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FlowLLM-AI

Finance MCP

by FlowLLM-AI

react_agent

Process financial queries using AI to deliver research and analysis, supporting stock/fund entity extraction and structured investigations.

Instructions

A React agent that answers user queries.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYesquery
Behavior1/5

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. The description only states it 'answers user queries' without explaining how it works, what kind of answers it provides, whether it has limitations, rate limits, authentication needs, or what happens when invoked. This is completely inadequate for a tool with no annotation coverage.

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

Conciseness3/5

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

The description is extremely concise - a single sentence. However, this conciseness comes at the cost of being under-specified rather than efficient. While it's front-loaded with the core function, the sentence doesn't earn its place by providing necessary context or differentiation from sibling tools.

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

Completeness1/5

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

Given the tool has no annotations, no output schema, and competes with multiple query-related sibling tools, the description is completely inadequate. It doesn't explain what makes this tool unique, how it behaves, what it returns, or when to use it. For a tool in this complex context, the description fails to provide essential information.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has 100% description coverage with one parameter 'query' described as 'query'. The description doesn't add any meaningful information about this parameter beyond what the schema already provides. Since schema coverage is high, the baseline score of 3 is appropriate - the description neither compensates nor adds value beyond the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose2/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description 'A React agent that answers user queries' is tautological - it essentially restates the tool name 'react_agent' with minimal elaboration. While it mentions answering queries, it doesn't specify what type of queries, what domain it operates in, or how it differs from other query-answering tools like dashscope_search or tavily_search among the siblings. The purpose is vague rather than specific.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines1/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives. With sibling tools like dashscope_search and tavily_search that also handle queries, there's no indication of what makes this React agent unique or appropriate for certain contexts. No explicit or implied usage scenarios are mentioned.

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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