ContextQuant MCPサーバー

ContextQuant

An MCP proxy that shrinks large JSON tool results before they reach an AI agent. It tells the agent exactly what it cut, and gives it a tool to get the cut parts back.

Real GitHub data, recorded with vhs from media/demo.tape.

The problem

MCP tools often return far more than the agent needs. One GitHub search_issues call for 30 issues is about 70,000 tokens: every issue body, about 60 API links per issue, and the same author metadata repeated 30 times. To answer "which open auth bugs are unassigned?" the agent needs four fields: number, title, labels and assignee.

Clients deal with oversized results in one of three ways, and all three hurt:

What the client does	What goes wrong
Puts the whole result in context	Fills the context window; every later turn re-reads it
Truncates it, or keeps only some fields	The agent can't tell what's missing, so it guesses or searches again
Saves it to a file	Fine if the agent can run code (jq); otherwise it never sees the data

ContextQuant takes a fourth approach: cut the result down, and declare every cut.

How it works

ContextQuant sits between the agent and any MCP server. Tool calls pass through unchanged; only the results are rewritten.

agent (Claude Code, any MCP client)
   │   tool call ──────────────► passes through unchanged
   ▼
ContextQuant proxy ──────────────► wrapped MCP server (GitHub, Jira, a database…)
   │   ◄─────────────────────────  70k-token JSON result
   │
   │   1. find the list of records in the JSON
   │   2. pick the fields worth keeping (short values stay; long text and API links go)
   │   3. print fields that are the same in every record once, not 30 times
   │   4. fit as many records as the token budget allows (default 8,000)
   │   5. write a header saying what was left out and how to get it
   │   6. keep the original in memory under a handle, e.g. "r1"
   ▼
agent reads ~6k tokens, and can call contextquant_more("r1", …) for anything cut

What the agent reads for 18 GitHub issues (37,903 tokens raw, 5,556 through ContextQuant):

# contextquant: handle="r1" holds the full response · contextquant_more(handle, offset, fields) returns cut records or left-out fields
# search_issues: showing 18 of 18 · total_count=18 · incomplete_results=false
# same in every record: user.type="User" · state="open" · locked=false · …
# empty (null) in every record: closed_at, milestone, …
# left out fields: body, +66 URL/ID fields
{"html_url":"https://github.com/modelcontextprotocol/typescript-sdk/issues/2510","number":2510,"title":"Mid-session re-auth after refresh-token expiry dead-ends: …","labels":[{"name":"bug",…}],…}
…

Each header line tells the agent something it would otherwise have to guess:

Line	What it tells the agent
showing 18 of 18	How many records came back and how many are shown
same in every record: …	Values shared by all records, printed once; nothing is missing
empty (null) in every record: …	Fields that exist but are empty, so "nobody is assigned" is an answer, not a gap
left out fields: body, …	Fields removed from the rows; contextquant_more(handle, fields=["number","body"]) returns them
12 records not shown · call contextquant_more with handle="r1" and offset=18	Records cut to fit the budget, with the exact call for the next page

Why declaring the cuts matters. A model that can't see a field and isn't told it was removed tends to fill the gap with something plausible. In testing, a model asked for a person's "headline" answered with their job title every time when the field had been dropped silently, and said "not shown" when the cut was declared. The header turns a silent gap into a known one.

Results that aren't a list of records (plain text, single objects, errors), results that wouldn't get smaller, and results with a declared output schema pass through untouched.

Use cases

It fits any tool that returns many records when the agent needs a few fields from each:

• Issue and ticket triage. GitHub, Jira, Linear or Sentry searches: "which open bugs are unassigned?"
• CRM and people search. HubSpot or enrichment APIs return 50+ fields per profile; the agent filters on five.
• Database and log queries. SELECT *-style results where most columns repeat or are empty.
• Agents that can't run code. Chat apps, connectors and hosted agents can't jq a saved file. This is where ContextQuant measured best (see Results).

It doesn't fit:

• Prose, code or logs. It only works on JSON lists of records.
• Coding agents with a shell, if cost is the goal. Raw results saved to a file plus jq were as accurate and about 2× cheaper. ContextQuant was faster there, but not cheaper.
• Tools whose every field is needed every time. Mark them skip in the config.

Quick start

Needs Node 20+. Put ContextQuant in front of any MCP server's own command:

claude mcp add --env GITHUB_PERSONAL_ACCESS_TOKEN=ghp_... github-cq \
  -- npx -y contextquant proxy -- npx -y @modelcontextprotocol/server-github

Any MCP client works the same way: npx -y contextquant proxy -- <the server's command>. Remote (HTTP) servers use --url:

npx -y contextquant proxy --url https://api.githubcopilot.com/mcp/ --header "Authorization: Bearer ghp_..."

