gitworthy
Enables coding agents to scout, rank, and preflight GitHub issues and pull requests by checking linked work, branches, releases, and contribution policy, then returns evidence-backed ACT, VERIFY, or SKIP decisions.
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@gitworthyhunt for good first issues worth working on in vercel/next.js"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
Why Gitworthy
Coding agents make implementation cheap. Choosing the wrong task is still expensive.
An issue can look open while:
a fix already exists on an unlinked branch;
another contributor has an active pull request;
the change landed on
mainbut has not been released;the repository requires assignment before accepting work;
a duplicate or abandoned implementation already contains the answer.
Gitworthy performs that preflight before a human or agent spends the next unit of work.
Related MCP server: Hivelore
How agents use Gitworthy
The agent owns the loop. Gitworthy is the MCP decision engine it calls to scout and validate work.
Contribution goal, repository, or organization
│
▼
Cursor / ChatGPT / Hermes / agent
│
Gitworthy MCP
│
▼
policy → scan → rank → preflight
│
┌────────┼────────┐
▼ ▼ ▼
ACT VERIFY SKIP
queue it inspect drop it
│
▼
optional `portfolio`
(mode + dispatch, not a new verdict)
│
▼
Ranked candidate shortlist
│
▼
Select a candidate
│
▼
recheck → reproduce → implement → reviewThere are two distinct lanes:
Scout lane: discover, filter, rank, preflight, and return a shortlist.
Execute lane: select, record intent, recheck fresh state, reproduce, implement, review, and submit through the repository's allowed contribution path.
ACT means “worth considering,” not “start coding blindly.” Scout results go stale, so Gitworthy should be run again when an agent actually claims or begins a target.
See docs/AGENT_WORKFLOW.md for the complete agent policy, tool-selection guide, and scout-to-execute handoff.
Use it with Cursor, ChatGPT, Hermes, or any MCP client
Local stdio (desktop / agent VM)
{
"mcpServers": {
"gitworthy": {
"command": "npx",
"args": ["-y", "gitworthy@latest", "mcp"],
"env": {
"GITHUB_TOKEN": "github_pat_..."
}
}
}
}Streamable HTTP (Cloud Agents / mobile / iPad)
Run or deploy the same engine over HTTP and point the client at a URL:
export GITWORTHY_MCP_TOKEN="$(openssl rand -hex 32)"
export GITHUB_TOKEN="github_pat_..."
npx -y gitworthy@latest mcp --http --host 127.0.0.1 --port 8787{
"mcpServers": {
"gitworthy": {
"url": "https://mcp.example.com/mcp",
"headers": {
"Authorization": "Bearer ${env:GITWORTHY_MCP_TOKEN}"
}
}
}
}Non-loopback binds require GITWORTHY_MCP_TOKEN. Keep GITHUB_TOKEN on the server. Full deploy and Cloud Agent notes: docs/HTTP_MCP.md. Tool descriptions, annotations, and structured results: docs/MCP.md. Portable Agent Plugins packaging (skill + MCP): docs/AGENT_PLUGINS.md.
Give the agent a policy like this:
Use Gitworthy before investing in external repository work. Use
huntto build a short ranked queue. Read each candidate'sworth_check;huntdoes not produce one global verdict. TreatACTas a queue candidate, perform the named checks onVERIFY, and removeSKIPcandidates. Before implementing a selected target, rerunworth_checkand follow the repository's contribution policy.
The token only needs read access to public repositories. For the most accurate linked-work detection, use a classic PAT or a fine-grained token with Issues: Read so Gitworthy can see timeline cross-references. Weaker tokens still work, but missing visibility is reported in not_checked.
Try it in 30 seconds
Check that your environment is ready:
npx -y gitworthy@latest doctor --jsonEvaluate a specific issue:
npx -y gitworthy@latest check owner/repo#123Render a deterministic handoff brief from a stored decision:
npx -y gitworthy@latest brief decision_abc123 --format markdown --out brief.mdBrief generation is read-only over the local store and never calls GitHub, npm, or an LLM. Stored decisions are considered stale after 24 hours; stale briefs warn and recommend recheck.
Scout and preflight a short list of contribution targets:
npx -y gitworthy@latest hunt owner/repo --json
npx -y gitworthy@latest hunt openclaw --max-checks 3 --jsonUse @latest for exploration. Pin a version in repeatable agent workflows and CI.
What hunt actually returns
hunt is a bounded funnel, not a bulk verdict command:
contribution policy
↓
scan repository or organization
↓
rank issue quality and optional skill fit
↓
remove obvious land-only, assigned, weak, or known-SKIP rows
↓
run worth_check on only the best few candidates
↓
return candidates with individual verdicts and evidenceThe agent should read each hunt_candidate.worth_check. Gitworthy intentionally avoids running an expensive full preflight on every issue returned by a broad scan.
