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Academic Proposal MCP Server

Academic Proposal MCP Server

by Ranoes
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
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validate_canvas_compliance

Checks academic proposal sections against Research Design Model Canvas v2.0, ensuring a single measurement-oriented problem, explicit variables, linear objectives, and specific stakeholder benefits.

Instructions

Memvalidasi kepatuhan naskah proposal terhadap panduan Research Design Model Canvas v2.0. Dapat digunakan untuk segala topik skripsi/tesis. Memeriksa:

  1. Rumusan masalah tunggal dan berorientasi pengukuran (non-deskriptif).

  2. Variabel independen (X) dan dependen (Y) yang terdefinisi eksplisit.

  3. Tujuan penelitian yang linier dengan capaian variabel.

  4. Manfaat penelitian spesifik bagi stakeholders (bebas klausul klise).

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": {
      "rumusan_masalah": "<rumusan_masalah>",
      "variabel_independen": "<variabel_independen>",
      "variabel_dependen": "<variabel_dependen>",
      "tujuan_penelitian": "<tujuan_penelitian>",
      "manfaat_penelitian": "<manfaat_penelitian>"
    },
    "name": "validate_canvas_compliance"
  }
}

Input Schema

NameRequiredDescriptionDefault
rumusan_masalahYes
tujuan_penelitianYes
variabel_dependenYes
manfaat_penelitianYes
single_problem_onlyNo
variabel_independenYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

A3.8/5.0
Behavior3/5

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

No annotations are provided, so the description carries the burden. It discloses the validation criteria in detail (four checks), which is useful behavioral context. However, it does not state what the output looks like, whether it returns a pass/fail, a list of violations, or a score, nor does it mention any side effects (it appears read-only). The description adds value by listing the checks but lacks output/behavioral details.

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 well-structured with a clear opening sentence and a numbered list of checks. It is concise and front-loaded with the main purpose. The numbered list is easy to parse. It could be slightly more compact, but the structure is effective.

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

Completeness3/5

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

Given the tool has 6 parameters, no output schema, and no annotations, the description is moderately complete. It explains the validation criteria and the inputs, but it does not describe the return value/format, which is important for an agent to know what to do with the result. It also doesn't clarify the 'single_problem_only' parameter. For a validation tool, the output format is a significant missing piece.

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

Parameters3/5

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

Schema description coverage is 0%, so the description must compensate. The description mentions the four main inputs (rumusan masalah, variabel independen/dependen, tujuan penelitian, manfaat penelitian) in its checklist, which maps to the required parameters. However, it does not explain the 'single_problem_only' boolean parameter, which is in the schema but not described. The description adds some meaning but leaves a gap for one parameter.

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's purpose: validating a proposal manuscript against the Research Design Model Canvas v2.0 guidelines. It specifies the resource (naskah proposal) and the action (memvalidasi kepatuhan), and lists the four specific compliance checks. It distinguishes itself from siblings like generate_rubric_checklist_report or get_canvas_guidelines by focusing on validation of a manuscript against the canvas.

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 states it can be used for any thesis/dissertation topic, which gives a clear context. It does not explicitly name alternatives or when-not-to-use, but the sibling list shows related tools like get_canvas_guidelines (for retrieving guidelines) and generate_rubric_checklist_report (for generating a report), which are distinct. The usage context is clear enough, though explicit exclusions are missing.

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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