trace-mcp
Framework-aware code intelligence MCP server — 91 framework integrations, 81 languages, 72.7% fewer input tokens to review a pull request, comprehension at parity
Install / Use
npx skills add nikolai-vysotskyi/trace-mcpInstalls into whichever agent you are using.
SKILL.md
Installable skill definition
Quality Score
Category
Development & EngineeringSupported Platforms
Our assessment of trace-mcp
trace-mcp scores 81/100 on our quality scale, 3439th of 4,611 Development & Engineering skills we index.
Its SKILL.md is 54 KB long, well organised into 35 sections with 13 code examples: long enough that it reads more like full documentation than a focused instruction file, which agents can find harder to follow.
It has 202 GitHub stars, a meaningful sign that others use it.
Maintenance, license and trust
- The repository was last updated today, so trace-mcp is actively maintained.
- Our last check on 2026-10-18 found the source still online.
- It is released under the MIT license, a permissive license that allows use, modification and commercial use with attribution.
- Its trust signals score 100/100, with no cautions. These come from repository metadata, not a code audit — read the skill file before letting an agent act on it.
Safety scan
No issues foundOur scan of the whole file found no instruction hijacking, hidden characters, credential access, data exfiltration or destructive commands. An AI review of the same text found nothing harmful.
AI review by kimi-k2.7-code on 2026-09-24. Automated pattern scan on 2026-09-24. It catches known dangerous patterns, not every risk — read a skill before letting an agent act on it.
trace-mcp compared with similar skills
All 4 of these similar skills score higher than trace-mcp; compare them before choosing.
| Skill | Score | Stars | Updated | Format |
|---|---|---|---|---|
| trace-mcp (this skill)by nikolai-vysotskyi | 81 | 202 | today | SKILL.md |
| taste-skillby Leonxlnx | 100 | 111.0k | 1d ago | CLAUDE.md |
| Agent-Reachby Panniantong | 100 | 109.5k | 1d ago | CLAUDE.md |
| headroomby headroomlabs-ai | 100 | 79.0k | today | CLAUDE.md |
| ai-job-searchby ai-job-search-dev | 100 | 70.0k | today | CLAUDE.md |
Frequently asked questions
- How do I install trace-mcp?
- Run
npx skills add nikolai-vysotskyi/trace-mcp. The install tabs above show the steps for each supported agent. - Which AI agents does trace-mcp work with?
- It is written for Claude Code and OpenAI Codex, as a SKILL.md file. Other agents that read the same format can often use it too.
- Is trace-mcp safe to use?
- Our scan of the whole file found no instruction hijacking, hidden characters, credential access, data exfiltration or destructive commands. An AI review of the same text found nothing harmful. It is MIT-licensed and scores 100/100 on trust signals. Skills are instructions an agent will follow, so read the file before installing it and do not approve commands you do not understand.
- Is trace-mcp still maintained?
- The repository was last updated today, so trace-mcp is actively maintained.
Skill content
View source on GitHubnpm install -g trace-mcp # MCP server, no app
trace init # wire it into your agent, once per machine
trace add # index the repo you are in
72.7% fewer input tokens to review a pull request — median over 60 merged PRs in six repos that are not ours, 13,595 → 3,291 per pull request. Method and reproduction →
<sub>Measured at trace-mcp 3.23.2 (cb8ab30c) on 7 September 2026 — a result from that build, not a claim about the current one. What it set out to measure, the bar it had to clear and the verdict: preregistration.</sub>
Cheaper is not the same as better, so the same 60 pull requests were reviewed twice and scored blind. The trace-mcp arm understood the change in 67% of them against 65% for naive file loading, at 0.80 false positives per PR against 0.58. Quality half of the benchmark →
<p align="center"> <img src="docs/images/app-graph.webp" alt="trace-mcp app — GPU graph explorer visualizing symbol connections, light appearance" width="820" height="512" loading="lazy" /> <br/> <sub>The <a href="#desktop-app">desktop app</a>: a GPU graph explorer over the same index the MCP server serves.</sub> </p>The problem
AI agents pay repeatedly for work they have already done. Every turn, the agent re-reads the same files, re-traverses the same dependencies, and re-inflates the context window with structure it discovered five steps ago. That repeated work is most of what a long session costs in tokens and latency.
