
Tablestakes
io.github.oborchers由 GitHub 账号 oborchers 发布v1.1.0更新于 Oct 24, 2026
Read and edit HTML/Markdown tables in GitBook-synced documents via MCP tools.
概览
一个 MCP 服务器,让助手列出、读取并精准编辑本地文件中的 HTML 与 Markdown 表格,把折叠的 GitBook HTML 转为管道表格并在保存时还原。
- 可以做什么
- 列出 Markdown 文件中的表格,返回元数据与预览行,并读取完整表格为整洁的管道表格及版本哈希
- 批量更新单元格,插入或删除行,插入、删除或重命名列
- 用管道输入整体替换表格,或新建 HTML 或管道格式的表格
- 支持 GitBook 折叠 HTML 与一般 HTML 表格的往返转换,保留 width、data 属性与行内格式
- 适用场景
- 适用于从 GitBook、Notion 或 CMS 同步的文档中表格被压成单行 HTML、难以阅读且编辑易出错的情况,希望以更省 token 且保留结构的方式修改表格,而不是整篇读取和重写文件。
- 运行要求
- 以 stdio 本地进程运行;可从 PyPI 安装(pip install tablestakes)或用 uvx tablestakes 启动。未声明账号、API 密钥或环境变量。需要访问被编辑的 Markdown 文件。
安装
在 SourceWeft 中
- 打开 控制台中的 Tablestakes,将其添加到工作区。
- 为需要使用其工具的对话启用该服务。
Desktop only,通过 STDIO。 STDIO 服务会启动本地进程,因此需要 SourceWeft 桌面宿主。
其他 MCP 客户端
参照 仓库 中的启动说明。
README
tablestakes
[PyPI version] [Python versions] [CI] [License]
An MCP server that gives LLMs clean, surgical access to tables trapped in messy HTML.
The Problem
Tools like GitBook, Notion exports, and CMS platforms collapse tables into single-line HTML when syncing to Markdown files. The result looks like this in your editor:
This is unreadable for humans and unreliable for LLMs. Models struggle to parse collapsed HTML tables, frequently hallucinate cell boundaries, and cannot edit them without corrupting the structure.
tablestakes fixes this. It sits between the LLM and the file, converting tables to clean pipe format on read and writing back in the original format on save — preserving GitBook compatibility, HTML attributes, and inline formatting.
What the LLM Sees
Discovery — scan a 26-table document in one call:
Read — collapsed HTML becomes a clean pipe table:
Write — surgical cell edit, version-checked:
14 characters. The file is updated, GitBook HTML format preserved, width attributes intact.
Token Efficiency
Baseline: Claude Code's built-in Read + Edit tools operating on the same file. Measured on a synthetic 18-row, 4-column table with realistic requirement-style content (bold IDs, inline emphasis, mixed-length cells).
Where the savings come from:
-
Read (HTML): collapsed HTML tags (
<td>,<tr>,<th>,<strong>,width="...") are pure overhead. Pipe tables carry the same information without markup. The Read tool also addscat -nline-number prefixes. -
Read (GFM): modest savings from stripping line-number prefixes and surrounding document context. The table content itself is already clean.
-
Write: the Edit tool requires
old_string(enough context to be unique in the file) +new_string(the modified version), both generated as output tokens. For GFM,old_stringis the entire row line (~190 chars). tablestakes needs only{"row": 0, "column": "B", "value": "Should"}(~18 tokens). -
Discovery: without tablestakes, the LLM reads the entire file to find tables.
list_tablesreturns a compact index — metadata + 1 preview row per table. -
Compact pipe tables with no column padding. Per the ImprovingAgents benchmark, GFM pipe tables achieve the best token-to-accuracy ratio: 1.24x CSV cost at 51.9% QA accuracy, beating JSON (2.08x, 52.3%) and YAML (1.88x, 54.7%).
Experiment details
Tokenizer: tiktoken cl100k_base (GPT-4). Claude uses a different tokenizer, but relative comparisons hold. The benchmark script (script.py) constructs tables programmatically and generates tablestakes output using the actual converter code — no hardcoded strings.
Read baseline: simulate_read_tool() wraps file content in cat -n format (line-number prefix per line), matching what Claude Code's Read tool returns. The full file (document text + table) enters the LLM context.
Write baseline: for each cell edit, the script computes the minimum unique old_string by expanding leftward from the target <td> until the substring is unique in the file. new_string is the same context with the cell value replaced. This is a best-case scenario for the Edit tool — a human might include more context than the minimum.
list_tables baseline: 26 copies of an 18-row GitBook HTML table in a markdown document. Naive = Read the full file (~28k tokens). tablestakes = list_tables output with preview_rows=0..3:
Reproduce: uv run --with tiktoken python scripts/script.py
Quick Start
Claude Code:
Codex CLI:
Gemini CLI:
Or install from PyPI directly: pip install tablestakes
Other clients (Cursor, Windsurf, Claude Desktop)
Add the following JSON to your client's MCP config file:
Tools
Discovery & Read
Cell, Row & Column Operations
All write tools require a version hash from read_table — optimistic concurrency that prevents stale overwrites without locks.
Supported Table Formats
While GitBook is the primary motivation, tablestakes works with any Markdown document containing HTML tables — CMS exports, Notion dumps, wiki migrations, or hand-written HTML in .md files.
Column Addressing
Columns can be referenced by:
- Letter:
"A","B","AA"(bijective base-26, like Excel) - Name:
"Priority"(must be unique) - Composite:
"B:Priority"(for disambiguation) - Index:
"0","1"(0-based)
Development
Author
Built by Dr. Oliver Borchers, Executive AI and Automation Partner and Head of AI Engineering at IU Group.
License
Apache-2.0
mcp-name: io.github.oborchers/tablestakes
来源:README.md,提交 d1b96b6
工具
0版本历史
1- v1.1.0最新Sep 16, 2026
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