This server has extremely limited functionality with only a single tool that lacks essential production-grade characteristics. The tool has a name that violates verb-noun conventions, a description that is too generic, and critically, no visible input schema definition. The implementation is a bare Python class with no MCP protocol integration visible. No error handling, no output schema documentation, no parameter validation, and no security considerations are evident.
Tool name 'plan_steps' violates verb_noun naming convention. 'plan_steps' is vague, does it execute steps, create a plan, validate steps, or simulate them? Should be 'create_execution_plan', 'generate_task_steps', or 'simulate_multi_app_workflow'.
Description 'Plan execution steps across multiple applications' is 57 characters, below the 194-character production baseline (p10=34, p90=392 for A+ tools). Lacks context on WHEN to use, WHAT the tool returns, and any prerequisites. Does not state if this is a read-only planning tool or if it executes steps.
Parameter 'apps_list' has a generic description 'List of application names to coordinate across' (55 chars). No constraints on format, length, or valid values. LLM cannot determine if it should accept Gmail, gmail, email_app, or G001. Missing enum or validation guidance.
Recommendations
Add a proper MCP server implementation using an MCP SDK (e.g., python-mcp for Python). Currently, this is a standalone Python class with no MCP protocol integration.
Define a complete JSON Schema for the tool's input parameters. Include type definitions, constraints (min/max length for arrays, pattern for app names), and whether parameters are required or optional. Example: {"type": "object", "required": ["apps_list", "task_description"], "properties": {"apps_list": {"type": "array", "items": {"type": "string", "enum": ["Gmail", "Slack", "Teams", ...]}, "minItems": 2, "description": "List of apps to coordinate (e.g., Gmail, Slack, Teams)"}, ...}}
Rename 'plan_steps' to a more specific verb_noun pair. Suggestions: 'generate_multi_app_workflow', 'create_execution_plan', 'simulate_app_sequence', or 'design_task_workflow'. The name should clearly indicate the tool returns a plan, not executing it.
Expand the tool description to 100-200 characters, covering: (1) What the tool does: 'Generates a multi-step execution plan for a task across specified applications.' (2) When to use it: 'Use this when a user wants to coordinate actions between multiple apps (e.g., fetch data from Gmail, summarize, and post to Slack).' (3) What it returns: 'Returns an ordered list of execution steps, each targeting a specific application.'
Add enum constraints for 'apps_list'. Define valid application names (e.g., 'Gmail', 'Slack', 'Microsoft Teams', 'Google Drive') as an enum in the schema OR as a discovery tool that lists available apps.
Parameter 'task_description' description 'Description of the task to be executed' (41 chars) is circular, it defines the parameter as 'a description of a description.' Does not clarify format, length limits, or what constitutes a valid task. Missing actionable guidance.
No output schema documented. The implementation returns a hardcoded dict with 'execution_steps' array, but no JSON Schema is defined for the response. LLM cannot plan downstream calls or validate response structure.
No error handling visible. The tool returns hardcoded mock data regardless of input validity. No validation of apps_list (e.g., empty list, null values, invalid app names). No recovery guidance for LLM if the tool fails.
No MCP protocol integration detected. Source code shows a bare Python class with no MCP server implementation, no tool registration via MCP SDK, no transport (STDIO, HTTP, SSE), and no capability negotiation. This is not an MCP server.
Tool description says 'Plan execution steps' but implementation returns hardcoded strings like 'Read from Gmail', 'Post results to Slack', semantically meaningless. The tool appears non-functional for its stated purpose.
No idempotency guarantees. A tool named 'plan_steps' should be read-only and safe to call repeatedly. The implementation returns the same mock output, but this behavior is not documented or enforced. Marked as READ_ONLY in metadata but not reflected in tool description or output schema.
plan_steps
Improve 'task_description' documentation. Example: 'Description of the task to coordinate (50-500 characters, e.g., "Forward email summaries from Gmail to Slack every morning"). This guides the LLM to provide coherent task narratives instead of vague inputs.'
Define a complete output schema. Example: {"type": "object", "properties": {"execution_steps": {"type": "array", "items": {"type": "object", "properties": {"step_number": {"type": "integer"}, "target_app": {"type": "string"}, "action": {"type": "string"}, "description": {"type": "string"}}}, "description": "Ordered list of steps to execute the task"}}}
Implement actual planning logic instead of hardcoded mock output. The tool should analyze the apps_list and task_description to generate meaningful steps, not return 'Read from Gmail' and 'Post to Slack' regardless of input.
Add input validation and error handling. Validate that apps_list is non-empty, contains valid app names, and task_description is non-null. Return actionable errors like 'Invalid app name: "Gmial". Did you mean: Gmail, Google Drive, Google Calendar?'
Document idempotency in the tool description. Example: '(Read-only) This tool generates a plan but does not execute it. Calling it multiple times with the same input returns the same plan.'
Add parameter descriptions for 'apps_list' clarifying the expected format. Example: 'List of application names (e.g., ["Gmail", "Slack", "Teams"]). Each app must be supported by the system. Call list_available_apps() first to see all supported integrations.'
Implement a transport layer (STDIO or HTTP). Without a working MCP transport, this cannot be used by any MCP client. Choose STDIO for local agents or Streamable HTTP for hosted clients.
Add tool annotations if using MCP: mark 'plan_steps' as readOnlyHint=true (since it doesn't modify state) to signal to LLMs that it's safe to call repeatedly.