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Query cataloged read-only data

query_read_only_data

Execute parameterized read-only SQL queries against cataloged fitness-data views to retrieve paginated results for a chosen athlete, with safeguards that reject mutations, filesystem access, and system tables.

Instructions

Advanced fallback after describe_data/describe_dataset: one parameterized SELECT or WITH … SELECT over cataloged views only. Do not query information_schema, DuckDB system tables, or uncataloged physical relations. Results use deterministic cursor pagination and always report whether they are incomplete. Filesystem access, extensions, DDL, and mutation are rejected. Requires athleteId from list_athletes.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
sqlYes
cursorNo
valuesNo
pageSizeNo
athleteIdYes
Behavior5/5

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

With no annotations, the description carries the full burden, and it delivers: read-only scope, prohibited targets (information_schema, DuckDB system tables, uncataloged relations), rejected operations (filesystem access, extensions, DDL, mutation), and cursor pagination semantics. This is unusually thorough for an unannotated 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?

Dense and front-loaded: the core purpose appears in the first clause, followed by compact constraints and behavior. No filler or redundant restatement of the tool name.

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

Completeness5/5

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

For a high-complexity SQL tool with no output schema, the description covers scope, safety boundaries, pagination, incompleteness reporting, and required input provenance. An agent has enough context to decide whether this is the right tool and to call it safely.

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?

Schema description coverage is 0%, so the description must compensate. It adds meaning for key parameters: 'sql' must be a SELECT/WITH over cataloged views, 'values' is implied by 'parameterized', 'cursor' is tied to deterministic pagination, and 'athleteId' is sourced from list_athletes. However, 'pageSize' is not explicitly addressed in the description.

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 a specific resource ('cataloged views only') and the exact form of action ('one parameterized SELECT or WITH … SELECT'). It also positions itself as an 'Advanced fallback after describe_data/describe_dataset', distinguishing it from those siblings and making its role clear.

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?

Gives explicit sequencing ('fallback after describe_data/describe_dataset') and a prerequisite ('Requires athleteId from list_athletes'). It does not name other sibling alternatives or explicitly state when not to use this tool, but the fallback context provides reasonable guidance.

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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