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fastmcp-me

OpenDataMCP

by fastmcp-me
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Python
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railway-lines

Query railway line information using filters, grouping, and sorting to find specific routes or station details from public datasets.

Instructions

Fetch railway line information

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMaximum number of railway line entries to return (1-100)
whereNoFilter conditions. Examples: 'linie = 100', 'bpk_anfang LIKE "*Zürich*"'
offsetNoNumber of railway line entries to skip for pagination
selectNoFields to select in the response. Examples: 'linie,linienname' for basic info, 'bpk_anfang,bpk_ende' for station info
group_byNoGroup railway lines by specific fields. Example: 'bpk_anfang' to group by starting station
order_byNoSort railway lines. Example: 'linie ASC' for line number order, 'km_ende DESC' for longest routes first

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

C2.6/5.0
Behavior1/5

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

With no annotations provided, the description carries the full behavioral disclosure burden, but it only restates the fetch purpose. It does not mention response format, pagination behavior, filtering semantics, rate limits, or what data is actually returned.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The single sentence is free of filler and front-loaded with the core action. However, it is under-specified rather than efficiently informative, leaving the agent with only a minimal statement of purpose.

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 six-parameter query tool with no output schema and no annotations, this is not a complete definition. It omits default return fields, how filtering and pagination behave, and how this tool fits with the sibling rail-related tools.

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%, so all six parameters (limit, where, offset, select, group_by, order_by) are already documented in the input schema. The description adds no parameter-level meaning, so the baseline score of 3 applies.

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?

'Fetch railway line information' names a clear verb and resource, so an agent knows this is a read operation on railway line data. It does not go beyond that to distinguish itself from siblings like rail-traffic-info or rolling-stock, so it falls short of a 5.

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?

The description gives no guidance about when to use this tool instead of rail-traffic-info or rolling-stock, and it states no exclusions or alternative conditions. An agent must infer usage entirely from the schema and tool name.

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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