promoted, which version is live, the refresh schedule and what the platform has learned about its rhythm, when it last refreshed and why it last failed. Example: {"table_id": "…"}. Returns {table_id, dataset_id, promotion_status, li

• diagnose_table

  Reads one table's current promotion pointers and latest recorded failure. When that failure names a run, it also reads that run and its declared recipe sources, so the diagnosis still works when a failed run never sealed a version. Example: {"table_id": "…"}. Returns {table, late

• list_source_credentials

  Lists named secret metadata and saved workspace connections without secret values. Pass dataset_id to check dataset grants. Follow connections.next_offset with connection_offset to read another bounded page. Returns credentials, connections (status, safe items, grants_status), an

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10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（mostlyright.md）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 338 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 60 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 1157 个服务器的 0%
• 发布到注册中心的时间比此分类中其他 1157 个服务器的 32% 更新
• 基础检查结果等于或优于此分类中其他 1157 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