Local task-scoped residue evidence for Agent testing and build workflows
- ✓Open-source license (Apache-2.0)
- ✓Actively maintained (<30d)
- ✓Clear description
- ✓Topics declared
- ✓Documented (README)
git clone https://github.com/fantasyce/agent-residue-evidence{
"mcpServers": {
"agent-residue-evidence": {
"command": "agent-residue-evidence"
}
}
}MCP Servers overview
# Agent Residue Evidence
Agent Residue Evidence (ARE) adds an evidence checkpoint to an Agent's test and
build workflow:
```text
observe → Agent reviews evidence → user authorizes cleanup
→ Agent cleans with its own tools → ARE verifies
```
ARE shows task-scoped files, directories, attributed processes, and their
listening ports. It never deletes files, stops processes, closes ports, decides
that something is safe to delete, scans the full machine, uploads data, uses
the network, or collects telemetry.
Visit the [project site](https://fantasyce.github.io/agent-residue-evidence/),
run the [reproducible task-residue demo](docs/demo.md), or download the
[latest verified release](https://github.com/fantasyce/agent-residue-evidence/releases/latest).
## Standard task
Before the first test or build, the Agent calls `begin_task_observation` with
the exact workspace and any task-owned temporary roots. Events are optional;
safe generic events or empty heartbeats can be sent with
`append_task_events`. Before its final answer, the Agent calls
`end_task_observation`, explains the candidates, and asks the user what to do.
Only after explicit authorization does the Agent clean with its normal tools.
It then calls `verify_task_residue` to recheck the same stable candidates.
`NO_CANDIDATES_OBSERVED` means only that no candidate appeared inside the
declared observation scope. It is not a host-wide cleanliness claim.
## Completed task without a baseline
`inspect_completed_task` accepts explicit roots and a bounded time window.
The result is always `PARTIAL_EVIDENCE`; it can use Event, receipt, inferred,
or unattributed evidence, but never claims `BASELINE_OBSERVED`. Use
`get_residue_report` to read a saved report without observing again.
Every observation is isolated by an opaque owner capability. Task IDs, report
IDs, observation IDs, and candidate IDs are display references only and never
authorize access. State is encrypted at rest with opaque filenames; exact
paths are revealed only through an authorized candidate-resolution call.
Short-lived executor capabilities can append only explicitly allowed event
types and root aliases and cannot read, end, verify, or resolve a task.
The MCP surface contains exactly eight tools:
- `begin_task_observation`
- `append_task_events`
- `end_task_observation`
- `inspect_completed_task`
- `get_residue_report`
- `verify_task_residue`
- `delegate_task_executor`
- `resolve_residue_candidate`
There is deliberately no cleanup, delete, execute, terminate, or close tool.
## Local-first packaging
One release contains a native CLI/stdio MCP server, thin Agent Plugin, MCPB,
checksums, provenance, and SPDX SBOM. See [Quickstart](docs/quickstart.md) and
[Install](docs/install.md). Runtime operation is fully local and offline.
Reports default to seven-day retention and a 100 MB total cap; retained reports
and active baselines are protected. Uninstall never silently deletes reports.
The MCP Registry namespace is
`io.github.fantasyce/agent-residue-evidence`.
Native acceptance covers macOS 14+ arm64, Linux amd64, and Windows 11 amd64
through mandatory native CI jobs. Cross-compilation alone is not reported as
native acceptance.
## Development
Go 1.26 or 1.27 is required.
```bash
bash scripts/check.sh
bash scripts/run_native_acceptance.sh
```
Apache-2.0 licensed. See [Security](SECURITY.md),
[Contributing](CONTRIBUTING.md), and [native acceptance](docs/native-acceptance.md).
What people ask about agent-residue-evidence
What is fantasyce/agent-residue-evidence?
+
fantasyce/agent-residue-evidence is mcp servers for the Claude AI ecosystem. Local task-scoped residue evidence for Agent testing and build workflows It has 0 GitHub stars and its last recorded update is dated 2026-08-27.
How do I install agent-residue-evidence?
+
You can install agent-residue-evidence by cloning the repository (https://github.com/fantasyce/agent-residue-evidence) or following the README instructions on GitHub. ClaudeWave also provides quick install blocks on this page.
Is fantasyce/agent-residue-evidence safe to use?
+
Our security agent has analyzed fantasyce/agent-residue-evidence and assigned a Trust Score of 95/100 (tier: Verified). See the full breakdown of passed checks and flags on this page.
Who maintains fantasyce/agent-residue-evidence?
+
fantasyce/agent-residue-evidence is maintained by fantasyce. The last recorded GitHub activity is dated 2026-08-27, with 1 open issues.
Are there alternatives to agent-residue-evidence?
+
Yes. On ClaudeWave you can browse similar mcp servers at /categories/mcp, sorted by popularity or recent activity.
Deploy agent-residue-evidence to your cloud
Ship this repo to production in minutes. Each platform spins up its own environment with editable env vars.
Maintain this repo? Add a badge to your README
Drop the badge into your GitHub README to show it's tracked on ClaudeWave. Each badge links back to this page and reflects the live Trust Score.
[](https://claudewave.com/repo/fantasyce-agent-residue-evidence)<a href="https://claudewave.com/repo/fantasyce-agent-residue-evidence"><img src="https://claudewave.com/api/badge/fantasyce-agent-residue-evidence" alt="Featured on ClaudeWave: fantasyce/agent-residue-evidence" width="320" height="64" /></a>More MCP Servers
Fair-code workflow automation platform with native AI capabilities. Combine visual building with custom code, self-host or cloud, 400+ integrations.
User-friendly AI Interface (Supports Ollama, OpenAI API, ...)
An open-source AI agent that brings the power of Gemini directly into your terminal.
Real-time global intelligence dashboard. AI-powered news aggregation, geopolitical monitoring, and infrastructure tracking in a unified situational awareness interface
The fastest path to AI-powered full stack observability, even for lean teams.
🕷️ An adaptive Web Scraping framework that handles everything from a single request to a full-scale crawl!