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research-assistant-mcp

research-assistant-mcp

by laxmimerit
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

list_research_topics

List all research topics stored as vector databases, helping you identify available subjects for retrieval and management.

Instructions

List all available research topics (vector databases).

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "list_research_topics"
  }
}

Input Schema

NameRequiredDescriptionDefault

No arguments

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.1

TDQS

A4.3/5.0
Behavior4/5

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

No annotations are provided, so the description carries the full transparency burden. It explicitly states it lists 'all available' topics, implying a non-destructive read-only operation. The parenthetical 'vector databases' adds useful context. While it does not mention pagination or side effects, the presence of an output schema covers return values, and the simplicity of a list operation make this sufficient.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single sentence with no redundant words. It is perfectly concise and front-loaded.

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

Completeness5/5

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

With 0 parameters and an output schema present, the description fully conveys the tool's function. It tells the agent exactly what will happen when invoked, leaving no ambiguity for a tool of this simplicity.

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?

The tool has 0 parameters, so the baseline score is 4. There are no additional parameter details needed.

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 uses a specific verb ('List') with a clear resource ('research topics') and adds clarifying context ('vector databases'). It clearly distinguishes itself from siblings: 'save', 'search', 'delete', and 'get_topic_info', with 'all available' disambiguating from get_topic_info which likely retrieves a single topic.

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?

There is no explicit guidance about when to use this tool versus alternatives. However, the verb 'list' and the sibling context (save, search, delete, get_topic_info) imply that this tool is for enumerating all topics before other operations, but this is only implied, not stated.

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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