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Academic Proposal MCP Server

Academic Proposal MCP Server

by Ranoes
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
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generate_rubric_checklist_report

Generate an audit report of thesis proposal compliance with the rubric and Research Design Canvas checklist across Chapters 1-3, and save it as a Markdown file.

Instructions

Menghasilkan laporan audit kepatuhan proposal skripsi dalam format Markdown berdasarkan rubrik evaluasi dan checklist Research Design Canvas (Chapter 1, 2, dan 3). Dapat langsung disimpan ke workspace sebagai berkas Markdown (.md).

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "proposal_title": "<proposal_title>",
      "student_name": "<student_name>",
      "student_id": "<student_id>",
      "rumusan_masalah": "<rumusan_masalah>",
      "variabel_independen": "<variabel_independen>",
      "variabel_dependen": "<variabel_dependen>",
      "tujuan_penelitian": "<tujuan_penelitian>",
      "manfaat_penelitian": "<manfaat_penelitian>"
    },
    "name": "generate_rubric_checklist_report"
  }
}

Input Schema

NameRequiredDescriptionDefault
student_idYes
student_nameYes
proposal_titleYes
rumusan_masalahYes
save_to_workspaceNo
tujuan_penelitianYes
variabel_dependenYes
manfaat_penelitianYes
variabel_independenYes
output_markdown_filenameNoproposal_rubric_checklist_report.md

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

C2.8/5.0
Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions the ability to save to workspace, implying a write side-effect, but does not disclose other behaviors such as whether the operation is read-only, whether it requires prior steps, or what happens when save_to_workspace is false.

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 concise with only two sentences, front-loaded with the main purpose and mentioning the save capability. It contains no extraneous information and is structurally efficient.

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

Completeness2/5

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

Given the tool has 10 parameters (8 required) and no output schema, the description is incomplete. It does not describe how to provide the parameters, what constitutes valid input, or the structure of the generated report, leaving significant gaps for a correct invocation.

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

Parameters1/5

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

Schema has 0% description coverage for parameters. The description does not explain any of the 10 parameters or expected formats, failing to compensate for the lack of schema documentation. An agent would have no idea what 'rumusan_masalah' or 'manfaat_penelitian' should contain beyond the parameter title.

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 tool generates a compliance audit report in Markdown based on a rubric and Research Design Canvas for chapters 1-3. It distinguishes itself from siblings like export_proposal_as_markdown (which exports the proposal itself) and generate_academic_proposal (which generates the proposal content) by specifying the audit-report nature.

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

Usage Guidelines2/5

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

The description does not provide explicit guidance on when to use this tool over alternatives. It only implies its purpose, lacking any 'use this when' or 'instead of X' statements, so an agent receives no routing cues beyond the basic action.

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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