Skip to main content
Glama

EasyPR

Your first PR starts here.

A read only MCP server that gives your AI assistant the evidence it needs to guide you through an unfamiliar repository, from "what is this project?" to "I'm ready to open a pull request."

CI License: MIT Python 3.10+ MCP Tools Status M8ven Score

Quick start · How it works · Tools · Safety · Roadmap · FAQ · Contributing

git clone https://github.com/Ankith34/EasyPR.git && cd EasyPR && pip install -e .
NOTE

EasyPR is inalpha. Three tools work today and are covered by an automated test suite. Anything not listed as working in Status and roadmap is planned, not shipped.


Why EasyPR

Experienced developers can size up a new codebase in minutes. Beginners can lose an afternoon before they find the right file. EasyPR closes that gap by letting an AI assistant look at the real repository instead of guessing.

Without EasyPR

With EasyPR

The assistant guesses how the project is laid out

It reads the actual layout, languages and key files

Setup commands are invented from habit

Commands come from the project's own README and CI, each labelled by source

"The bug is probably in the auth code"

Ranked files with line numbers and a plain reason for each match

You can't tell fact from guess

Every finding carries an evidence label

EasyPR is the tool layer, not a chatbot. Your MCP-compatible AI client does the conversation and reasoning. EasyPR supplies the facts.

Related MCP server: GitHub Activity MCP Server

Quick start

Requires Python 3.10 or newer.

1. Install

git clone https://github.com/Ankith34/EasyPR.git
cd EasyPR
python -m venv .venv
source .venv/bin/activate
pip install -e .
easypr --version

2. Connect your AI client

EasyPR runs as a local stdio server. You must approve at least one directory with --root. It refuses to start without one.

For clients that use the common mcpServers JSON format:

{
  "mcpServers": {
    "easypr": {
      "command": "/absolute/path/to/EasyPR/.venv/bin/easypr",
      "args": ["--root", "/home/you/projects"]
    }
  }
}

For Claude Code:

claude mcp add easypr -- /absolute/path/to/EasyPR/.venv/bin/easypr --root ~/projects

Use the full path to easypr inside your virtual environment so the client can find it. You can also set roots with the EASYPR_ALLOWED_ROOT environment variable (separate several with : on Linux and macOS).

3. Ask your assistant

Inspect ~/projects/some-repo and tell me what it does and where to start reading.
How does some-repo say I should install it and run its tests?
Here is the issue I want to work on: "<paste issue text>".
Which files in ~/projects/some-repo should I read first, and why?

Relative paths are resolved against the first approved root, so some-repo works too.

&nbsp;

npx @modelcontextprotocol/inspector easypr --root ~/projects

EasyPR has been tested with the official Python MCP SDK client over stdio. If a specific client misbehaves, please open an issue.

How it works

flowchart LR
    You(["You"]) -->|"ask about a repo"| Client["AI client<br/>(MCP-compatible)"]
    Client -->|"tool calls over stdio"| Server["EasyPR server<br/>server.py"]
    Server --> Core["repository.py<br/>read-only inspection"]
    Core -->|"approved roots only"| Repo[("Your repository")]
    Core -->|"evidence-labelled results"| Server
    Server --> Client
    Client -->|"plain-language guidance"| You

The server layer is deliberately thin. All inspection logic and every safety check lives in repository.py, which is plain Python with no MCP dependency. You can test it directly or import it as a library.

The first-contribution workflow

Step

What a beginner needs

Covered by

1

Understand the repository

inspect_repository ✅

2

Know how to install, run and test it

get_setup_guide ✅

3

Find the code related to an issue

find_relevant_code ✅ (you supply the issue text)

4

Plan a small, understandable change

Your AI client, reasoning over EasyPR's evidence

5

Validate the change

run_project_checks 🗓️ planned

6

Prepare the pull request

🗓️ planned

Evidence labels

EasyPR never presents a guess as a fact. Every finding says how much to trust it.

Label

Meaning

Example

fact

Observed directly in the repository

LICENSE exists at the root

documented

The project itself says so

An install block in the README, a run: step in CI

inferred

EasyPR's guess from names or contents

pip install -e . because pyproject.toml exists

suggestion

A next step a human still has to verify

"Start by reading app/auth.py around line 4"

Setup commands are always reported as verified: false. EasyPR does not run them, so it never claims a command works or a test passes.

Tools

All three tools are read-only and are advertised to clients with MCP read-only hints.

