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totonga

odsbox-jaquel-mcp

by totonga

query_execute

Read-only

Execute a Jaquel query directly on the connected ODS server.

Instructions

Execute a Jaquel query directly on connected ODS server.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYesJaquel query dict to execute. Top-level key is entity name, value is filter/attribute object. Example: {"AoTest": {"name": {"$like": "*"}}, "$attributes": {"id": 1, "name": 1}, "$options": {"$rowlimit": 100}}
max_rowsNoMaximum number of rows to return (default: 100). Also capped adaptively by max_cells to protect LLM context size. Use a small value like 10-20 for wide results (many columns).
max_cellsNoAdaptive cell budget: effective_rows = min(max_rows, max_cells // col_count). Default 10 000 ≈ 6 000 LLM tokens for double data. Increase only if you need more data and understand the context cost.
result_formatNoResult serialisation format: "split" (default) encodes column names once — {"columns": [...], "index": [...], "data": [...]}; "records" repeats all keys per row — [{"col": val, ...}, ...]. "split" is more token-efficient for wide results.split

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

B3.4/5.0
Behavior3/5

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

The readOnlyHint annotation already conveys the safety profile, and the description is consistent with it, so there is no contradiction. The word 'directly' suggests immediate execution without validation or transformation, and the requirement of a connected ODS server is hinted, but the description does not disclose failure modes, validation behavior, or context-size safeguards beyond what the schema explains.

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 a single, front-loaded sentence with no filler or redundant wording. Every word contributes to identifying the operation, making it highly concise while remaining more informative than a tautology.

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?

The input schema is unusually rich, the read-only annotation is present, and an output schema exists, so the description does not need to explain return values or parameter details. The main gap is the lack of routing guidance relative to query_validate and other siblings, but for an execution tool with this much structured context, the definition is close to complete.

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 the schema already provides detailed semantics: a full Jaquel query example, defaults for max_rows, the adaptive max_cells formula, and format/token guidance for result_format. The description itself 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?

The description states a specific action ('Execute'), a specific resource ('a Jaquel query'), and a target ('connected ODS server'), which makes the tool's core purpose clear. It does not explicitly contrast it with sibling tools like query_validate, query_describe, or data_read_submatrix, but the verb 'execute' plus 'directly' gives the agent a reasonable basis for differentiation.

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?

There is no guidance on when to use this tool versus query_validate, query_generate_skeleton, query_describe, or data_read_submatrix. It does not mention whether the query should be validated first, whether an active ODS connection is required beyond the phrase 'connected', or when an alternative would be preferable.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.