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Comprehensive MCP server providing filesystem, CLI, git, web scraping, and project management tools for AI coding assistants
This MCP server has significant definition quality gaps across nearly all 21 tools. While tool names are generally action-oriented (validate_project, create_validation_script, execute_command), many descriptions are too short or generic to guide LLM selection. Parameter descriptions are inconsistent, some tools have well-documented parameters (filesystem with action enums, git with multiple action types), but others lack critical constraint documentation. Output schemas are not documented anywhere in the visible code. No evidence of error handling guidance, recovery hints, or actionable error messages. The server exposes sensitive operations (execute_command with arbitrary shell execution, filesystem write, git push) without confirmation mechanisms or destructive operation warnings. Tool compositions show concerning patterns: execute_command and filesystem operations could easily be chained into destructive sequences, but there's no mention of idempotency, confirmations, or rate limiting. Baseline comparison: the average tool description appears 30-80 characters (well below the 194-char median for A+ tools), and parameter annotations are sparse or missing entirely for many tools.
Analyze log files for errors, exceptions, and common issues
Create or update validation scripts in package.json for automated checks
Execute a shell command in the workspace directory
Unified filesystem tool for all file and directory operations. Supports read, write, list, search, info, and create_directory actions.
Find all log files in the workspace
List all authenticated accounts and show the active account
Authenticate with Google Cloud using Application Default Credentials (ADC). This will open a browser for OAuth flow.
execute_command allows arbitrary shell execution with no destructive operation warning, confirmation mechanism, or error guidance. Description does not indicate that this is a high-risk operation.
Output schemas are completely undocumented. No tool in the visible code shows what fields are returned, making it impossible for LLMs to chain tools or extract data. This violates the baseline requirement that 100% of A+ tools have documented return types.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-22 | D | 51 | 2026-07-28+ | v2 |
| 2026-03-09 | F | 44 | - | v1 |
Generate and print an access token for the active account. Useful for API calls.
Generate and print an identity token (JWT) for the active account. Used for authenticating to Cloud Run and other services.
Get the value of a gcloud configuration property
List all gcloud configuration properties
Set a gcloud configuration property (project, region, zone, etc.)
List all GKE clusters in the current project
Read logs from Cloud Logging with filters and time range
Write a log entry to Cloud Logging
Get environment variables
Get compilation errors, linting issues, and other problems from VS Code diagnostics by running TypeScript and ESLint checks
Unified git tool for all version control operations. Supports status, log, diff, branch, commit, push, pull, clone, and stash actions.
Read and parse log files with optional filtering and tail support
Run complete project validation (lint, type-check, test, build)
Find the path to an executable command
Google Cloud authentication tools (gcloud_auth_login, gcloud_auth_print_access_token, gcloud_auth_print_identity_token) expose tokens and secrets in responses. These should never appear in tool output, they enter LLM context and risk leakage.
Many tool descriptions are under 20 characters or lack actionable context. Examples: 'Get environment variables' (26 chars), 'Find the path to an executable command' (38 chars), 'List all authenticated accounts' (30 chars). These fall far short of the 194-char median for production tools and do not convey when/why to use them instead of related tools.
filesystem and git tools are overloaded with multiple actions (read, write, list, search, info, create_directory for filesystem; status, log, diff, branch, commit, push, pull, clone, stash for git). Each action requires different parameter sets, making the tool interface confusing and difficult for LLMs to select the right action. These should be split into separate, focused tools.
No error handling guidance visible. Tools do not indicate how to recover from failures, whether errors are retryable, or what the LLM should do next. This prevents agents from self-correcting on failures.
No confirmation or dry-run mechanisms for destructive operations. create_validation_script, gcloud_logging_write, gcloud_config_set, and git (commit, push, clone) all modify state but offer no way for the agent to preview changes or confirm before executing.
Parameter descriptions are inconsistent or missing critical constraints. For example, execute_command's 'timeout' parameter has no min/max bounds (should be 1-600000 ms), and filesystem's 'pattern' for glob search has no guidance on performance implications of wildcards. Many parameters lack examples of valid inputs.
gcloud tools require opaque property names (e.g., 'project', 'compute/region'). No guidance on valid property keys or how to discover them. This forces extra lookup calls or hallucinations by the LLM.
No pagination documented for list-returning tools (gcloud_logging_read, find_log_files, gcloud_container_clusters_list, gcloud_auth_list). No limit parameter or cursor guidance. Large result sets will blow context windows.