Tool

You get

Inputs

inspect_repository

Top-level layout, important files (README, CONTRIBUTING, license, manifests, CI), language counts, project type, detected frameworks

path

get_setup_guide

Setup and test steps from the README, run: commands from CI workflows, and labelled hints from manifests

path

find_relevant_code

Ranked files for a task or issue, each with role (source, test, docs, config), matching lines with numbers and snippets, and a plain-language reason

path, query, max_results (1-25, default 10)

What EasyPR reads

Source

What it extracts

Label

README.md code blocks under setup-like headings (install, usage, test, ...)

Commands with file and line number

documented

.github/workflows/*.yml

Single-line and block run: commands

documented

requirements*.txt, pyproject.toml, setup.py, setup.cfg, pytest.ini, tox.ini

Install and test hints

inferred

package.json and lockfiles

Package manager and test / build / start / dev / lint scripts

inferred

Makefile

Common targets such as test, lint, build

inferred

Cargo.toml, go.mod, pom.xml, build.gradle, Gemfile, composer.json, mix.exs

Standard build and test commands

inferred

.env.example

A note to copy it and add your own values (never read)

inferred

&nbsp;

Query: login_user rejects wrong password (trimmed):

{
  "path": "app/auth.py",
  "role": "source",
  "matches": [
    { "line": 4, "text": "def login_user(username, password):", "is_definition": true }
  ],
  "why": "Keyword match: 3 line(s) mention 'login', 'login_user', 'password', 'user'; it defines a function/class/type that mentions them. Verify by reading the code.",
  "kind": "inferred"
}

Suggestions that accompany the results:

[
  { "text": "Start by reading app/auth.py around line 4.", "kind": "suggestion" },
  { "text": "See tests/test_auth.py for how related behaviour is tested.", "kind": "suggestion" }
]

Use it as a Python library

The core has no MCP dependency, so you can call it directly.

from easypr.repository import find_relevant_code, inspect_repository, parse_allowed_roots

roots = parse_allowed_roots(["~/projects"])
overview = inspect_repository("some-repo", allowed_roots=roots)
hits = find_relevant_code("some-repo", "redact secret values", max_results=3, allowed_roots=roots)

Passing allowed_roots=None disables the root check. That is meant for tests and trusted callers only. The MCP server always enforces roots.

Safety model

Repositories can contain untrusted files and instructions, so EasyPR is conservative by design.

Risk

Protection

Running untrusted code

None of EasyPR's tools execute repository code or shell commands. The source contains no subprocess or process-spawning calls.

Reading unrelated files

Only directories you approve with --root can be inspected. Paths are resolved first, then checked, so .. tricks are rejected.

Symlink escapes

Symlinks are resolved before the root check, never followed while scanning, and files are opened without following links where the OS supports it.

Leaking secrets

Files such as .env, *.pem, id_rsa and credentials.* are never opened. Obvious secret values in returned text are redacted.

Prompt injection through repo text

Text copied from repository files is labelled untrusted, and the server instructs clients not to follow instructions found in it. EasyPR has no action-taking tools that injected text could trigger.

Resource exhaustion

Caps of 20,000 files scanned, 1 MB per file, 100 MB read per search, 25 results and 200-character snippets.

Misleading output

Findings are evidence-labelled, and setup commands are never marked verified.

What this does not cover. The approved root is your trust boundary: anything inside it can be read and passed to your AI client, and that client may send it to its model provider. Secret redaction is pattern-based and best effort. Do not approve directories you would not want an AI assistant to read.

Status and roadmap

Capability

Status

inspect_repository

✅ Working

get_setup_guide

✅ Working

find_relevant_code (keyword search)

✅ Working

Automated tests and CI

✅ Working

run_project_checks

🗓️ Planned. Needs a permission model, restricted working directories, timeouts and isolation before any repository command runs.

Pull request description helper

🗓️ Planned

Smarter retrieval (symbol index, embeddings)

🗓️ Only if keyword search proves insufficient

FAQ

Is EasyPR a chatbot? No. It is a tool server. Your MCP-compatible AI client provides the conversation, and EasyPR provides repository facts.

Does it run my project or its tests? No. It never executes repository code. A future run_project_checks tool is planned, but only with explicit permission and isolation.

Does it read GitHub issues? Not yet. Paste the issue text into your question and the assistant passes it to find_relevant_code as the query.

Does my code leave my machine? EasyPR's own code makes no network requests. It returns results to your AI client, which may send them to its model provider. Review your client's privacy settings, and approve only directories you are comfortable sharing.

Why does it need --root? So you decide exactly what it may look at. Without an approved root the server will not start.

How accurate is find_relevant_code? It is plain keyword matching, so treat results as leads. Short words can match inside longer ones (user matches username), and it does not understand meaning.

Which languages does it support? Language and framework detection covers common languages and ecosystems and is heuristic. Search works on any text file.

