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axionquant

AxionQuant MCP Server

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by axionquant

econ_find

Find economic datasets by describing what you need in plain English. AI matches your query to the correct data from live market sources.

Instructions

Find economic datasets using natural language description (AI-powered)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYesNatural language description of the data you need
Behavior2/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It only states 'AI-powered' which hints at interpretation but does not explain what the tool returns, its limitations, or how it differs from a standard search. This is a significant gap for a discovery tool.

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 a single sentence, front-loaded with the key action and resource. It is concise and without fluff, efficiently conveying the core purpose. However, it is so brief that it misses opportunities to add useful context.

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

Completeness2/5

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

For a tool with one parameter and no output schema, the description is minimal. It does not explain the nature of the returned datasets, the scope of coverage, or how it relates to econ_search and econ_dataset. Given the abundance of sibling tools, more context is needed to ensure correct selection and usage.

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 schema already describes the only parameter 'query' as 'Natural language description of the data you need' with 100% coverage. The tool description adds no extra meaning or syntax beyond that, so a baseline score of 3 is appropriate—it neither detracts nor enhances.

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 description clearly states the verb 'Find' and the resource 'economic datasets', and adds that it uses natural language with AI. It is specific enough to understand the core function, but does not explicitly distinguish it from sibling tools like econ_search or econ_dataset, which are likely related.

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 versus alternatives. The description does not mention any conditions, exclusions, or relationships to econ_search or econ_dataset, so an agent has no way to know when this tool is the right choice among the econ-* siblings.

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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