data-bs-mcp

by DCC-BS

Overview Schema Source Related Servers Discussions Admin

Python

Remote

Query Dataset Records

get_records

Retrieve and filter dataset records using ODSQL queries. Apply conditions, sorting, and pagination to get exact data.

Instructions

Query and filter records from a dataset using ODSQL syntax. Use this to retrieve actual data from a dataset with optional WHERE clauses, ordering, and pagination.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "dataset_id": "<dataset_id>"
    },
    "name": "get_records"
  }
}

Input Schema

Name	Required	Description	Default
`lang`	No
`limit`	No
`where`	No
`offset`	No
`refine`	No
`select`	No
`exclude`	No
`group_by`	No
`order_by`	No
`timezone`	No
`dataset_id`	Yes
`include_links`	No

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Aug 10, 2026

TDQS

A3.7/5.0

Behavior3/5

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 reveals important behavioral traits: ODSQL syntax, optional filters, ordering, and pagination. However, it does not explain return format, default pagination behavior, or error handling, leaving gaps for a query tool.

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 two sentences, front-loaded with the core purpose. Every word adds value, and the structure is efficient for a tool description.

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?

Given 12 parameters, no annotations, and no output schema, the description is notably incomplete. It gives only a surface-level overview and does not clarify the ODSQL syntax or the semantics of the many optional parameters. This is insufficient for an agent to use the tool correctly without additional context.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must compensate. It mentions high-level concepts like WHERE, ordering, and pagination, but fails to map them to actual parameters (where, order_by, limit, offset). Many parameters (select, refine, group_by, timezone, include_links) remain completely unexplained, making proper invocation difficult.

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's purpose: 'Query and filter records from a dataset using ODSQL syntax.' It specifies the verb (query/filter), resource (records from a dataset), and distinguishes itself from siblings by emphasizing 'actual data' versus metadata or export functions.

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

Usage Guidelines4/5

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

The description provides clear context: 'Use this to retrieve actual data from a dataset with optional WHERE clauses, ordering, and pagination.' This tells the agent when to use the tool, though it does not explicitly mention exclusions or alternative tools. It lacks a direct comparison to siblings like get_dataset or get_facets.

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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