Excel LSP
An MCP server that provides a semantic index and 14 bounded tools for AI agents to navigate Excel workbooks by symbols, references, and diagnostics, avoiding the need to read entire rows.
README
Excel LSP
<!-- mcp-name: io.github.Brody900/excel-lsp -->
An LSP for Excel: semantic index + MCP server so AI agents navigate workbooks by symbols, references, and diagnostics — not by reading 50,000 rows.
(LSP-style: the ideas — symbols, references, diagnostics, incremental index — not the LSP wire protocol.)
Measured release evidence: Excel LSP used 3,410 deterministic tool-result tokens versus 222,289 for naive dump—a 65.2× reduction—and answered 12/12 headless runs exactly versus 8/12. Full Codex CLI usage, which includes fixed agent context and schemas, is reported separately in the raw rows.
60-second lineage demo

The demo is assembled only from the numbered desktop-Excel captures. Its evidence manifest records every source hash, the output hash, dimensions, and frame duration; the complete live protocol index records the matching machine-readable assertions.
Install with your AI agent
Copy this prompt into Codex or another coding agent:
Download and install Excel LSP from https://github.com/Brody900/excel-lsp,
configure it as a local MCP server for this environment, and verify that the
server starts and exposes its tools. Follow the repository's Quickstart,
prefer the git-based uvx command until the PyPI package is available, preserve
unrelated project files, and report exactly what you changed and verified.
The agent can use the commands below directly. Excel LSP runs locally over stdio; installing it does not upload workbook data or start a network daemon.
Quickstart
Excel LSP requires Python 3.11 or newer and runs as a local stdio process. It does not require Microsoft Excel for indexing or editing OOXML files.
Run the MCP server
The verified public-repository install works now:
uvx --from git+https://github.com/Brody900/excel-lsp@main excel-lsp serve
After the PyPI publication is visible, the shorter equivalent is:
uvx excel-lsp serve
Add Excel LSP to Codex
codex mcp add excel-lsp -- uvx --from git+https://github.com/Brody900/excel-lsp@main excel-lsp serve
codex mcp get excel-lsp
Codex will launch the stdio server when it needs it; Excel LSP is not a network
daemon. The shorter PyPI registration is
codex mcp add excel-lsp -- uvx excel-lsp serve. That syntax was verified with
Codex CLI 0.144.5 in an isolated configuration home.
Configure Codex manually
Add the following entry to ~/.codex/config.toml:
[mcp_servers.excel-lsp]
command = "uvx"
args = ["--from", "git+https://github.com/Brody900/excel-lsp@main", "excel-lsp", "serve"]
A copy is available at examples/codex.config.toml.
<details> <summary>Generic <code>.mcp.json</code> for clients that use MCP JSON configuration</summary>
{
"mcpServers": {
"excel-lsp": {
"command": "uvx",
"args": ["--from", "git+https://github.com/Brody900/excel-lsp@main", "excel-lsp", "serve"]
}
}
}
This is a generic MCP-client example, not Codex's native configuration format.
It is also committed as examples/mcp.json.
</details>
Tools
The contracts below are frozen for v0.1.0 and verified by the completed P7 milestone. Exact generated schemas and one worked example per tool are in the tool reference.
| Tool | What it gives an agent |
|---|---|
open_workbook |
Index or refresh a workbook and return its compact semantic map. |
refresh |
Explicitly resynchronize the index and optionally clear recalculated staleness. |
list_symbols |
Search sheets, regions, columns, formula blocks, names, and cells by stable symbol ID. |
get_region_schema |
Inspect headers, types, validation, formula blocks, confidence, and bounded samples. |
read_range |
Read a small, paginated range with a hard limit of 200 values per response. |
find |
Search bounded snippets across values, headers, formulas, and defined names. |
trace_precedents |
Trace what a cell or symbol reads, with bounded depth and node counts. |
trace_dependents |
Trace what a cell or symbol can affect, with bounded depth and node counts. |
trace_path |
Explain bounded dependency paths between two cells or symbols. |
explain_formula |
Show A1/R1C1 forms, block membership, resolved references, flags, and diagnostics. |
get_diagnostics |
Filter formula and workbook diagnostics by sheet, severity, or code. |
profile_column |
Return bounded numeric statistics or top values for a region column. |
write_cells |
Surgically write bounded cell values or formulas without workbook round-tripping. |
set_column_formula |
Fill a region column formula with formula-block and staleness tracking. |
The first 12 tools are read tools. The final two are destructive write tools and carry the corresponding MCP annotations so clients can request confirmation.
