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bharathvardhan

Climate MCP Server

list_datasets

Lists registered climate finance datasets with metadata including URI, primary key, grain, and description. Use to discover available datasets or retrieve registry metadata.

Instructions

    List registered datasets (URI, primary key, grain, description).

    Prefer analysis tools (portfolio_summary, rank_by_column, etc.) when
    the client already documents fund.csv / pledges.csv / projects.csv —
    do not call this as a first step only to rediscover known files, as
    responses can be large.

    Use when the dataset list is unknown or you need registry metadata.
    

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior4/5

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 warns that responses can be large, which is a key behavioral trait. However, it does not disclose other potential behaviors like permissions or pagination, though for a simple list tool this may be sufficient.

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?

Three sentences, front-loaded with purpose, each sentence adds distinct value: purpose, usage guidance, and when-to-use. No wasted words.

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 zero parameters and presence of an output schema, the description covers purpose and usage well. It could mention pagination or size implications beyond 'large', but is fairly complete for the tool's complexity.

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?

There are no parameters (schema coverage 100%), so the baseline is 4. The description adds context about the output fields, which provides value beyond the empty schema.

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 tool lists registered datasets and specifies the returned fields (URI, primary key, grain, description). It also distinguishes itself from sibling analysis tools by advising when to use them instead.

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

Usage Guidelines5/5

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

Explicitly provides when to use (unknown dataset list or need registry metadata) and when not to use (if client already documents known files, prefer analysis tools). Mentions alternatives and cautions against unnecessary calls.

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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