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research-assistant-mcp

research-assistant-mcp

by laxmimerit
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
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save_research_data

Save research content to a vector database for future retrieval. Use topics to organize data into separate collections for easier management.

Instructions

Save research content to vector database for future retrieval. Args: content: List of text content to save topic: Topic name for organizing the data (creates separate DB)

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": {
      "content": "<content>"
    },
    "name": "save_research_data"
  }
}

Input Schema

NameRequiredDescriptionDefault
topicNodefault
contentYes

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.1

TDQS

A3.9/5.0
Behavior3/5

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

With no annotations available, the description carries the full burden of behavioral disclosure. It mentions that a separate DB is created for each topic, which adds useful context, but it does not explain what happens when a topic already exists (append vs. overwrite), whether there are any side effects, or if specific permissions are required. These are significant gaps for a mutation tool.

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 extremely concise, two sentences plus an args list, with the core purpose front-loaded. Every part contributes meaning; there is no fluff or repetition of schema details. The structure is clean and scannable.

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's simplicity (2 params, no nested objects) and the presence of an output schema, the description covers the basics well. However, it lacks a critical behavioral detail: what happens when the same topic is used again. This ambiguity could lead an agent to misuse the tool, so the description is not fully complete for this write operation.

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 schema's parameter descriptions are absent (0% coverage), so the description's explicit explanation of both parameters is essential. It clarifies that 'content' is a list of texts to save and that 'topic' is used for organization and creates a separate DB, adding meaning beyond the raw schema types and defaults.

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 action ('Save') and the resource ('research content to vector database') with a clear purpose ('for future retrieval'). It distinguishes itself from siblings like search_research_data (retrieval) and delete_research_topic (deletion) by focusing solely on the write operation.

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 usage context ('for future retrieval') but does not explicitly state when to use this tool over alternatives, nor does it mention any exclusions. The sibling tools are known from context, but the description itself offers no direct comparison, so the guidance is implicit rather than explicit.

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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