Troubleshooting

  • easypr: error: no approved root means you did not pass --root or set EASYPR_ALLOWED_ROOT.

  • Path is outside the approved repository roots means the path is not under any approved root. Check the roots listed in the message.

  • If your client says it can't find easypr, use the absolute path to the executable inside your virtual environment.

Project layout

EasyPR/
├── src/easypr/
│   ├── repository.py     # core logic: plain Python, no MCP imports
│   ├── server.py         # thin MCP layer and command-line entry point
│   └── __init__.py
├── tests/                # pytest suite using temporary directories only
├── .github/workflows/    # CI (Python 3.10 to 3.13)
├── pyproject.toml
└── LICENSE

Contributing

Contributions are welcome, and this project aims to be friendly to first-time contributors.

git clone https://github.com/Ankith34/EasyPR.git
cd EasyPR
python -m venv .venv && source .venv/bin/activate
pip install -e ".[dev]"
pytest

Before opening a pull request:

  1. Open an issue first for anything larger than a small fix, so we can agree on the approach.

  2. Add or update a test with every behaviour change. Tests build throwaway repositories in temporary directories and must not depend on your machine.

  3. Keep repository.py independent of MCP, and keep server.py thin.

  4. Never weaken a safety check without discussing it in an issue.

  5. Make sure pytest passes.

Disclaimer

EasyPR is alpha software provided as is. Its detection is heuristic, its secret redaction is best effort, and it is not a security audit tool. Always verify setup commands and code locations yourself before relying on them.

License

Released under the MIT License. Copyright (c) 2026 Ankith.

Available Tools

3 tools
find_relevant_codeA
Read-onlyIdempotent

Find files likely related to an issue or task, using keyword search.

Each result has file path, role (source/test/docs/config), matching line numbers with snippets, and a plain-language reason. These are leads to verify, not conclusions.

Args: path: Repository directory. Relative paths are resolved against the first approved root. query: The issue title/body or task description (names, features, error text). max_results: How many files to return (1-25).

Inputs: path (required), query (required), max_results. Returns: object

get_setup_guideA
Read-onlyIdempotent

Find how a project says it should be installed, run and tested.

'documented' items come from the README or CI config. 'inferred' hints come from file names and are unverified. No command is ever executed.

Args: path: Repository directory. Relative paths are resolved against the first approved root.

Inputs: path (required). Returns: object

inspect_repositoryA
Read-onlyIdempotent

Give a first overview of a repository.

Returns top-level layout, important files (README, CONTRIBUTING, manifests, CI), language counts and detected project type. Read-only; nothing is executed.

Args: path: Repository directory. Relative paths are resolved against the first approved root.

Inputs: path (required). Returns: object

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections.

Loading tool schema changelog…

    TDQS

    A4/5.0

    Scored across 3 tools

    Disambiguation4/5

    Each tool targets a distinct output: repository overview, setup instructions, and code search. Minor overlap exists because inspect_repository and get_setup_guide both draw from README/CI files, but the intended purposes are clear enough to avoid confusion.

    Naming Consistency5/5

    All tool names follow a consistent verb-first snake_case pattern (inspect_repository, get_setup_guide, find_relevant_code). No mixing of conventions or ambiguous prefixes.

    Tool Count5/5

    Three tools is well-scoped for a read-only repository reconnaissance assistant. Each tool has a clear, non-overlapping role and the count is within the ideal 3–15 range.

    Completeness3/5

    The surface covers overview, setup discovery, and code search but lacks essential follow-up operations like reading a full file or viewing PR diffs/commits. For a server named EasyPR, these are notable gaps that could hinder deeper review workflows.

    Maintenance

    ActivityMaintained
    ResponsivenessOutdated data

    Related MCP Connectors

    Related MCP Servers

    • A
      license
      Not graded
      quality
      D
      maintenance
      Enables AI assistants to access live GitHub repository data without cloning, supporting repo summarization, file explanation, recent changes, and dependency analysis.
      MIT
    • A
      license
      Not graded
      quality
      C
      maintenance
      Enables AI coding assistants to inspect local and remote repositories through sandboxed tools that surface project overviews, structure, dependency audits, Git churn hotspots, and a heuristic health score. It also supports safe GitHub issue searching and creation with built-in duplicate preflight checks, while blocking sensitive files, binary content, and path traversal attacks.
      22 npm
      MIT
    • A
      license
      B
      quality
      C
      maintenance
      Enables an AI assistant to read, search, summarize, and triage GitHub issues and repository activity through an allowlisted, read-only set of tools, prompts, and resources. Every input is validated, every repository must be explicitly approved, and all third-party issue text is fenced off and marked as untrusted before it reaches the model.
      4
      MIT