Loading ToolBench
Autonomous GitHub Issue Solver. RAG-powered MCP server that ingests repositories, analyzes issues, generates patches, and creates PRs using AI and semantic search.
Server exhibits significant definition quality gaps. 18 tools total; most lack proper descriptions (many under 20 chars or generic). Input schemas present but sparse, many parameters lack type definitions or descriptions. No documented output schemas. Tool naming is inconsistent (verb_noun mostly present but some tools like 'bash' and 'show_diff' are vague). Critical security issue: bash tool exposes shell execution without constraints. Composition is poor, multiple tools do similar work (ingest_repository_tool vs ingest_repository_docs/code/issues/prs; analyze_github_issue_tool vs analyze_issue). No error handling guidance, no pagination support, no idempotency markers. This server would not pass code review.
Analyzes a GitHub issue by its URL, providing a summary, proposed solution, complexity, and relevant past issues based on repository knowledge. Uses sophisticated analysis with RAG.
Run RAG-powered analysis on a GitHub issue. Returns root cause, affected files, and proposed solution.
Run a shell command. Use for git, tests, gh CLI, and general shell operations.
Replace the first occurrence of `old` with `new` in a file.
Generate AI-suggested code patches for an issue.
Check if a repository has been ingested and its ingestion status.
bash tool exposes arbitrary shell execution with no constraints, validation, or permission gates. Accepts any command string. Critical security vulnerability, agents can delete files, exfiltrate data, or compromise the system.
Multiple tools perform nearly identical ingestion work: ingest_repository_tool, ingest_repository_docs, ingest_repository_code, ingest_repository_issues, ingest_repository_prs. Violates single-responsibility principle. LLM cannot disambiguate which to call.
Duplicate analysis tools: analyze_github_issue_tool and analyze_issue both analyze issues but with different parameter names (issue_url vs url). No output schemas documented. LLM cannot predict what fields are returned.
Inferred effective spec: <=2025-11-25.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-23 | D | 59 | <=2025-11-25 | v2 |
Ingest a repository (docs, code, issues, PRs) into the vector database for RAG.
Ingest source code from a GitHub repository (Step 2 of 4). Analyzes and chunks source code files for context understanding.
Ingest documentation from a GitHub repository (Step 1 of 4). Fetches and embeds README files, wikis, and documentation into the knowledge base.
Ingest issues history from a GitHub repository (Step 3 of 4). Processes recent issues for pattern recognition and context understanding.
Ingest PR history from a GitHub repository (Step 4 of 4 - Final Step). Analyzes pull request history for solution patterns and completes the ingestion process.
Ingests a GitHub repository into the knowledge base for analysis. This should be run first before analyzing issues to build the RAG database.
Read a file's contents.
Semantic search over the ingested codebase using ChromaDB.
Search stored learnings, patterns, and never-do rules for a repository.
Show the current git diff of all changes in the working directory.
Start the multi-step repository ingestion process by validating the repository and setting up the initial state. This is the first step in the multi-step ingestion workflow.
Write content to a file. Creates parent directories if needed.
No output schemas documented for any tool. LLMs cannot plan downstream calls or extract required fields. Violates pattern:tool and pattern:response-shaper. Agents must guess what fields are returned.
No error handling guidance. Tools do not indicate what errors are retryable, user-fixable, or fatal. No recovery suggestions. Agents cannot self-correct on failures.