Axom MCP Server
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": false
} |
| prompts | {
"listChanged": false
} |
| resources | {
"subscribe": false,
"listChanged": false
} |
| experimental | {} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| axom_mcp_memoryA | Store, retrieve, search, and manage persistent memories in the Axom database. Memory Types:
Naming Convention: [type][descriptor][YYYYMMDD] Example: bugfix_auth_timeout_20260203 Content Format (recommended): TASK|APPROACH|OUTCOME|GOTCHAS|RELATED Actions:
|
| axom_mcp_execA | Execute file operations and shell commands with chain-reaction support. Operations:
Chain Reactions: Chain multiple operations together using the chain parameter. Each step can reference the previous result using ${_result} variable substitution. Example: { "operation": "read", "target": "/file.txt", "chain": [ { "tool": "axom_mcp_transform", "args": {"input": "${_result.content}", "output_format": "json"} } ] } Security:
|
| axom_mcp_analyzeA | Analyze code and data with configurable depth and scope. Analysis Types:
Focus Areas:
Depth Levels:
Chain Support: Use chain parameter to automatically act on analysis results. |
| axom_mcp_discoverA | Discover available resources, structures, and capabilities. Discovery Domains:
Filter Options:
Chain Support: Use chain parameter to act on discovered resources. |
| axom_mcp_transformA | Transform data between formats and structures. Supported Formats:
Transformation Rules:
Template Support: Use Jinja2 templates for custom output formatting. Chain Support: Use chain parameter to continue processing transformed data. |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
| memory-workflow | Standard workflow for memory-driven task execution. Use at the start of every task. |
| debug-session | Start a structured debugging session with memory persistence. |
| code-review | Perform a comprehensive code review and store findings. |
| store-pattern | Store a discovered pattern or best practice for future reference. |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
No resources | |
TDQS
Scored across 5 tools
Each tool has a distinct primary purpose: analyze code/data, discover resources, execute operations, manage memories, and transform data. However, there is some potential overlap between 'axom_mcp_analyze' (which can suggest improvements) and 'axom_mcp_transform' (which can restructure data), though their core functions remain separate. The descriptions clearly differentiate the tools, making misselection unlikely.
All tool names follow a consistent 'axom_mcp_' prefix with a descriptive verb suffix (e.g., analyze, discover, exec, memory, transform). This pattern is uniform across all five tools, making them predictable and easy to identify. There are no deviations in naming conventions, ensuring clarity and readability.
With 5 tools, the server is well-scoped for its purpose of code analysis, resource management, and data transformation. Each tool serves a clear and necessary function without redundancy, covering key areas like analysis, discovery, execution, memory storage, and data formatting. This count is appropriate and avoids being too sparse or overwhelming.
The tool set provides comprehensive coverage for code analysis and data manipulation workflows, including analysis, discovery, execution, memory management, and transformation. A minor gap exists in the lack of a dedicated tool for direct code editing or version control integration, but agents can work around this using the execute and transform tools. Overall, the surface supports most common tasks without significant dead ends.