Skip to main content
Glama

git-asmt-repo

Builds and tests GitHub coding-assignment repos via the org-hub autograder, auditing instruction fidelity, proving 100/100, and shipping starter code with gates.

Instructions

GLOBAL, language-agnostic L1 abstract for building + testing a GitHub coding-assignment repo through the org-hub autograder. Owns the WHAT (audit → instruction-fidelity harness → prove 100/100 → ship the Starter → gates) with NO language mechanics. The HOW-to-compile/run/assert is delegated to an L2 language skill (git-asmt-repo/languages/*), and the course coordinates come from an L3 course wrapper (config). Use as the shared core every course's git build/test wrapper points to. NEVER re-implement this per course.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior3/5

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

With no annotations, the description carries the burden of behavioral disclosure. It explains that the tool 'Owns the WHAT' and delegates HOW to L2 and course coordinates to L3, adding architectural context. However, it does not describe concrete invocation behavior, side effects, or return format beyond the existence of an output schema. This is a moderate disclosure.

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 dense but each sentence adds value: it defines the scope, the WHAT pipeline, delegation to L2/L3, and usage instructions. It is front-loaded with 'GLOBAL, language-agnostic L1 abstract.' No wasted words, though the chain 'audit → instruction-fidelity harness → prove 100/100 → ship the Starter → gates' is compact and may require interpretation.

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

Completeness4/5

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

Given the tool's abstract nature, no parameters, and presence of an output schema, the description sufficiently situates it within the broader system (L1 vs L2 vs L3, course wrappers). It does not explain what a caller should expect on invocation, but the output schema covers that. The description is complete enough for an agent to understand its place and usage.

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

Parameters4/5

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

There are zero parameters and the schema coverage is 100% (vacuously), so the baseline is 4. The description does not need to add parameter details; it correctly focuses on the tool's role. No additional parameter semantics are required.

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

Purpose4/5

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

The description clearly identifies the tool as a global, language-agnostic abstract for building and testing GitHub coding-assignment repos, distinguishing it from siblings by making explicit its L1 role and delegation to L2/L3. It lacks a concrete verb+resource phrasing but communicates scope and intent effectively.

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

Usage Guidelines5/5

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

The description provides explicit usage guidance: 'Use as the shared core every course's git build/test wrapper points to' and 'NEVER re-implement this per course.' It also names the alternatives (L2 language skills, L3 course wrapper), making it clear when to use this tool versus others.

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

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/Canvas-LMS-MCP/canvas-teacher-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server