Skip to main content
Glama
lj22503

InvestBrain

rag_get_master_view

Retrieve a specific investment master’s view on a given topic, including citations, to support informed investment research and decision-making.

Instructions

Get a specific master's view on a topic.

Args:
    master: Master name (e.g., "巴菲特", "芒格", "段永平")
    topic: Topic to query (e.g., "护城河", "安全边际")

Returns:
    dict: Master's view on the topic with citations

Input Schema

NameRequiredDescriptionDefault
topicYes
masterYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.5/5.0
Behavior3/5

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

No annotations are provided, so the description carries the full burden. It does disclose the return shape ('dict: Master's view on the topic with citations'), which is genuinely useful for a retrieval tool, but it says nothing about behavior when the master or topic is unknown, whether the view is cached/generated, or any access constraints.

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 one-line purpose is front-loaded, followed by compact Args/Returns sections with no filler. Slightly over-formatted for only two parameters, but nothing is wasted.

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?

With no output schema and no annotations, the description is the only source of truth, and it covers inputs plus the rough return shape. It leaves open the routing question against sibling RAG tools and any failure behavior, which matters for a tool whose only inputs are free-text names.

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?

Schema description coverage is 0% – the schema lists only bare string types for 'master' and 'topic'. The description compensates by documenting both parameters and supplying concrete example values in the target language, which is well beyond what the schema offers. It stops short of enumerating the valid master set, so an agent could still guess an unsupported name.

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 first line states a specific verb and resource ('Get a specific master's view on a topic'), which is unambiguous on its own. It does not, however, distinguish itself from near-neighbors like rag_ask_investment, rag_search_knowledge, or rag_rag_search_reports, so an agent cannot tell from the description alone which RAG tool to pick.

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?

Usage is only implied through the argument examples (masters 巴菲特/芒格/段永平, topics 护城河/安全边际), which signal a value-investing wisdom lookup. There is no explicit statement of when to use this versus the sibling RAG tools, and no exclusions or prerequisites are given.

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

Deploy Server

Other Tools