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erddap-mcp

erddap-mcp

by mrfentmen
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

dataset_info

Retrieve metadata for a specific ERDDAP dataset, including title, institution, summary, time and space coverage, and queryable variables to inspect before running data queries.

Instructions

Metadata for one dataset: title, institution, summary, time/space coverage, and the full variable list you can query.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "dataset_info"
  }
}

Input Schema

NameRequiredDescriptionDefault
serverNoServer nickname or URL.
dataset_idNoDataset ID from search_datasets.

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 exist, so the description carries the load. The enumeration of returned metadata makes the read-only, non-mutating nature evident, but it says nothing about permissions, whether unavailable datasets error or return partial metadata, or any cost/latency behavior. The return-content disclosure is the main value added.

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?

A single, front-loaded sentence that names the resource first and then the payload. Nothing is wasted; it could only be improved by adding a routing clause at similar length.

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?

There is no output schema, so enumerating the returned fields in the description is exactly the right compensation, and it is done well. A brief note connecting the variable list to the query_tabledap/query_griddap siblings would make it fully complete for this dataset-inspection step.

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?

Schema description coverage is 100% and both parameters (server, dataset_id) are documented in the schema itself, so the baseline is 3. The description adds no extra detail about how server vs dataset_id interact or what a dataset_id format looks like.

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?

States a specific resource (one dataset) and enumerates exactly what it returns: title, institution, summary, time/space coverage, and variable list. It never explicitly contrasts itself with search_datasets, but 'one dataset' plus the full-returns list makes the scope clear enough to pick it over the list/search siblings.

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: the schema notes dataset_id comes from search_datasets, and 'the full variable list you can query' hints at a follow-up step. There is no explicit when-to-use statement, no mention of when not to use it, and no named alternative such as query_tabledap.

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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