Mostly Right

Query a connected table

query_table

Read-onlyIdempotent

A bounded, structured query over one table's current version. NO SQL: send columns, filters, order_by, aggregates and limit as JSON. Example: {"table_id": "0f2f...", "columns": ["observed_at", "air_temp_f"], "filters": [{"column": "air_temp_f", "operator": "gte", "value": 80}], "order_by": [{"column": "observed_at", "direction": "desc"}], "limit": 50}. Ceilings: 20 columns, 8 filters, 2 sort keys, 4 aggregates, 10000 rows a page (25 when limit is omitted), 8 MiB of JSON. Every page answers with next_cursor; send it back as cursor (same columns, filters and order_by) for the next page until it is null, and you have read the whole table on one immutable version. Operators: eq, neq, in, gt, gte, lt, lte, is_null, is_not_null. Requires an mr_use_ workspace key (Authorization: Bearer) or an OAuth connection. The first query over a dataset connects it to the workspace; connect_dataset makes that explicit but is not required. Returns rows plus table.version_id and table.content_digest; cite those. For the whole table in one request, download the Parquet instead.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`limit`	No
`cursor`	No
`offset`	No
`columns`	Yes
`filters`	No
`order_by`	No
`table_id`	Yes
`aggregates`	No

Schema Changelog

Changes observed during successful MCP inspections.

Changed3 schema fields changedSep 11, 2026

addedInput schema / properties / cursor

Added value: +{
+  "maxLength": 512,
+  "minLength": 1,
+  "type": "string"
+}

changedInput schema / properties / limit / maximum
```
Previous value: -100New value: +10000
```

addedInput schema / properties / offset

Added value: +{
+  "maximum": 100000000,
+  "minimum": 0,
+  "type": "integer"
+}

First observedSep 10, 2026

TDQS

A4.8/5.0

Behavior5/5

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

Annotations only say readOnly/idempotent/non-destructive; the description adds the real behavioral payload: auth requirements (mr_use_ key or OAuth), the immutable-version snapshot semantics, cursor pagination protocol, ceilings on columns/filters/sorts/aggregates/rows/bytes, and the citation fields returned. This is far beyond what annotations provide.

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?

Front-loads the key constraint (NO SQL, bounded structured query), then example, then ceilings, then pagination and auth. Dense but every sentence is load-bearing; slightly long but no filler.

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?

With 8 params, no output schema, and 0% schema coverage, the description supplies the missing pieces: auth, pagination lifecycle, hard limits, return fields, and the alternative path. An agent has everything needed to call it correctly.

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 coverage is 0%, so the description must compensate, and it does: it enumerates the operator set, shows a concrete JSON example, and documents the cursor round-trip. It leaves some gaps on offset behavior and the aggregates object shape, but the semantics for the main fields are well covered.

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 precise verb+resource: a bounded, structured query over one table's current version. Distinguishes from siblings by explicitly ruling out SQL and by pointing to download-the-Parquet for the whole table and connect_dataset for explicit connection.

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

Usage Guidelines5/5

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

Explicitly says NO SQL and what to send instead, names the alternative (Parquet download) for whole-table reads, and explains the first-query-connects behavior with connect_dataset as the explicit alternative. Covers when to use and when to use something else.

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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