Architecture
The workbook remains authoritative; the SQLite sidecar is a disposable derived index. Every transport operation delegates to the same core services.
flowchart LR
Workbook["Excel OOXML workbook"] --> Loader["Streaming OOXML loader"]
Loader --> Index["Persistent semantic index<br/>SQLite + R*Tree or interval fallback"]
Index --> Regions["Regions and symbols"]
Index --> Graph["Formula dependency graph"]
Graph --> Diagnostics["Diagnostics"]
Regions --> MCP["MCP server<br/>14 bounded tools"]
Graph --> MCP
Diagnostics --> MCP
MCP --> Codex["Codex and other MCP clients"]
MCP --> Editor["Surgical OOXML editor<br/>2 write tools"]
Editor --> Workbook
See the architecture for implemented boundaries and phase status.
Benchmarks
The verified P8 milestone commits the raw rows, exact/set-semantic answer checks, both headless-Codex repetitions, environment metadata, and scripts that regenerate every chart. The raw results index explains each artifact and the methodology documents isolation, grading, cost guards, and the optional-arm exclusion.
Results
| Arm | Exact answers | Accuracy | Mean full CLI tokens |
|---|---|---|---|
| Excel LSP | 12/12 | 100.0% | 77,310.5 |
| Naive dump | 8/12 | 66.7% | 64,909.8 |
Excel LSP meets S5 on its defined deterministic payload metric: 3,410 tokens versus 222,289, a 65.2× reduction, with equal-or-better headless accuracy. The disclosed 1,000-row archive workload is identical across arms and every original OOXML member stays byte-identical except package declarations and F03's deliberately extended Summary XML; a regression separately proves every pre-existing Summary cell, formula, and cache is unchanged. Mean full Codex usage was 77,310.5 versus 64,909.8 because that secondary measure includes fixed agent context, schemas, and reasoning; it is reported rather than conflated with workbook payload. See the criterion calculation and per-repetition table.
The 50,000-row median is 9.440 seconds cold and 0.066 seconds after a one-sheet change, satisfying S1's strict 10-second and 1-second limits.
Reproduce
Run the deterministic twelve-row replay with:
excel-lsp bench
For a fresh headless run and regenerated timing/charts, follow the commands in the benchmark methodology. Headless runs consume account capacity; use a new JSONL path instead of overwriting the committed evidence.
Comparison
This is a capability comparison, not an overall ranking. Upstream observations are limited to each project's README at a pinned revision accessed 2026-07-29; “not documented” does not mean impossible. See the source notes and exact revisions.
| Capability | Excel LSP | haris-musa/excel-mcp-server | jwadow/mcp-excel | Naive dump baseline |
|---|---|---|---|---|
| Persistent semantic index | SQLite semantic index | Not documented | Smart cache documented; persistent semantic index not documented | None |
| Formula dependency graph | Bidirectional, bounded traces | Not documented | Not documented | None |
| Incremental reindex | Part-hash driven and measured | Not documented | Not documented | Reopens workbook per request |
| Formula diagnostics | Typed catalog | Formula/range validation documented; diagnostic catalog not documented | Not documented | None |
| Edit support and untouched-part fidelity | 2 narrow writes; untouched ZIP parts byte-identical | Broad edits documented; byte-identity claim not documented | Read-only; writes on roadmap | Read-only |
| Token discipline | Hard response caps; 65.2× less measured workbook payload | Comparable caps not documented | Context limits and bounded previews documented | Full CSV dump |
How it works
P2 adds sparse region detection without constructing dense grids. Native Excel ListObjects take precedence; otherwise bounded header, type, style, merge, and density features produce a region and an explicit confidence score. See the architecture.
Verified P3 normalizes copied formulas into R1C1 signatures and groups matching cells into formula blocks. That design lets an agent reason about a large calculated column as one semantic unit while preserving cell-level references and anomalies. See the P3 evidence and index internals.
P4 stores formula range dependencies as rectangles in SQLite R*Tree, with a portable interval-table fallback. Point and range queries then feed bounded precedent, dependent, path, and diagnostic operations without expanding every range into individual edges. See index internals.
Security & scope
The local stdio server makes no runtime network requests and supports realpath-resolved workbook confinement.
The P6 core edit services surgically modify targeted worksheet XML and required
calculation metadata. Complete F16/F21 part manifests and a property test prove
that every OOXML part not deliberately modified stays byte-identical in the
verified implementation. EXCEL_LSP_ROOT provides an optional
os.pathsep-separated directory allowlist applied after realpath resolution.
Its default is unrestricted
local-path access. See SECURITY.md for the current threat model
and implementation status.
Evidence
Start with the evidence index. It distinguishes verified artifacts from scope declarations and links the exhaustive README claims-to-artifacts matrix, clean-install report, and registry submission packet.
Not affiliated with Microsoft. Excel is a trademark of Microsoft Corporation.
Recommended Servers
playwright-mcp
A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.
Magic Component Platform (MCP)
An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.
Audiense Insights MCP Server
Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.
VeyraX MCP
Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.
graphlit-mcp-server
The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.
Kagi MCP Server
An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.
E2B
Using MCP to run code via e2b.
Neon Database
MCP server for interacting with Neon Management API and databases
Exa Search
A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.
Qdrant Server
This repository is an example of how to create a MCP server for Qdrant, a vector search engine.