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Academic Proposal MCP Server

Academic Proposal MCP Server

by Ranoes
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

plan_proposal_research

Turns a research topic into a structured proposal plan: defines the research problem, independent and dependent variables, measurable objectives, and optimized literature search queries for paper-search tools.

Instructions

Menurunkan rumusan masalah tunggal, variabel independen (X), variabel dependen (Y), tujuan penelitian terukur, serta kueri pencarian literatur yang dioptimalkan khusus untuk dijalankan pada MCP paper-search (search_arxiv, search_semantic, search_google_scholar).

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": {
      "topic": "<topic>"
    },
    "name": "plan_proposal_research"
  }
}

Input Schema

NameRequiredDescriptionDefault
topicYes
variabel_xNo
variabel_yNo
bidang_kajianNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

C2.7/5.0
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 implies the tool outputs a plan and search queries, but it does not state whether the tool is read-only, whether it performs any searches itself, how outputs are returned, or whether any side effects occur. This leaves the agent guessing about external calls and mutation behavior.

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 entire description is one dense sentence, front-loaded with the main deliverable and free of filler. It loses a point because the long-final list of outputs reads more like a clause rather than an intuitive roadmap, but the content is concise and focused.

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?

With no output schema, no annotations, and a 0% schema-description coverage, the description leaves an agent without a clear expected output shape or workflow. It communicates the conceptual output but not enough operational detail for correct usage, such as optional parameter effects, formats, or handling when no variables are supplied.

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%, and the description never names the actual parameters topic, variabel_x, variabel_y, or bidang_kajian. Although it mentions variabel X/Y concepts, it doesn't map them to the schema. The meaning of topic and bidang_kajian must be inferred entirely from the input-schema titles.

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?

Description names a concrete output: a single problem statement, independent/dependent variables, measurable objective, and literature-search queries. It is clearly distinct from siblings that generate full proposal text, because it explicitly targets MCP paper-search queries. However, it stops short of naming sibling tools to contrast with, so a small clarity gap remains.

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?

No explicit when-to-use or when-not-to-use guidance is provided. The only usage signal is that the produced queries are intended for paper-search MCP tools, but the description does not explain when this planning tool should be chosen over generate_proposal_from_topic or generate_academic_proposal. It also doesn't warn against using siblings for planning.

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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