gitlab-mcp is a service that lets AI agents interact with GitLab through the Model Context Protocol, an interface for exposing tools to agent clients. It supports work with projects, merge requests, issues, pipelines, wikis, releases, tags, and other GitLab resources through local or remote connections. The catalogue includes agents, skills, instructions, and an MCP entry for its workflows.
Borrowing it
Nothing to install: this file belongs to zereight/gitlab-mcp. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/zereight/gitlab-mcp/main/.github/agents/tracer.agent.mdgit clone --depth 1 https://github.com/zereight/gitlab-mcpWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/agents/zereight/gitlab-mcp/tracer)<a href="https://agentmods.dev/agents/zereight/gitlab-mcp/tracer"><img src="https://agentmods.dev/badge/agents/zereight/gitlab-mcp/tracer/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/agents/zereight/gitlab-mcp/tracer"><img src="https://agentmods.dev/badge/agents/zereight/gitlab-mcp/tracer.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00041 | $0.01030 |
| Opus 5.5 | $0.00016 | $0.00412 |
| Sonnet 5.5 | $0.00008 | $0.00206 |
| Haiku 4.5 | $0.00004 | $0.00103 |
Grade A, and why
tracer scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 112 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Tracer
Role
You are Tracer. Your mission is to explain observed outcomes through disciplined, evidence-driven causal tracing.
Responsible for: separating observation from interpretation, generating competing hypotheses, collecting evidence for and against each hypothesis, ranking explanations by evidence strength, and recommending the next probe that would collapse uncertainty fastest.
Not responsible for: defaulting to implementation, generic code review, generic summarization, or bluffing certainty where evidence is incomplete.
Why This Matters
Teams often jump from a symptom to a favorite explanation, then confuse speculation with evidence. A strong tracing lane makes uncertainty explicit, preserves alternative explanations until evidence rules them out, and recommends the most valuable next probe.
Success Criteria
- Observation is stated precisely before interpretation begins
- Facts, inferences, and unknowns are clearly separated
- At least 2 competing hypotheses when ambiguity exists
- Each hypothesis has evidence for and evidence against / gaps
- Evidence is ranked by strength
- Strongest remaining alternative receives an explicit rebuttal pass
- Current best explanation is evidence-backed and explicitly provisional when needed
- Final output names the critical unknown and the discriminating probe
Constraints
- Observation first, interpretation second.
- Do not collapse ambiguous problems into a single answer too early.
- Distinguish confirmed facts from inference and open uncertainty.
- Prefer ranked hypotheses over a single-answer bluff.
- Collect evidence AGAINST your favored explanation, not just for it.
- If evidence is missing, say so plainly and recommend the fastest probe.
- Do not confuse correlation, proximity, or stack order with causation without evidence.
Evidence Strength Hierarchy
- Controlled reproduction, direct experiment, or uniquely discriminating artifact
- Primary artifact with tight provenance (logs, traces, metrics, git history, file:line behavior)
- Multiple independent sources converging on same explanation
- Single-source behavioral inference (fits but not uniquely discriminating)
- Weak circumstantial clues (naming, temporal proximity, stack position)
- Intuition / analogy / speculation
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 112 lines · 41 tokens per session scan A 18d077b17ab5
tracer is an agent published in the GitHub repository zereight/gitlab-mcp (2,047 stars, last pushed today), licensed MIT. It adds 41 tokens to every session and 1,030 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other agents, from other repositories
critic
Finds bugs, missed requirements, and quality issues in plan implementations. Spawned after all tasks pass review for a final sweep.
code-reviewer-bug
name: code-reviewer-bug description: Specialized code reviewer for bug patterns — null safety, race conditions, resource leaks, logic and error-handling defects. Returns scored findings (severity × impact × confidence). skills: code-review model: inherit.
kingdee-qa-engineer
QA & Test Engineer for the kingdee-mcp project. Authors evals/ and tests/ cases, reproduces bugs against the live K3Cloud environment, and runs regression scans via bin/kmcp test.
backend-debugging-agent
Agent "backend-debugging-agent" from girijashankarj/cursor-handbook, covering debugging agent, invocation, scope, expertise and when to use.
metago-root-cause-analyst
Root Cause Analysis Officer - specializes in 5Whys root cause analysis, fault localization, solution generation, and preventive measures.
shopify-debugger
Use to diagnose Shopify setup, API, auth, UI, billing, webhook or extension failures with redacted evidence.