Loading ToolBench
Agent-Native Open Source Contribution Engine & Model Context Protocol (MCP) Server
OpenContrib MCP server defines 17 tools with generally clear naming (verb_noun pattern: contrib_plan_capabilities, contrib_list_plugins, etc.) and comprehensive descriptions (avg ~180 chars, within 10-1024 baseline). All tools are READ_ONLY, reducing risk. However, critical gaps exist: (1) Input schemas visible for most tools but lack consistent type enforcement and validation guidance; (2) Output schemas are NOT documented, LLMs cannot predict response structure for chaining; (3) Error handling descriptions are absent, tools do not guide recovery; (4) Parameter descriptions exist but many lack format/constraint details (e.g., 'targetPath' lacks validation rules, 'intent' enum values documented but not enforced in schema); (5) No tool annotations (readOnlyHint, idempotentHint) despite all being read-only. Tools like contrib_eval_prepare_judge and contrib_audit_governance have complex nested input objects but no output schema documentation. Composition is strong (tools chain logically: plan → list → info → assess → qualify → diagnose → rank → eval → evidence → governance), but lack of output schemas breaks downstream planning.
Analyze patch for cross-platform anti-patterns (e.g. filepath.ToSlash on Linux, CRLF regex bugs) and identify overlooked sibling files
Assess OS and toolchain execution feasibility for an issue against local machine (Windows/WSL/Docker/Mac)
Audit patch diff size, anti-AI text patterns, and compute evidence-backed 7D quality rubric & confidence breakdown
Capture immutable RED baseline (failing test + source tree hash) before applying fix
Diagnostic-only evidence inspection; canonical runs must use contrib_capture_red followed by contrib_verify_green
Parse GitHub Actions or local CI terminal raw logs to extract exact failing test names, line numbers, and root-cause summaries without guessing
Output schemas not documented. LLMs cannot predict response structure, breaking tool chaining and forcing agents to guess field names. Tools like contrib_rank_opportunity, contrib_eval_parse_judgment, and contrib_audit_governance return complex objects but provide no schema.
Error handling guidance absent. Tools do not describe what errors can occur, whether they are retryable, or what the LLM should do next. No recovery paths documented.
Parameter constraints not enforced in schema. 'intent' parameter in contrib_plan_capabilities lists enum values in description but schema does not enforce them. 'targetPath' lacks validation rules (must be absolute? relative? must exist?). 'stressLoopCount' has min/max in schema but no guidance on what values mean.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-23 | C | 60 | 2025-06-18+ | v2 |
Diagnose repo workflows, package.json, pyproject.toml, and Cargo.toml for <=100 line PR improvement suggestions
Audit host environment health (Git, Bun/Node, Docker, WSL, and OpenContrib storage)
Parse and validate the raw JSON response from the neutral judge sub-agent. Input: the raw text output from the judge sub-agent. Output: a validated, structured JudgeEvaluationReport with weighted overall score, weakest-dimension gate applied, and reflexion directives ready for memory ledger ingestion.
Compress a conversation transcript JSONL into a structured G-Eval judge prompt. Returns: (1) a compressed trajectory summary text, (2) a fully-formatted judge prompt that you must feed to a **neutral, independent sub-agent** (not yourself) for blind evaluation. The sub-agent should return a JSON evaluation report conforming to JudgeOutputSchema. This tool performs ZERO scoring itself — all judgment comes from the LLM that reads the prompt.
List all active Microkernel security plugins, AST analyzers, and registered probe adapters
Evaluate repository fingerprint and agent intent to generate an optimized, scored capability routing plan across security, concurrency, and defect domains
Get detailed capability schema, supported languages, cost metrics, and defect patterns for a specific active probe or plugin
Verify if an issue is safe and qualified for external contribution (checks author-first-right, anti-bandwagoning, and blocking labels)
Extract objective multi-dimensional probability signals (skill match, feasibility, issue clarity, actionability, repo health) without prescribing binary decisions
Render a clean, human-engineered PR description adhering strictly to target repository template or Master 6-Tier standard
Verify GREEN baseline (passing test + source tree hash) after applying fix; compare against RED to confirm reproducibility
Tool annotations missing. All 17 tools are READ_ONLY but none declare readOnlyHint in schema. This prevents LLMs from optimizing retry logic and understanding safety guarantees.
Complex nested input objects lack field-level descriptions. contrib_rank_opportunity accepts 'issue' and 'repository' objects with nested properties, but descriptions do not explain which fields are required vs optional or what happens if fields are missing.