ACT, VERIFY, or SKIP
Verdict | Scout behavior | Execution behavior |
ACT | Add it to the ranked queue after reading evidence. | Recheck before claiming or implementing; proceed only if fresh state and policy still allow it. |
VERIFY | Surface it with the unresolved checks named. | Resolve those checks before forking or making a public claim. |
SKIP | Remove it from the queue. | Do not begin parallel work; inspect the cited work or choose another target. |
A verdict is not a vibes-based score. Gitworthy returns structured evidence, the checks it completed, and the checks it could not complete.
A real preflight
A target issue in block/buzz appeared open, but a prior implementation already existed in a pull request using nearly the same title.
Target: block/buzz#1659
Verdict: VERIFY
Disposition: review
Evidence:
- An implementation exists in PR #1675.
- The pull request is linked work, not a duplicate issue.
Next action:
Inspect the current implementation before starting parallel work.That case also exposed a false-positive path in duplicate detection: GitHub's issues API includes pull requests. The fix became a regression test and calibration case.
More real contribution sessions are documented in CASE_STUDIES.md.
How it works
Gitworthy combines bounded checks that answer different ways a task can be unsafe or wasteful to start.
Check | What it protects against |
Linked work | Existing pull requests, assignments, referenced commits, and prior attempts |
Branch scan | Work already present on unlinked remote branches |
Issue vs. main | Fixes that landed while the issue remained open |
Release gap | Changes on |
Contribution policy | Claim requirements, assignment rules, and repositories that do not accept pull requests |
Duplicate analysis | Related or previously reported issues |
Scan / org scan | Unranked issue tracker noise |
Hunt | Wasteful broad checking by narrowing candidates before expensive preflight |
The CLI and MCP server are thin adapters over the same TypeScript core.
Trust model
Gitworthy is designed to be useful without pretending to know more than it checked.
Heuristic evidence cannot independently create a hard
SKIP.Hard
SKIPrequires definitive evidence.Failed mandatory checks cap the result at
VERIFY.Every real result includes meaningful
checkedandnot_checkedfields.Human-readable prose is never parsed to decide the verdict.
Target repositories and packages are treated as hostile input.
Telemetry is off by default. The MCP path emits no telemetry.
Scout results are not treated as permanent; execution requires fresh revalidation.
ACT does not mean “blindly start coding.” Read the evidence, disposition, reasons, linked work, and repository policy before investing.
Agent-native output
Every core result includes a structured envelope:
{
"verdict_summary": "one sentence",
"evidence": [],
"signals": [],
"checked": [],
"not_checked": [],
"cached": false,
"fetched_at": "2026-01-01T00:00:00.000Z"
}signals is the load-bearing input to the decision policy. checked and not_checked are part of the result, not footnotes.
Common commands
gitworthy check owner/repo#123 [--npm-package name] [--json]
gitworthy hunt owner/repo [--manifest path] [--max-checks 3] [--json]
gitworthy portfolio owner/repo [--max-items 10] [--json]
gitworthy prs owner/repo [--json]
gitworthy ci-triage --head-check test:failure --base-check test:success [--json]
gitworthy history owner/repo --path src/core/hunt.ts [--json]
gitworthy opportunity-ingest --source hermes-eval --id case-9 [--json]
gitworthy scan owner/repo [--label "help wanted"] [--json]
gitworthy org owner [--manifest path] [--max-repos 8] [--json]
gitworthy policy owner/repo [--json]
gitworthy init [--user] [--repo] [--json]
gitworthy config validate [--path path] [--manifest path] [--json]
gitworthy config show --effective [--json]
gitworthy profile show [--json]
gitworthy related owner/repo [issue] [--json]
gitworthy ledger show owner/repo#123 [--json]
gitworthy doctor [--json]
gitworthy mcp
gitworthy mcp --http [--host 127.0.0.1] [--port 8787] [--stateless]Exit codes for check:
0— ACT10— VERIFY20— SKIP1— error
See SKILL.md for the strict contribution gates and docs/AGENT_WORKFLOW.md for the explanatory workflow guide.
