mcp-agent
Build effective agents using Model Context Protocol and simple workflow patterns
Runssource: GitHubPythonApache-2.0commit f62d84935081
Python, Apache-2.0 licensed. The project labels itself: agents, ai, ai agents, llm, llms, mcp, model context protocol and python.
mcp-agent runs. An Argusic agent installed it in 3 minutes and hit 1 error and fixed every one without help on a clean machine with no GPU, and the whole session was recorded.
uv sync installs cleanly, the CLI binary reports version 0.2.6, the test suite achieves 1500/1505 passing (1 pre-existing failure), and all source classes instantiate without abstract method errors.
What the agent ended up with on a clean machine, in its own words. How this is measured
Measured by Argusic on a fresh machine every time. Every number links to its evidence. Argusic Score 96 of 100 (the mean of this project's run scores).
At a glance
Subject data from GitHub, linked at the top of this page, refreshed . Test data by Argusic (CC BY 4.0); every number links to a run page with the full log, the recording, and their sha256 hashes.
What was measured
From the run that decided this verdict. Every line is a recorded value, not an estimate.
What went wrong, in the agent's own words
The agent's own words, worst first, never rewritten into advice.
- generate_stream abstract method not implemented in 8 source subclasses and 9 test mock classes25 minutes
- Install time
- 3 minutes
- Cold machine to finish
- 55 minutes
- Errors hit and fixed
- 1 hit, 1 fixed with no human help
- How the result was proved
- uv run pytest -q --tb=no reported 1500 passed, 1 failed (pre-existing bedrock streaming test), 4 skipped; uv run mcp-agent --version returned 'mcp-agent 0.2.6'; Python import of all 8 affected classes succeeded
- Model tokens used
- 931,422
- Exact commit tested
- f62d84935081
- Container image
- sha256:cdd920bce783
What was not measured
The other half, so you can see exactly how far this evidence goes. A gap is printed as a gap and never filled in.
- Peak GPU memory
- this ran on a CPU machine, so there was no GPU to sample
- Behaviour over a long run
- each attempt uses the smallest documented settings and a fixed time budget, so this measures whether it runs, not how it holds up under load
The end of the recording
The last lines of the terminal session that decided this verdict, exactly as they were recorded.
**Install and setup:** - `uv sync --all-extras --all-packages --group dev` succeeded - Python 3.10.22 in venv, using uv from pip **Root cause of test failures:** - `generate_stream` was recently added as `@abstractmethod` to `AugmentedLLM` but 8 source subclass… - This caused Python 3.10 to raise `TypeError: Can't instantiate abstract class ... with abstract m… **Fix applied:** Added stub `generate_stream` implementations (wrapping `generate()` and yielding `StreamEvent` obje… - Source: `augmented_llm_openai.py`, `augmented_llm_google.py`, `augmented_llm_azure.py`, `parallel… - Test mocks: `test_multithread_smoke.py`, `test_token_integration_convenience.py`, `test_deep_orch… **Final test results:** 1500 passed, 1 failed (pre-existing), 4 skipped The single remaining failure (`test_bedrock_streaming.py::test_single_turn_text_streaming`) is pre-…
Replay the whole session, every command from a clean machine to this point.
How it was tested
One agent, one container, no human help. It installs the project the way its own documentation says to, runs it, and fixes what breaks. Everything below is recorded as it happened: the terminal session, the log and the exact commit. The full method.
Strengths and limits
Measured facts, not opinions. How this is written.
What went well
- Reached a running state on a clean machine, with the session recorded.
- Installed in 3 minutes, faster than the median of the 37 comparable projects Argusic has measured.
- Recovered from all 1 error without a human stepping in, which says the failures are documented well enough to solve.
- Ran without a GPU, so it does not need one to start.
- Licensed Apache-2.0, as reported by its host.
- Measured 2 times, so the result is not a one-off.
Of the 38 agents projects Argusic has installed and timed, mcp-agent was the 11th fastest to reach a running state, and 28 of 38 reached one at all.
What it is a reasonable choice for
- Trying it on a laptop or a small server: it reached a running state without a GPU.
- A quick evaluation: it was running 3 minutes after the clone.
Also tested, in the same area
Every one of these was installed and run by Argusic on a clean machine. Nothing appears here that was not tested.
Run history
| tested by | attempt | outcome | score | cost USD | when |
|---|---|---|---|---|---|
| Argusic Runner | 2/3 | Runs | 100.00 | 0.48 | |
| Argusic Runner | 2/3 | Runs with mocks | 92.00 | 0.07 |
Topics (from GitHub)
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Markdown for the project README. It links back here; terms on the terms page.
[](https://argusic.com/subject/mcp-agent)Questions
- Does mcp-agent run?
- Yes. mcp-agent runs. Argusic installed and launched it on a clean machine in 3 minutes, hitting 1 error on the way, and recorded the session.
- How did Argusic test mcp-agent?
- On a fresh, disposable machine, with every command recorded and the repository pinned at commit f62d84935081. 2 attempts are recorded, and the full method is on the methodology page.
- What did it cost to test mcp-agent?
- The run that produced this verdict cost $0.48: real compute and model cost, not a list price per million tokens. It is recorded on that run's page.
- How long does mcp-agent take to install?
- 3 minutes in the run that decided this verdict, from a clean machine to a running state. That is one measured attempt, not an average.
- Does mcp-agent need a GPU?
- Not to start. Argusic reached a running state on a machine with no GPU.
- What goes wrong when installing mcp-agent?
- 1 thing broke in the recorded run, and 1 were fixed without human help. Each one, and the time it cost, is listed on this page.
- How does mcp-agent compare with the alternatives?
- Of the 38 agents projects Argusic has installed and timed, mcp-agent was the 11th fastest to reach a running state, and 28 of 38 reached one at all.
- Where is the evidence for mcp-agent?
- All 2 recorded runs are on this page, each linking to its full log and terminal recording, stored with a sha256 fingerprint so it cannot be quietly altered.