trace-mcp builds a framework-aware graph of your codebase once, then serves it through MCP so the agent reasons from a precomputed structure instead of brute-reading the repo. Ask "what breaks if I change this model?" — instead of 80 Grep calls and 190 file reads, the agent calls get_change_impact once and gets the blast radius across PHP, Vue, migrations, and DI. 88 framework integrations across 81 languages, 182 tools.
The binding constraint is recomputation, not model capability: token bills, latency, and hallucinations all grow with project size instead of with task complexity. trace-mcp closes the recomputation leak. The graph is built once, kept incrementally fresh, and served to every agent that asks — so the same work isn't paid for over and over.
- Lower cost — fewer tokens per successful answer, on average and at peak
- Lower latency — fewer sequential tool calls, fewer round-trips to the model
- Higher accuracy — less noise in context means fewer hallucinations and stronger first-response correctness
- Production stability — context growth tracks task complexity rather than repository size
We started with code intelligence, where the repetition is most expensive, and the same engine now indexes markdown knowledge vaults (Obsidian, Logseq, plain MD) as a peer domain. Wikilinks, tags, frontmatter, and embeds become graph edges and symbol metadata; search, find_usages, get_change_impact, and apply_rename work identically over both.
What trace-mcp does for you
| You ask | trace-mcp answers | How |
|---|---|---|
| "What breaks if I change this model?" | Blast radius across languages + risk score + linked architectural decisions | get_change_impact — reverse dependency graph + decision memory |
| "Why was auth implemented this way?" | The actual decision record with reasoning and tradeoffs | query_decisions — searches the decision knowledge graph linked to code |
| "I'm starting a new task" | Optimal code subgraph + relevant past decisions + dead-end warnings | plan_turn — opening-move router with decision enrichment |
| "What did we discuss about GraphQL last month?" | Verbatim conversation fragments with file references | search_sessions — FTS5 search across all past session content |
| "Show me the request flow from URL to rendered page" | Route → Middleware → Controller → Service → View with prop mapping | get_request_flow — framework-aware edge traversal |
| "Find all untested code in this module" | Symbols classified as "unreached" or "imported but never called in tests" | get_untested_symbols — test-to-source mapping |
| "What's the impact of this API change on other services?" | Cross-subproject client calls with confidence scores | get_subproject_impact — topology graph traversal |
| "What notes link to this concept?" | Backlinks across the vault, with section + alias context | find_usages on a note:<basename> symbol |
| "What breaks if I rename this note?" | Every [[wikilink]] and [text](path.md) that references it | get_change_impact — wikilink-aware reverse graph |
Four capabilities that are rare among adjacent tools:
-
Framework-aware edges — trace-mcp understands that
Inertia::render('Users/Show')connects PHP to Vue, that@Injectable()creates a DI dependency, that$user->posts()means apoststable from migrations. 88 framework integrations. -
Code-linked decision memory — when you record "chose PostgreSQL for JSONB support", it's linked to
src/db/connection.ts::Pool#class. When someone runsget_change_impacton that symbol, they see the decision. MemPalace stores decisions as text; trace-mcp ties them to the dependency graph. -
Cross-session intelligence — past sessions are mined for decisions and indexed for search. When you start a new session,
get_wake_upgives you orientation in ~300 tokens;plan_turnshows rel
Truncated for display — read the full file on GitHub.
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Languages
Trust signals
From repository metadata: license, adoption, age and documentation. Not a code audit — see the Safety scan above for what the skill file itself contains.
