tiny-context — efficient file tools by AVRG3

tiny-context — MCP tools for document search, SQL and JSON export changes

Know things about files without reading them. Eight local, deterministic MCP tools that let an AI agent outline, search, slice, query, cluster, diff, validate and extract from files — including PDF, DOCX, PPTX and XLSX — and get back only what it needs, with a savings line on every response.

Find a tool by task, with example inputs · Actual tool schemas · Installation. Built by AVRG3.

Designed for agents seeking fewer steps on file tasks:

• Explain a large error log: summarize_log returns repeated errors, counts and time ranges without sending every log line into context.
• Answer a spreadsheet question: query_table runs SQL over CSV/XLSX/Parquet and returns the result instead of the source rows.
• Find a PDF or Office passage: query_file returns ranked matches; read_section reads a known location. Use file_map only when an outline is useful.
• Compare JSON/JSONL exports: diff_files records mode matches orders, issues or inventory by explicit IDs and reports actual changes despite shuffled rows. Choose fields such as status/quantity; receive exact scoped counts and bounded before/after previews. Contract and example.

Measure the whole task: the tool response can be much smaller than the source file, but startup, validation and extra agent turns still cost time. Results depend on the task and client. Agent comparison, including regressions · Historical first comparison.

The whole-file-read benchmark below measures a different baseline; its savings are not a prediction for a capable agent.

Find tiny-context

Listed in the official MCP Registry as io.github.Warddamn/tiny-context, with a Glama profile. Directory indexing is controlled externally; your client must still connect the server before an agent can call it.

New in 0.2.0: compare records by ID

diff_files now accepts mode: "records" for JSON/JSONL/NDJSON snapshots, explicit key pointers and optional fields/records_path. It rejects ambiguous IDs and precision loss; out saves every change as JSONL without replacing existing files. Eight tools remain; no new dependency or model call. Worked example and limits · Benchmark against an existing correct script · Real-agent checks.

This avoids sending unchanged records to the model, but still reads both files locally. Small files and existing correct scripts can be cheaper and faster.

Safety controls (introduced in 0.1.1)

Upgrade older installs using the current install button/config below, then reconnect the server. Existing version-pinned installations do not update automatically.

SQL accepts one read-only query against the supplied table. External file/network access and SQL write commands are disabled; exports require out and never replace existing files. Input/export limits are 64 MB; the query worker defaults to a 15-second deadline with bounded engine/JavaScript memory. These controls are not an operating-system security sandbox.

Go directly to query_file for a question or read_section for a known location; use file_map only when an outline is useful. Repeated document reads reuse a cache of up to eight files, 16 MB of serialized parsed data, and 30 seconds, checking file identity and modification metadata on every hit. The optional Read hook still builds an outline and starts a separate process; it can add work and is not required. Small text and exact-string searches often need only built-ins. Tools have startup/validation overhead and do not guarantee lower total cost on every task.

New in tiny-context 0.3.0: reusable document profiles

Save a document's labels and checks once, then use extract on recurring forms/reports in one batch. TXT/Markdown, DOCX and text-layer PDFs; no training, model calls or automatic inference. Missing or ambiguous fields return needs_review without partial values. Profiles remain local, and every new document is parsed and checked again. Save and reuse a profile · Setup, batch and prepared-script measurements.

A smaller response is not proof of a smaller bill. Earlier keyed-comparison trials ran faster but cost about 33% and 3% more in their two paired runs. Profiles are opt-in for repeated work, not a replacement for a cheap single read or an existing correct script. Their total tool-definition payload is slightly smaller than 0.2.0 after shortening descriptions; agent/setup/cache costs still depend on the client. The saved-profile trial and its limits include the initial extra-call issue and the corrected result.

Install

MCPB-compatible clients: download the bundle for your OS from the agent bundle release and open it in your client. darwin = macOS, win32 = Windows, linux = Linux. Each bundles dependencies for x64 and arm64; a Node.js 20+ runtime is still required (some clients provide it). These are unsigned bundles with SHA-256 hashes in the registry. Downloads are approximately 77 MiB for macOS, 96 MiB for Linux and 34 MiB for Windows. All eight tools were tested from extracted bundles on macOS, Linux and Windows; not every CPU/OS combination or client UI has been tested.

Other MCP clients: use the existing commands below. They download only the dependencies needed for the current machine.

