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mcp-server-demo

mcp-server-demo

by charley-forey
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

upload_to_github

Create a new GitHub repository and upload the generated MCP server directory to complete the workflow.

Instructions

Step 6 of 6. Create a new GitHub repo and upload the generated MCP server directory.

Requires step 5 (generate_mcp_server). Uses stored output_dir if source_dir not provided. repo_name: optional repo name; default unique name (base + timestamp) to avoid repeats. visibility: "public" or "private". Default: "public". source_dir: optional path to generated directory; default: get_stored("output_dir").

Returns: repo_url, message, next_step_guidance. Completes workflow step 6.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "upload_to_github"
  }
}

Input Schema

NameRequiredDescriptionDefault
repo_nameNo
source_dirNo
visibilityNopublic

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.3/5.0
Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden. It discloses that it creates a repo and uploads a directory, and outlines default parameter behaviors. However, it does not mention potential behavioral traits like whether it overwrites existing repos, authentication requirements, or error handling.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is clear and each sentence adds value. It is slightly verbose with line breaks, but still efficient. The front-loading with 'Step 6 of 6' provides immediate context.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given no output schema, the description lists return values (repo_url, message, next_step_guidance). It also notes completion of workflow step 6. Parameter descriptions are adequate, and the workflow context is well-explained.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 0%, so the description must compensate. It effectively explains each parameter: repo_name default avoids repeats, visibility defaults to public, source_dir defaults to stored output_dir. This adds significant meaning beyond the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the action (create repo and upload directory) and the resource (GitHub repo and MCP server directory). It distinguishes itself from sibling tools like generate_mcp_server (step 5) by labeling itself as step 6 of 6.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly states a prerequisite (step 5: generate_mcp_server) and explains default behaviors for parameters. However, it does not provide guidance on when not to use this tool or mention alternatives among siblings.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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