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Ranoes

Academic Proposal MCP Server

by Ranoes

generate_proposal_from_topic

Turn a research topic into a complete thesis proposal document (.docx) with chapters, literature review tables, references, and methodology, saved directly to your workspace.

Instructions

Menghasilkan dokumen proposal skripsi (.docx) lengkap hanya dengan memberikan topik penelitian, data CSV literatur di workspace, dan/atau data paper yang diperoleh dari MCP paper-search. Secara otomatis menyusun Bab 1, Bab 2 (beserta tabel tinjauan pustaka), Bab 3, dan daftar referensi, lalu menyimpannya langsung ke workspace.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
topicYes
variabel_xNo
variabel_yNo
csv_contentNo
csv_filenameNo
output_filenameNoProposal Skripsi v1.0.docx
retrieved_papersNo
student_metadataNo
latar_belakang_notesNo
metode_penelitian_notesNo
custom_template_filenameNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

B3.1/5.0
Behavior3/5

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

There are no annotations, so the description must carry the behavioral burden. It does disclose the main side effects: it generates a .docx, composes chapters, and saves the document to the workspace. However, it does not mention overwrite behavior, confirmation/return value, or prerequisites/authorization, so transparency is only partial.

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 compact and front-loaded: the first sentence states the core purpose and inputs, and the second adds the resulting chapters and file destination. It is slightly dense with slash-separated input alternatives but stays within two purposeful sentences.

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 11 parameters, zero schema descriptions, no annotations, and no output schema, the description is not enough for an agent to call the tool correctly for all intended uses. It covers the headline purpose but leaves most optional parameters, file naming, and completion behavior to inference.

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

Parameters2/5

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

Schema description coverage is 0%, so the description needed to compensate for all 11 parameters. It only clarifies topic, CSV literature data, and paper-search data; variabel_x, variabel_y, output_filename, student_metadata, latar_belakang_notes, metode_penelitian_notes, and custom_template_filename remain unexplained by both schema and description.

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 opening phrase gives a specific action and artifact: generate a complete thesis proposal .docx from a topic. It names the content (Bab 1-3, references) and key inputs, which strongly implies this is the topic-based proposal generator, though it never explicitly contrasts it with siblings like generate_praproposal_from_topic or generate_academic_proposal.

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

Usage Guidelines3/5

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

The description implies the tool is appropriate when the user has a research topic and optionally CSV literature or paper-search results, but it does not state when to prefer this tool over the sibling generators or when not to use it. No explicit exclusions or alternative routing is provided.

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