Documentation
Doc | Topic |
Scout vs execute, tool selection | |
ACT / VERIFY / SKIP and dispositions | |
CLI reference and exit codes | |
MCP tools, annotations, structured results | |
Agent Plugins v1 packaging (skill + MCP) | |
Streamable HTTP MCP | |
Config, manifests, profiles | |
Local store, outcomes, captures | |
Frozen / live / private eval | |
Corpus taxonomy (pass rate vs precision) | |
How to grow Track O vs Track F | |
Outcome calibration (Track O; not a release gate) | |
Design-partner beta (GW-034) | |
Trust boundaries | |
Doctor-driven remediation | |
Pre-1.0 contract policy | |
Counters and budgets | |
Ranking contract | |
1.0 gate checklist |
Configuration
GITHUB_TOKEN/GH_TOKENenables authenticated GitHub REST checks.Config files are data-only JSON. Gitworthy reads user config from
~/.gitworthy/config.jsonand repository-local config from.gitworthy/config.json.Precedence is: CLI/MCP input,
GITWORTHY_*environment variables, repository config, user config, built-in defaults.gitworthy config show --effective --jsonincludes provenance for each effective value.Create a secret-free skeleton with
gitworthy init --repoorgitworthy init --user. Validate withgitworthy config validate.Skill profiles (
profilein config or--skill-profile) can includelanguages,topics,preferred_ecosystems, and avoid lists; they affect scan/hunt ranking inputs only, never hard verdict policy.Target manifests (
--manifest pathormanifest_pathin config) can list repos/orgs, include/exclude filters, per-repo npm package mappings, and target-specific overrides. Ambiguous npm package mappings are rejected. Include/exclude filters are validated and carried as manifest metadata in GW-019; full filtering consumption is deferred to GW-027.Tokens and credentials are never persisted in config or manifests; use environment variables such as
GITHUB_TOKEN/GH_TOKEN.GITWORTHY_MCP_TOKENis the bearer secret for Streamable HTTP MCP (required for non-loopback binds).GITWORTHY_CACHE_DIRoverrides the cache under~/.gitworthy/cache.GITWORTHY_LEDGER_DIRoverrides local scout history under~/.gitworthy/ledger.GITWORTHY_TELEMETRY=onplusGITWORTHY_POSTHOG_KEYrequests optional telemetry.
Optional PostHog telemetry also requires a user-installed posthog-node package. If it is missing, Gitworthy warns once and continues with telemetry disabled.
Without a GitHub token, checks that require GitHub REST return structured errors or explicit not_checked entries. Checks that can use public git or npm endpoints still run.
Current status
Gitworthy is pre-1.0 and actively hardening its decision contract, hostile-input handling, local outcomes, evaluation corpus, and onboarding.
The 1.0 goal is a trustworthy, local-first CLI and MCP decision engine—not a hosted SaaS and not another coding agent.
See ROADMAP.md for the release ladder.
Where this is going
Gitworthy is becoming the agent-native readiness layer between task discovery and implementation:
discover candidate work;
rank the most promising targets;
determine whether each is actually ready;
return a machine-actionable next step;
revalidate selected work before execution;
record what happened;
improve future decisions from real outcomes.
The open-source decision engine stays local-first and MIT licensed. Possible post-1.0 layers include shared outcome history, scheduled hunts, private-repository coordination, and an API for agent dispatch.
Contributing
Gitworthy is built from real cases where apparently open work turned out to be handled, blocked, or unsafe to start.
The most valuable contributions are:
examples where Gitworthy made the wrong decision;
repositories or ecosystems it cannot inspect correctly;
Cursor, ChatGPT, Claude Code, Codex, OpenClaw, Hermes, or other agent integrations;
improvements to evidence quality and failure reporting;
new frozen evaluation cases;
documentation that helps another person get a useful result quickly.
See CONTRIBUTING.md for development setup and trust requirements.
Requirements
Node.js 22 or newer
A GitHub token is strongly recommended for real hunts
Security
Report vulnerabilities privately using SECURITY.md or email security@gitworthy.com.
License
MIT
This server cannot be installed
Maintenance
Resources
Unclaimed servers have limited discoverability.
Looking for Admin?
If you are the server author, to access and configure the admin panel.
Related MCP Servers
- AlicenseNot gradedqualityDmaintenanceEnables AI agents to scan GitHub repositories for security vulnerabilities, deployment blockers, and code quality issues. It provides detailed findings and auto-generated code patches to help developers ensure their code is production-ready.60MIT
- AlicenseAqualityAmaintenanceEnforces team knowledge and workflow policies for AI coding agents by providing context, decisions, and gates before code changes are made.2151Apache 2.0

MatterAI MCP Serverofficial
AlicenseNot gradedqualityCmaintenanceEnables code reviews, implementation planning, and pull request generation for AI agents in IDEs like Cursor and Windsurf.1MIT- AlicenseAqualityAmaintenanceEnables AI coding agents to orchestrate the full software development lifecycle on GitHub, including planning, issue creation, code review, security triage, and release readiness checks.131221MIT
Related MCP Connectors
Agentic code review, no signup to try: reality gates + frontier-model review, with veto.
Codebase intelligence for agents: 152 structured artifacts across 21 programs, one call.
The project brain for AI coding agents — memory, decisions, sprints, knowledge base via MCP.
Latest Blog Posts
- Who's Calling? MCP Hosts Are an Identity Blind Spot (And the Spec Knows It)By Om-Shree-0709 on .mcpAgent IdentityOAuth 2.1
- Your AI Chatbot Just Exposed Your CEO's Salary to an InternBy Om-Shree-0709 on .Agent IdentityMCP SecurityOAuth Delegation
- Why MCP Servers Need Execution Sandboxing (And Why Your Current Stack Isn't Enough)By Om-Shree-0709 on .Agentic AiPrompt InjectionWebAssembly
MCP directory API
We provide all the information about MCP servers via our MCP API.
curl -X GET 'https://glama.ai/api/mcp/v1/servers/trippyogi/gitworthy'
If you have feedback or need assistance with the MCP directory API, please join our Discord server