mcp-zen-of-languages
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Instructions
Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.
This server publishes no instructions, or was last inspected before Glama recorded them.
Capabilities
Features and capabilities supported by this server
Protocol revision2025-11-25
| Capability | Details |
|---|---|
| tools | {
"listChanged": true
} |
| logging | {} |
| prompts | {
"listChanged": false
} |
| resources | {
"subscribe": false,
"listChanged": false
} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| detect_languagesB | Return supported language list for analysis. |
| analyze_zen_violationsC | Analyze a code snippet against zen rules with stricter request-quality guardrails and richer telemetry metadata. |
| generate_promptsB | Generate remediation prompts with MCP-first guidance metadata and v2 versioned prompt semantics. |
| analyze_repositoryB | Analyze a repository path and return per-file analysis results. |
| analyze_batchA | Analyse a repository path and return token-budgeted, paginated violations designed for LLM context windows. Highest-severity violations appear first. Pass the returned cursor to resume from the next page. |
| analyze_batch_summaryA | Return a compact project health score and top-5 hotspot files from a repository scan. Always fits within a single LLM context window. Use this before analyze_batch to decide whether full pagination is needed. |
| analyze_batch_autoA | Smart entry point for LLM agents: automatically decides between returning all violations at once (small repos) or paginating (large repos). Pass the returned cursor back to continue pagination if has_more is true. Prefer this over manually choosing between analyze_repository and analyze_batch. |
| generate_agent_tasksA | Convert zen violations into structured agent task lists for automated remediation. |
| check_architectural_patternsA | Return detected architectural patterns for a code snippet. |
| generate_reportA | Generate a markdown/json report with gap analysis and prompts. |
| export_rule_detector_mappingA | Generate rule-detector mapping JSON from the live registry. |
| get_configA | Return the current server configuration including any runtime overrides. |
| set_config_overrideA | Override configuration values for a specific language at runtime. Overrides persist for the session. |
| clear_config_overridesA | Clear all runtime configuration overrides, reverting to zen-config.yaml defaults. |
| onboard_projectA | Get interactive onboarding guidance for setting up zen analysis on a project. Returns recommended configuration based on project characteristics. |
| get_supported_languagesA | Return list of all languages with zen rules and their detector coverage. |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
| zen_remediation_prompt | Generate a remediation prompt scaffold for violations in a language. |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
| zen_config_resource | Read-only resource exposing current configuration and active overrides. |
| zen_languages_resource | Read-only resource listing language principle and detector coverage counts. |
TDQS
Scored across 16 tools
The repository analysis tools form a confusing cluster: analyze_repository, analyze_batch, analyze_batch_summary, and analyze_batch_auto all analyze a repository but differ in output format, pagination, and token budget. Additionally, detect_languages and get_supported_languages both return language lists with only subtle differences. This overlap creates real ambiguity for an agent despite the descriptive text.
All tool names consistently use a verb_noun structure with snake_case, such as analyze_repository, set_config_override, and onboard_project. The pattern is predictable and easy to navigate.
At 16 tools, the server is slightly over the ideal 3-15 range, but the tools cover distinct aspects of the analysis workflow: snippet analysis, repository analysis, batch processing, reporting, configuration, and onboarding. The count feels justified rather than bloated.
The tool set covers the core workflow of language detection, code and repository analysis, paginated scanning, summarization, remediation prompt generation, agent task creation, and configuration management. Minor gaps exist, such as no explicit rule-listing tool, but the export_rule_detector_mapping and get_supported_languages partially compensate.