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open-data-mcp

open-data-mcp

by markatdataandbeyond
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

dataandbeyond--dk-statistics

Read-only

Search Statistics Denmark Statbank tables by keyword, inspect their variables, and fetch official rows as JSON for analysis or reporting.

Instructions

Search Statistics Denmark (Statbank) tables by keyword, see their variables, and fetch rows as JSON. Official data, CC BY 4.0. Calls api.statbank.dk directly.

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": "dataandbeyond--dk-statistics"
  }
}

Input Schema

NameRequiredDescriptionDefault
modeNosearch = find tables by keywords; data = fetch rows from one table. Example values: "search"search
searchNoAll words must match title/variables. Example values: "population municipality"population municipality
filtersNoVariable code -> list of value codes, '*' for all, '(latest)' for latest period. Omitted variables use totals/latest. Example values: {"OMRÅDE":["*"]}
maxRowsNoMax rows. Example values: 10000
tableIdNoe.g. FOLK1A, PRIS111, BY1
languageNoLanguage. If not set: English, and Danish search words are also matched against Danish titles.
waitSecsNoMax seconds (0–45, default 30) to cap the wait for the Actor run to reach terminal state. For long-running Actors the response returns at the cap with the current run status; follow `nextStep` to poll via get-actor-run. Set to 0 to fire-and-forget.
maxTablesNoMax tables (search mode). Example values: 10
lastNPeriodsNoOverrides time filter with the N most recent periods.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.1.0

TDQS

A3.8/5.0
Behavior4/5

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

Annotations already declare readOnly, non-destructive and openWorld. The description adds real context beyond them: data is official, CC BY 4.0 licensed, and the underlying calls hit api.statbank.dk directly, which hints at an external dependency. It still doesn't describe return structure or rate-limit behavior in the description body.

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?

Three short, front-loaded sentences with no filler; the core capability comes first and provenance/licensing trails. Slightly fragmentary phrasing ('Calls api.statbank.dk directly.') keeps it from a 5.

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

Completeness3/5

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

With 9 parameters, a nested filters object, and no output schema, the description should explain what a 'data' response looks like (rows plus variable metadata) and how modes sequence into each other. It names the modes but leaves the return shape and multi-step flow largely implicit.

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 nested objects are documented, so the schema carries parameter meaning. The description adds nothing parameter-specific (no explanation of filters syntax, lastNPeriods, or mode interplay beyond what the schema states), so the baseline of 3 applies.

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?

States specific verbs (search, see variables, fetch rows) against a specific resource (Statistics Denmark / Statbank tables), and names the output format (JSON). No sibling tool touches Danish statistics, so an agent can route to it unambiguously.

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 a two-step workflow (search first, then data) but never says when to pick 'search' mode versus 'data' mode, nor gives prerequisites. Usage is inferable from the mode enum in the schema rather than from the prose.

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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