Mmcp.market

trovex MCP server

by TsukumoHQ·io.github.TsukumoHQ/trovex·v0.15.0

Serves coding agents one canonical doc per query instead of rereading the repo — ~60% fewer tokens.

B75/100grade B
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Safety scan
B75/100

full report

Adoption
Growing

10 stars94 downloads/wk

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If you have run it, two minutes of your experience saves the next person an afternoon.

trovex tools (7, 2 write)

write = sends, deletes, buys or posts

Read from the package source without running it. The installed server may list more.

  • decorator
  • trovex

    Find canonical docs for a query.

  • trovex_deletewrite action

    Delete a trovex-owned doc by id.

  • trovex_read

    Read a trovex-owned doc — by default returns the most relevant *passage*.

  • trovex_search

    Search the store — returns the top K relevant *passages* (not whole docs).

  • trovex_tag

    Add/remove tags on a trovex-owned doc — returns the doc's new tag set.

  • trovex_writewrite action

    Store a doc INSIDE trovex so every agent of every dev can read it.

Public scan report

scanner v0.1.9 · 2026-09-20 · same rubric, same numbers if you re-run it

2 medium
  • Code scan46 source files scanned; 34 source files scanned15/25
  • Live reliabilityno gateway calls yet and no remote to proben/a
  • Tool poisoningtools not inspected (local package is not executed); not countedn/a
  • Auth qualitylocal package, no credentials required12/15
  • Maintenancelast push 17 days ago15/15
  • Maintainer identityregistry namespace matches repository owner7/10

Findings (2)

  • mediumeval / new Function usedexec.eval
    trovex-0.15.0/src/trovex/cli.py: ….Option("gpt-5.4-mini", help="LLM for --eval (answers + judges)."), json_out: bool…
  • mediumeval / new Function usedexec.eval
    trovex-0.13.1/src/trovex/cli.py: ….Option("gpt-5.4-mini", help="LLM for --eval (answers + judges)."), json_out: bool…
Overall 75/100. Components that don't apply are left out of the denominator. Any critical finding is an F.RubricAppeal a findingJSON

What the publisher says

From the trovex repository's README, as published. We do not edit it. Read it on GitHub

<!-- mcp-name: io.github.TsukumoHQ/trovex -->

trovex

trovex: one canonical doc for your coding agents, ~60% fewer tokens.

Your coding agents (Claude Code, Cursor, Windsurf, Zed, any MCP client) reread the repo every session to work out which .md is current, then answer from a guess. You pay for that on every session, every agent, every teammate.

trovex indexes your repo's markdown and exposes one MCP tool. Your agent asks a question; trovex returns the single current doc that answers it as a path:line pointer with a freshness marker (canonical / stale / duplicate), and serves just the section that answers instead of the whole file. Agents also write what they learn back through one shared point, so every agent and teammate reads the same source of truth instead of re-deriving it.

About 60% fewer tokens on doc lookups, measured at equal task-success on our own repo (it varies by yours). Runs locally: vectors in SQLite, embeddings via ONNX, no cloud or API keys.

What you're installing

A 30-second trust check, since the decision happens on the README, not the directory listing:

  • First-party, open source. Built and run in production by tsukumo, the team behind it. AGPL-3.0-or-later.
  • Local-first, nothing leaves your machine. Vectors in SQLite, embeddings via ONNX. No cloud, no API keys, no network call to answer a query.
  • Confined writes, no shell. Six MCP tools: three read-only (trovex, trovexread, trovexsearch) and three that mutate only trovex's own doc store (trovexwrite, trovextag, trovex_delete). No shell execution, no writes to your source files.
  • The ~60% is reproducible. Measured at equal task-success on our own repo (median 69%, ~41–81% by repo, n=26, LLM-judged); trovex search prints the savings on yours. Full method at trovex.dev/measure.

Quick start

trovex is in public beta, on PyPI. No clone needed; uv tool install puts trovex on your PATH:

uv tool install trovex   # one-time, no clone
trovex setup             # wire into Claude Code (skill + hooks + MCP); idempotent

trovex index /path/to/your/repo        # index your markdown (~1 min)
trovex search "how do we roll back a deploy?"   # ask, prints the tokens it saved
trovex serve                           # wire into your agent: MCP at /mcp, dashboard at /savings

Don't have uv? It's a one-line install: curl -LsSf https://astral.sh/uv/install.sh | sh (or brew install uv).

The search step is the fast way to see the point: it returns the one canonical doc and prints how many tokens that saved versus reading the top few candidates. Once trovex is wired into your agent over MCP, the same numbers accumulate on the savings dashboard at http://localhost:8765/savings.

Prefer not to install anything yet? uvx trovex search "..." runs a single command in a

throwaway environment, no install.

Wire it into your agent

trovex is an MCP server. Point your client at http://localhost:8765/mcp after trovex serve. Per-client setup (Claude Code, Cursor, Windsurf, Cline, Zed, Roo) is at trovex.dev/for.

Claude Code, one command. trovex setup installs the Claude Code skill, the Active-Memory hooks, and registers the MCP server in one step (idempotent, safe to re-run):

trovex setup

Shortened. The full README is on GitHub.

Nothing above is checked by us. What we check is on the safety report.

Install directly

claude mcp add trovex -- uvx trovex
Add to Cursor

trovex: common questions

Is trovex MCP server safe?
Mostly: it is graded B (75/100). Read the trovex safety report
How do I install trovex?
It runs on your machine. Copy the Claude Code, Claude Desktop or Cursor config from the install section.
Does trovex need an API key?
Not as far as the registry entry and our scan can tell: no credentials are declared or required.
Is trovex maintained?
The last commit was 18 days ago (2026-09-03). The latest release is v0.15.0.
What can I use instead of trovex?
Servers from other publishers that do the same job: Codebase Memory MCP server and Trace MCP server.

Alternatives to trovex

Same job from other publishers: the closest match first, then the best rated.

  • Codebase Memory
    Codebase knowledge graph for AI agents — 162 languages, sub-ms queries, 99% fewer tokens.
    C
  • Trace
    Code graph MCP: 81 languages, 88 frameworks, 72.7% fewer PR-review tokens, comprehension at parity
    A

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