Ultra MCP
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
Server capabilities have not been inspected yet.
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| deep-reasoningC | Use advanced AI models for deep reasoning and complex problem-solving. Supports GPT-5 for OpenAI/Azure and Gemini 2.5 Pro with Google Search. |
| investigateC | Investigate topics thoroughly with configurable depth |
| researchC | Conduct comprehensive research with multiple output formats |
| list-ai-modelsB | List all available AI models and their configuration status |
| analyze-codeC | Analyze code for architecture, performance, security, or quality issues |
| review-codeC | Review code for bugs, security issues, performance, or style problems |
| debug-issueC | Debug technical issues with systematic problem-solving approach |
| plan-featureC | Plan feature implementation with step-by-step approach |
| generate-docsC | Generate documentation in various formats |
| challengeC | Challenge a statement or assumption with critical thinking |
| consensusC | Get consensus from multiple AI models on a proposal |
| plannerC | Multi-step planning with revisions and branches |
| precommitC | Pre-commit validation for code changes |
| secauditC | Security audit for code and configurations |
| tracerC | Trace execution flow and debug complex issues |
| ultra-reviewC | Comprehensive code review with step-by-step workflow analysis |
| ultra-analyzeC | Comprehensive code analysis with step-by-step workflow |
| ultra-debugC | Systematic debugging with step-by-step root cause analysis |
| ultra-planC | Multi-step feature planning with revisions and branches |
| ultra-docsC | Generate comprehensive documentation with step-by-step workflow |
| index-vectorsC | Index project files for semantic search using vector embeddings |
| search-vectorsB | Search for files and code snippets using natural language queries |
| clear-vectorsC | Clear all indexed vectors for a project |
| ultra-challengeC | Challenges a statement or assumption with critical thinking to prevent reflexive agreement |
| ultra-continuationC | Continue a conversation with context from a previous session, enabling context revival across interactions |
| ultra-sessionC | Manage conversation sessions for persistent context and memory |
| ultra-budgetC | Set and monitor conversation budgets for cost and token control |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
| deep-reasoning | Use advanced AI reasoning to solve complex problems requiring deep analysis |
| investigate | Thoroughly investigate any topic with configurable depth of analysis |
| research | Conduct thorough research on any topic with multiple output formats |
| list-ai-models | Show all available AI models and their configuration status |
| analyze-code | Analyze code for architecture, performance, security, or quality issues |
| review-code | Review code for bugs, security issues, performance, or style problems |
| debug-issue | Debug technical issues with systematic problem-solving approach |
| plan-feature | Plan feature implementation with comprehensive step-by-step approach |
| generate-docs | Generate comprehensive documentation in various formats |
| challenge | Challenge a statement or assumption with critical thinking |
| consensus | Get consensus from multiple AI models on a proposal or decision |
| planner | Create detailed multi-step plans with revisions and branching support |
| precommit | Validate code changes before committing with comprehensive checks |
| secaudit | Comprehensive security audit for code and configurations |
| tracer | Trace execution flow and debug complex code relationships |
| ultra-review | Comprehensive step-by-step code review with detailed analysis |
| ultra-analyze | Deep step-by-step code analysis with architectural insights |
| ultra-debug | Systematic step-by-step debugging with root cause analysis |
| ultra-plan | Advanced multi-step feature planning with revisions and branches |
| ultra-docs | Comprehensive step-by-step documentation generation |
| index-vectors | Index project files for semantic search using vector embeddings |
| search-vectors | Search indexed code files using natural language queries |
| clear-vectors | Clear all indexed vectors for a project |
| ultra-challenge | Challenge statements with critical thinking to prevent reflexive agreement |
| ultra-continuation | Continue conversations with session context revival |
| ultra-session | Manage conversation sessions for persistent memory |
| ultra-budget | Manage conversation budgets for cost and token control |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
No resources | |
TDQS
Scored across 27 tools
Multiple tools have overlapping purposes, causing significant ambiguity. For example, 'analyze-code' and 'ultra-analyze' both handle code analysis, while 'debug-issue' and 'ultra-debug' both address debugging. The 'ultra-' prefixed tools often duplicate core functions without clear distinctions, making it difficult for an agent to choose between them.
Naming conventions are inconsistent, mixing hyphenated names (e.g., 'analyze-code') with 'ultra-' prefixed versions (e.g., 'ultra-analyze') and some standalone terms (e.g., 'planner', 'tracer'). There is no uniform verb_noun pattern, and the duplication between core and 'ultra-' tools adds to the confusion rather than following a predictable structure.
With 27 tools, the count is excessive for a coherent set, as many tools overlap in functionality (e.g., multiple analysis, debugging, and planning tools). This bloat suggests poor scoping, where the server tries to cover too many similar tasks with redundant tools, making it heavy and inefficient for agents to navigate.
The tool set covers a broad range of development and AI-related tasks, such as code analysis, debugging, planning, and research, with no obvious major gaps in core workflows. However, the redundancy and lack of clear domain boundaries make it hard to assess true completeness, as overlapping tools might obscure missing operations rather than providing comprehensive coverage.