Node.js 20+ required. No npm account or token needed. Use the published GitHub release below. The npm package is not yet published; these commands do not depend on it. The server runs locally over stdio. Allow the first launch time to download its dependencies.

Claude Code

claude mcp add tiny-context -- npx -y -p https://github.com/Warddamn/tiny-tools/releases/download/context-v0.3.0/tiny-context-standalone-0.3.0.tgz tiny-context-mcp

Codex CLI (writes [mcp_servers.tiny-context] to ~/.codex/config.toml)

codex mcp add tiny-context -- npx -y -p https://github.com/Warddamn/tiny-tools/releases/download/context-v0.3.0/tiny-context-standalone-0.3.0.tgz tiny-context-mcp

Cursor — .cursor/mcp.json, or click the *Install in Cursor* badge above

{ "mcpServers": { "tiny-context": { "command": "npx", "args": ["-y", "-p", "https://github.com/Warddamn/tiny-tools/releases/download/context-v0.3.0/tiny-context-standalone-0.3.0.tgz", "tiny-context-mcp"] } } }

VS Code — .vscode/mcp.json (note the servers key), or click the *Install in VS Code* badge above

{ "servers": { "tiny-context": { "type": "stdio", "command": "npx", "args": ["-y", "-p", "https://github.com/Warddamn/tiny-tools/releases/download/context-v0.3.0/tiny-context-standalone-0.3.0.tgz", "tiny-context-mcp"] } } }

Windsurf — ~/.codeium/windsurf/mcp_config.json

{ "mcpServers": { "tiny-context": { "command": "npx", "args": ["-y", "-p", "https://github.com/Warddamn/tiny-tools/releases/download/context-v0.3.0/tiny-context-standalone-0.3.0.tgz", "tiny-context-mcp"] } } }

Claude Desktop — ~/Library/Application Support/Claude/claude_desktop_config.json (macOS) · %APPDATA%\Claude\claude_desktop_config.json (Windows)

{ "mcpServers": { "tiny-context": { "command": "npx", "args": ["-y", "-p", "https://github.com/Warddamn/tiny-tools/releases/download/context-v0.3.0/tiny-context-standalone-0.3.0.tgz", "tiny-context-mcp"] } } }

Then paste docs/AGENT_USAGE.md into your CLAUDE.md / AGENTS.md / .cursorrules so the agent reaches for the tools at the right moments. Claude Code users can also add the Read guard hook, an optional stricter policy. Start with the snippet; the hook did not improve the measured comparison.

TINY_TOOLS_DEBUG=1 logs each call to stderr.

Try it with your agent

Restart or reconnect your client after setup. Confirm that tiny-context is connected and exposes eight tools. Then use the three worked examples for a CSV question, a PDF clause and an error log, with expected answers.

Claude Code plugin: /plugin marketplace add Warddamn/tiny-tools, then /plugin install tiny-context@tiny-tools. This includes both the server and task-selection guidance. Choose this or the manual MCP setup to avoid duplicate servers.

Privacy

• No telemetry from the tools. No usage reports or analytics are sent by the server.
• The tools process local files and return selected text to your MCP client. They do not call a model or upload files themselves; your client may send that text to its model provider according to its settings.
• First launch downloads the release and third-party dependencies using npm, including optional DuckDB for query_table. This is not an offline installer. GitHub and npm maintain their own download counters; those are not counts of agents or people.

What it replaces

Whole-file Read/cat calls that mattered for one turn; ls+grep+head loops to learn what a folder holds; scripts written just to sum a column; tail/grep loops over logs; opening PDFs and Office files that built-in tools can't read at all.

Why

~50% of a typical agent's context window is tool results, dominated by whole-file reads that mattered for one turn; agent workloads are read/review-heavy (~59% checking vs ~9% writing in published measurements); keeping intermediate data out of context has shown up-to-98% token reductions. These tools let an agent *know things about files without reading them*. Every response is bounded and ends with a savings line.

Reducing tokens-per-step and wall-clock-per-step is what lets an agent take more steps before its context degrades — a capability multiplier, not just a cost saving.

Benchmarks

17 tasks · 7,417,733 naive tokens → 9,158 tool tokens · 99.9% saved overall · median 11ms per call

| query_table | how many rows have a negative total (sales.csv