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mcp-research-assistant

mcp-research-assistant

by CyprianFusi
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

get_topic_info

Retrieve detailed information and insights about any research topic using semantic search from saved research content.

Instructions

Get detailed information about a research topic. Args: topic: Topic name to get info for

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": "get_topic_info"
  }
}

Input Schema

NameRequiredDescriptionDefault
topicYes

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.5

TDQS

B3.1/5.0
Behavior2/5

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

With no annotations provided, the description must fully disclose behavioral traits. It only says 'get detailed information' without specifying read-only nature, error behavior for missing topics, or any side effects. The brevity leaves significant behavioral ambiguity.

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 concise with no wasted words. It front-loads the purpose in the first sentence and then lists the argument. However, it lacks structural elements like headings or examples that could enhance scannability.

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?

While an output schema exists (relieving the need to explain return values), the description does not mention what 'detailed information' entails—e.g., metadata, relationships, or associated data. It is minimally adequate but leaves the agent guessing about the nature of the output.

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%, meaning the description must compensate. It merely restates the parameter as 'topic name to get info for,' adding minimal meaning beyond the schema's 'topic' string. No constraints, examples, or format details are given.

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 'Get detailed information about a research topic,' which specifies the verb ('get') and resource ('research topic'). It distinguishes from siblings like 'delete_research_topic' and 'list_research_topics' by focusing on retrieval of detailed info rather than deletion, listing, saving, or searching.

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 guidance is provided on when to use this tool compared to alternatives like 'search_research_data' or 'list_research_topics'. There is no mention of context, preconditions, or exclusions, leaving the agent without decision criteria.

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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