OpenCollab MCP server surfaces mergeable open source issues in 30 seconds
Prakhar, a CS student at IIT Guwahati, built OpenCollab MCP — an MCP server exposing 22 tools that chains GitHub data lookups to surface actually-available open source issues and generate PR plans in under 30 seconds.
Score breakdown
Agentic coding practitioners building or evaluating MCP servers can study OpenCollab's architecture — parallel `asyncio.gather` API calls, Pydantic input validation with `extra="forbid"`, and a hand-rolled TTL cache — as a concrete, production-minded pattern for wrapping external APIs as MCP tool suites.
- 01OpenCollab MCP exposes 22 tools across four categories: Discovery, Evaluation, Issue Intel, and Profile.
- 02Key tool chain: `match_me` → `find_issues` → `check_issue_availability` → `issue_complexity`, returning results in under 30 seconds.
- 03`generate_pr_plan` fetches the issue body, comments, CONTRIBUTING.md, repo structure, and default branch to support PR drafting.
Prakhar, a CS student at IIT Guwahati, built OpenCollab MCP after spending three weekends fruitlessly hunting for open source issues to contribute to. The recurring problems — issues already assigned, PRs already open, repos abandoned, or required languages outside his practical skill set — led him to build an MCP server that filters GitHub data programmatically rather than relying on manual browsing. The server exposes 22 tools that an MCP client (Claude, Cursor, or any compatible client) selects and chains autonomously. A typical query like "find me a Python good-first-issue I can finish this weekend with nobody working on it" triggers `match_me` to identify the user's strongest language from their GitHub profile, `find_issues` to search relevant issues, `check_issue_availability` to verify no one is actively working on each candidate, and `issue_complexity` to rate difficulty on a 1–10 scale — returning five actionable results in under 30 seconds.
On the engineering side, Prakhar used `asyncio.gather` to parallelize previously sequential GitHub API calls, achieving a 3–5x latency improvement on heavy paths.
The 22 tools are organized into four groups: Discovery (6 tools including `find_issues`, `trending_repos`, and `match_me`), Evaluation (7 tools including `repo_health`, `contribution_readiness`, and `dependency_check`), Issue Intel (6 tools including `check_issue_availability`, `issue_complexity`, and `generate_pr_plan`), and Profile (3 tools including `analyze_profile` and `first_timer_score`). On the engineering side, Prakhar used `asyncio.gather` to parallelize previously sequential GitHub API calls, achieving a 3–5x latency improvement on heavy paths. A hand-rolled 5-minute in-memory TTL cache mitigates GitHub's rate limits without adding external dependencies. All tool inputs are validated with Pydantic models configured with `extra="forbid"` and `str_strip_whitespace=True` to handle stray fields or whitespace that LLM clients sometimes pass. The full test suite of 45 tests runs in 0.12 seconds using `httpx.MockTransport` with no real network calls. OpenCollab MCP is installable via `pip install opencollab-mcp` or `uvx opencollab-mcp`, and planned future tools include `first_pr_generator`, `track_my_prs`, and `skill_gap`.
Key facts
- 01OpenCollab MCP exposes 22 tools across four categories: Discovery, Evaluation, Issue Intel, and Profile.
- 02Key tool chain: `match_me` → `find_issues` → `check_issue_availability` → `issue_complexity`, returning results in under 30 seconds.
- 03`generate_pr_plan` fetches the issue body, comments, CONTRIBUTING.md, repo structure, and default branch to support PR drafting.