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bjunjo

treasury-mcp

by bjunjo

get_treasury_companies

Retrieve a comprehensive list of publicly traded companies holding Bitcoin in their treasuries, including their ticker symbols, exchange information, country, approximate BTC holdings, and last update dates.

Instructions

List all Bitcoin treasury companies tracked by Treasury-MCP.

Returns a table of all tracked companies with their ticker, exchange, country, approximate BTC holdings, and last data update date.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior3/5

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 clearly indicates a read-only operation ('List', 'Returns a table') and specifies the data structure returned, which helps the agent understand output format. However, it doesn't mention potential limitations like rate limits, data freshness, or error conditions, leaving gaps in behavioral context.

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 efficiently structured in two sentences: the first states the action and scope, and the second details the return format. Every sentence adds value without redundancy, and it's front-loaded with the core purpose. No wasted words or unnecessary elaboration.

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

Completeness4/5

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

Given the tool's simplicity (0 parameters, no annotations, but has an output schema), the description is reasonably complete. It clearly explains what the tool does and the structure of returned data, which aligns with the output schema's role. However, it could be more comprehensive by addressing behavioral aspects like data sources or update frequency, slightly reducing completeness for a tool with no annotations.

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 tool has 0 parameters, and schema description coverage is 100% (as there are no parameters to describe). The description appropriately doesn't add parameter details, maintaining focus on the tool's purpose and output. A baseline score of 4 is applied since no parameters exist, and the description doesn't attempt to compensate for non-existent schema gaps.

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 specific action ('List all Bitcoin treasury companies') and resource ('tracked by Treasury-MCP'), with explicit mention of the returned data fields (ticker, exchange, country, BTC holdings, update date). It distinguishes itself from sibling tools like 'get_treasury_holdings' by focusing on company metadata rather than holdings details.

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 by specifying it returns 'all tracked companies' with comprehensive fields, suggesting it's for obtaining a complete overview. However, it lacks explicit guidance on when to use this versus alternatives like 'get_treasury_holdings' or other data-fetching tools, leaving the agent to infer based on the data scope.

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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