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Skeletable MCP Server

answering

Skeletable is answering right now. Last checked 3 min ago. It exposes 16 tools. Last commit 2 Nov 2026.

Source-backed public tables with citations, claim checks, and reviewable corrections.

Uptime history 21 hours of history
21 hours agonow
100.0%
Uptime 24h
79 of 79 checks
16
Tools
read from the server
267 ms
Response time
average over 24h
0
Stars
last commit 2 Nov 2026

What changed 22

Every tool that appeared, vanished or quietly changed what it asks for. Recorded since 2 November 2026. No other catalogue keeps this.

2 Nov 6 tools changed the parameters they ask for9 times that day add_columns, add_rows, create_table and 3 more
2 Nov 4 tool descriptions were rewritten7 times that day add_rows, create_table, fill_cells and 1 more
2 Nov 6 tools appeared data_link, fill_ai_cells, fork_table and 3 more
and 6 more, back to 2 November 2026

Nothing serious here today

Today is the operative word: we check Skeletable every 15 minutes and re-read its code on every release. Watch it and you find out the day that stops being true.

Three servers free · no card

Connect this server

Endpoint below is the one we actually reach during checks — not the one copied from a README. Last verified 3 min ago.

run in your terminal
claude mcp add skeletable --transport http https://skeletable.com/mcp
Settings → Connectors → Add custom connector, then paste this address
https://skeletable.com/mcp
~/.codex/config.toml
[mcp_servers.skeletable]
url = "https://skeletable.com/mcp"
.cursor/mcp.json
{
  "mcpServers": {
    "skeletable": {
      "url": "https://skeletable.com/mcp"
    }
  }
}
.vscode/mcp.json
{
  "servers": {
    "skeletable": {
      "type": "http",
      "url": "https://skeletable.com/mcp"
    }
  }
}

Available tools 16

Read directly from the server with tools/list, grouped by what they act on. If a tool disappears, we record the date.