Environment variables (API tokens) pass through to the wrapped server. Each shaped call logs its saving to stderr, e.g. contextquant search_issues: 37903 → 5556 tokens (o200k).

Configuration (optional)

npx -y contextquant proxy --budget 8000 --config contextquant.json -- <server command>

{
  "budget_tokens": 8000,
  "long_text_chars": 200,
  "keep_responses": 50,
  "timeout_seconds": 600,
  "tools": {
    "search_issues": { "fields": ["number", "title", "labels.name", "assignee.login"], "counts": ["labels.name"] },
    "get_file_contents": { "skip": true }
  }
}

Key	Meaning (values above are the defaults)
budget_tokens	Max tokens per result. 8,000 keeps it under Claude Code's 10,000-token warning
long_text_chars	Text fields averaging more characters than this are left out
keep_responses	How many original responses contextquant_more can still reach
tools.<name>.fields	Fields to keep for that tool, replacing the automatic choice
tools.<name>.counts	Adds value counts over all records, so the agent reads counts instead of counting rows
tools.<name>.skip	Pass that tool's results through unchanged

Results

Token counts use a real tokenizer (o200k). All data is live GitHub results, captured once and replayed byte for byte so every option sees the same input.

Size. Five searches of 30 issues each (MCP SDK, bun, vscode, rust, next.js):

	Tokens	Facts visible to the agent
Raw JSON	373,363	100%
ContextQuant	37,883 (−90%)	90%; the rest declared and one call away
mcp-trunc-proxy	9,165	~3%; the rest behind its grep tool
headroom	312,290	99%; compressed 2 of the 5

Accuracy. 12 questions about two of those searches, graded in code, 3 runs each, on Claude Haiku, Claude Sonnet and Gemini 3.8 Flash (eval/github-agent.ts, results in eval/results/).

Agent with only the MCP tool (no shell):

	Correct	Confidently wrong
Raw JSON	19/108	0 (the client saved the result to a file the agent couldn't read)
ContextQuant	105/108	0
mcp-trunc-proxy	93/108	6 (its grep returns JSON lines without saying which record they belong to)

Agent with a read-only shell (jq, grep, head…), Claude only:

	Correct	Turns (Sonnet)	Cost, all runs
Raw JSON	71/72	3.9	$1.22
ContextQuant	71/72	2.7	$2.97
mcp-trunc-proxy	71/72	7.0	$2.41

With a shell, raw is cheapest: the big result stays in a file and jq pulls out only what's needed, while ContextQuant's ~8k tokens sit in the context and are re-read every turn. A smaller budget didn't fix that. Small sample (my own 12 questions); full numbers and caveats in notes/findings.md.

Compared to other tools

	What it reduces	How
ContextQuant	JSON tool results from any MCP server	Picks fields, prints shared values once, declares every cut, recovery tool
mcp-trunc-proxy	Large MCP tool results	Stores them, returns head/tail lines and a grep tool
headroom	All agent context: JSON, code, logs, history	Content-aware compressors, originals retrievable
rtk	Shell command output	Per-command filters
TOON	JSON in prompts	A more compact notation for the same data

Code map

src/
  proxy.ts      MCP proxy: wraps a server, shapes each result, serves contextquant_more
  shape.ts      the core: records → kept fields → budget → header + rows
  auto.ts       automatic field choice (what counts as long text or a machine link)
  ids.ts        shortens opaque IDs (UUIDs, hashes) to aliases like @1
  tokens.ts     token counting (o200k / cl100k)
  cli.ts        command line: proxy, gateway, shape, bench
  gateway/      a built-in MCP server with hand-tuned GitHub, Jira and HubSpot tools
  adapters/     per-API rendering for the gateway (Jira and HubSpot tested against fixtures only)
bench/compare/  capture live GitHub results, replay them, compare tools by tokens
eval/           agent accuracy eval on the replayed results
notes/          measurements, research and what failed

Also usable as a library: shape(responseText, { fields: "auto", maxTokens: 4000 }) from src/index.ts.

Development

Needs Bun 1.1+.

bun install
bun test                     # unit, stress and end-to-end proxy tests
bunx tsc --noEmit -p .
bun run bench                # token comparison on a synthetic people fixture
bun bench/compare/capture.ts && bun bench/compare/compare.ts   # live GitHub comparison

Status

Finished at v0.3.1. The question it set out to answer (does declaring cuts beat sending everything or cutting silently?) has an answer: yes for agents that can't run code, no for cost when they can. See notes/next.md for what a restart would pick up.

The first version was a C dictionary compressor (only-c branch). Measured with real tokenizers it saved nothing, because rare symbols cost more tokens than the words they replaced. That is why this version removes fields instead of compressing text.

License

MIT