mcpField
T
tablestakes

tablestakes

Local
github.com/oborchersProductivity1Updated 4d ago

About tablestakes

tablestakes is an MCP server for LLMs to read and edit tables in Markdown documents, especially HTML tables produced by GitBook syncs. It presents tables in compact pipe format and preserves their original format and attributes when writing changes.

Key features

  1. 01Scan documents and list tables
  2. 02Read tables in pipe format
  3. 03Batch cell updates
  4. 04Insert or delete rows and columns
  5. 05Prevent stale writes with version hashes
  6. 06Preserve HTML format and inline styling

Use cases

  1. 01Inspect tables in GitBook-synced documents
  2. 02Update selected cells in Markdown tables
  3. 03Edit CMS or Notion exports containing HTML tables

MCP support

The MCP server provides tools for discovering, reading, and editing tables in GFM, collapsed GitBook HTML, and general HTML formats.

Configuration

Local server config is the same for every client. Copy it into your client’s MCP config file.

mcp.json · Universal config
{
"mcpServers": {
"oborchers-tablestakes": {
"command": "uvx",
"args": [
"tablestakes"
]
}
}
}
Run commanduvx tablestakes

Local servers need a runtime on your machine (e.g. Node.js 18+). Config file locations differ by client; see your client’s docs.

Packages
pypitablestakes1.1.0

Tools

11

Saved tool list · updated 2w ago

ParameterTypeRequiredDescription
updatesarrayYesList of {row: int, column: str, value: str} objects.
versionstringYes12-char hex hash from read_table (after "v:", e.g. "a1b2c3d4e5f6").
file_pathstringYesAbsolute path to the Markdown file.
table_indexintegerYes0-based table index from list_tables.

Details

From the project 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:

<table><thead><tr><th width="520.11">Requirement</th><th width="122.07">Priority</th><th>Priority 1-2-3</th></tr></thead><tbody><tr><td><strong>1.1</strong> Agent sees only their Salesforce-assigned cases <strong>in the currently selected organization</strong> (case is "assigned" when SF <code>Case.OwnerId</code> matches the agent's linked SF user ID)...</td><td>Must</td><td>1</td></tr></tbody></table>

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:

26 tables

T0 pipe 5r 3c v:485f65f7b470 [Cross-Domain Dependencies]
  A:Integration | B:Source | C:Requirements

T2 gitbook 18r 3c v:77a9495fd328 [Case List]
  A:Requirement | B:Priority | C:Priority 1-2-3

T7 gitbook 3r 4c v:d9a9a45a370f [Attachments]
  A:Requirement | B:Priority | C:Dependency | D:Priority 1-2-3

Read — collapsed HTML becomes a clean pipe table:

v:d9a9a45a370f gitbook 3r 4c [Attachments]
A:Requirement | B:Priority | C:Dependency | D:Priority 1-2-3
| Requirement | Priority | Dependency | Priority 1-2-3 |
| --- | --- | --- | --- |
| **5.1** View inbound attachments in-app... | Must | — | 1 |
| **5.2** Send outbound attachments... | Must | Blocked on SF API | 1 |
| **5.3** Attachment file size limits... | Should | — |  |

Write — surgical cell edit, version-checked:

v:5749c94ffb1f

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).

OperationRead + EdittablestakesSavings
list_tables (26 HTML tables)~28,400 tokens~2,500 tokens91%
read_table (18-row HTML)~1,100 tokens~690 tokens39%
read_table (18-row GFM)~780 tokens~690 tokens11%
Cell edit (18-row HTML)~35 tokens~27 tokens23%
Cell edit (18-row GFM)~99 tokens~27 tokens73%
10-edit workflow (HTML)~1,470 tokens~960 tokens35%

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 adds cat -n line-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_string is 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_tables returns 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:

preview_rowsTokensSavings
0 (metadata only)~1,23096%
1 (default)~2,53091%
2~3,51088%
3~4,42084%

Reproduce: uv run --with tiktoken python scripts/script.py

Quick Start

Claude Code:

claude mcp add tablestakes -- uvx tablestakes

Codex CLI:

codex mcp add tablestakes -- uvx tablestakes

Gemini CLI:

gemini mcp add tablestakes -- uvx tablestakes

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:

{
  "mcpServers": {
    "tablestakes": {
      "command": "uvx",
      "args": ["tablestakes"]
    }
  }
}
ClientConfig file
Cursor.cursor/mcp.json
Windsurf~/.codeium/windsurf/mcp_config.json
Claude Desktopclaude_desktop_config.json

Tools

Discovery & Read
ToolPurpose
list_tables(file_path, preview_rows=1)Scan file, return all tables with metadata + preview
read_table(file_path, table_index)Full table normalized to pipe format + version hash
Cell, Row & Column Operations
ToolPurpose
update_cells(file_path, table_index, version, updates)Batch {row, column, value} patches
insert_row(file_path, table_index, version, position, values)Insert row at position (-1 to append)
delete_row(file_path, table_index, version, row_index)Remove row by index
insert_column(file_path, table_index, version, name, ...)Insert column with default value
delete_column(file_path, table_index, version, column)Remove column
rename_column(file_path, table_index, version, old_name, new_name)Rename header
replace_table(file_path, table_index, version, new_content)Full table replacement from pipe input
create_table(file_path, content, position, format)Create new table from pipe input (default: HTML)

All write tools require a version hash from read_table — optimistic concurrency that prevents stale overwrites without locks.

Supported Table Formats

FormatReadWriteRound-trip
GFM pipe tablesPass-throughIn-place editLossless
GitBook collapsed HTMLHTML → pipePipe → collapsed HTMLPreserves width, data-*, inline formatting
General HTML tablesHTML → pipe or pretty HTMLReconstructs HTMLPreserves structure

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

make init      # First-time setup: venv + deps + pre-commit hooks
make check     # All checks: format + lint + typecheck + test
make test      # Run tests only
make test-cov  # Tests with coverage report

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

Comments

Markdown supported

New to MCP?

From what the protocol is to installing your first server, these answers cover most first questions.

An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.