fill
fill_ai_cells
Save up to 200 answers to an AI column, each with the `row` and `basis` next_ai_cells gave it. An answer outside the column's choices, or to a prompt that has since changed, comes back with a reason. Each value is saved as generated, with the instruction and `model`. Needs a token.
fill_cells
Set up to 200 cells of a table you run. Usually a cell gives `value` and `origin` (`entered`, `imported` with a `note` like the file or email it came from, `link` with its `source`, or `generated`). Left out, the origin is `link` when there's a `source` and `generated` when there isn't. These can go into a published table too, and never replace a quoted value. A date is written YYYY-MM-DD, and a column with choices takes only one of them. In a table that requires proof, a cell instead gives `value`, `source` and `quote`, word for word from the source; each page is opened before it's saved, so send at most 25 of these at once, and only into a draft. If no acceptable source states it, give `checked` with the pages you read, and `none_stated` only if you read the whole source and it has no such value. The reply counts what saved and lists only the cells that didn't, with a reason, and `unchecked` names quoted values whose page couldn't be read. Needs a token.
table
create_table
Make a private draft table, ready to fill, for anything from a tracker to a dataset. Its values say where they came from unless you set `proof` to `required`. Only `title`, `columns` and `rows` are needed now, but give `question`, `scope`, and a `rule` for each column if you can: a person has to supply them before publishing. Returns the column ids fill_cells needs, and the sheet's link, so tell your person to open it and watch it fill. Needs a token.
get_table
Read a table: its scope, its columns with the rule for each, a summary of what each column holds, and its rows. A null value is blank. `origins` says where a row's unquoted values came from, `ai` says how far each AI column has got, and `versions` lists saved versions. For a big table, read the summaries first and page through rows with `offset` and `limit`.
cell
get_cell
The evidence for one value: the source, the exact quote, what kind of source it is and when it was read. For a missing value, where we looked, and `absent` means the source was read and gives no such value. `url` is the cell's lasting address, showing its proof, for citing. `confirmed` says how a second reader backed it up, and without it the value has been read once. `conflicts` lists sources that state something else, `history` lists past values, and `watch` says whether the quote was still on its page at the last check.
claim
check_claim
Test a statement against one cell, such as whether a value in a draft or an answer is right. The verdict is `matches`, `differs` (with what the source states), `disputed` (sources disagree, see `conflicts`) or `unknown` (no acceptable source stated it, so say so). It compares values and doesn't judge meaning, so read the quote before relying on a text verdict. Use list_tables and get_table to find the cell first.
columns
add_columns
Add columns to a draft you run, such as a blank one a person started. Returns the new column ids for fill_cells. Needs a token.
data
data_link
A private address that loads a table you run as CSV, for a notebook, a script or a spreadsheet, with the lines to do it. Add `?columns=a,b` to pick columns and `?names=0` to leave out row names. Anyone with the link can read the data, and `fresh` makes a new one so the old one stops working. Needs a token.
export
export_table
A table as CSV, ready to save as a file or load into a notebook. The first column is each row's name unless `names` is false. `columns` picks columns by id, in that order. With `proof`, each column is followed by its source and quote, and in a table whose proof is optional, by where each value came from. An empty field means no value.
fork
fork_table
Copy a table into your own account as a private draft, to extend or relabel: any published public table, or one you run. Every value keeps its proof or origin, and the copy remembers where it came from. Needs a token.
instruction
set_instruction
Make a column an AI column, change its instruction, or turn it back into a plain column with an empty one. The instruction says what to work out for each row and names other columns in braces, like "Classify {Pitch} by genre". Its values never overwrite what a person typed, linked or quoted. Needs proof to be optional and a token.
next
next_ai_cells
The next prompts to answer for a table's AI columns, up to 200 at a time: blank cells first, then answers whose row or instruction has changed. Answer each prompt yourself, then send the answers back with fill_ai_cells, each with its `row` and `basis`. Repeat until it says `done`. `redo` also re-answers generated values that came from outside the instruction. Needs a token.
propose
propose_change
Suggest a new value for a cell, with its proof. The quote must be word for word from the source and state the value. The owner reviews it before anything changes. Needs a token sent as a Bearer token.
rows
add_rows
Add up to 500 rows to a table you run. Each row is a name, or an object with its `values` by column id or label, so a whole tracker or dataset goes in a few calls. A row without a `key` gets the next free number. A name the table already has gets the values given for it, so syncing again from the same source is one call; with no values it's skipped. Say where the values came from with `origin` (`entered`, `imported` with a `note` naming the file or email, `link` with a `source`, or `generated`, which is the default without a source). Rows with values can go into a published table too; rows by name only into a draft. A table that requires proof takes rows by name only. Needs a token.
save
save_version
Freeze a table you run as it stands now, as a numbered version with a lasting link that never changes. Do this before training on a dataset or citing a table, and name the version you used. Needs a token.
tables
list_tables
Find tables in the library. Each result gives its slug, the question it answers, its size and when it was last read.

Endpoints

URLTransportStateLatencyChecked
https://skeletable.com/mcp streamable-http answering 245 ms 3 min ago

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Skeletable — questions

Answers built from our own checks of this server.

What can Skeletable do?
It exposes 16 tools, read directly from the server on our last check. Among them: add_columns, add_rows, check_claim, create_table, data_link, export_table and 10 more. The full list with descriptions is on this page — we take it from the server itself via tools/list, not from a README. How MCP servers expose tools in the first place →
What is Skeletable mostly used for?
Its tools cluster around fill and table. That is what this server is built to work with — the grouping comes from the actual tool names, not from a category we assigned.
Is Skeletable working right now?
We send a real MCP handshake every 15 minutes. Over the last 24 hours 79 of 79 checks got a reply (100.0%), average response time 267 ms. The bar chart above shows every period we have measured.
How do I connect Skeletable?
Copy the ready config from this page. We generate it for Claude Code, Claude Desktop, Codex, Cursor and VS Code, each in the form that client actually reads. It is a remote server, so there is nothing to install — the client connects to the address.
Does Skeletable need an API key?
No. Skeletable completed a full MCP handshake with us as an anonymous client and listed its tools without asking for anything. All 16 of them are readable on this page. This is what we observed, not what the docs claim.
How fast is Skeletable?
It answers our handshake in 267 ms on average, which is faster than 53% of all working MCP servers we measure. The comparison comes from our own checks across the whole registry, every 15 minutes.
Is Skeletable open source?
The code is open source: it is published under the Apache-2.0 licence, written in TypeScript and 0 stars on GitHub. The licence covers the code in the repository, not the hosted service at skeletable.com, which runs on its operator's own terms.