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Drupal Code Query

by tech.tresbien

Describe the dataset

describe_dataset
Read-onlyIdempotent

Learn the dataset before writing SQL for query_dataset. Call it with no arguments first. Returns text. Prefer a typed tool when one answers the question.

  • No arguments: one line per view, the join map, the four invariants, the causes of empty results, and the recipe index. The invariants: dev-branch isolation, no SUM(usage), *_seq compares, adoption polarity. Empty results come from fqn forms, placeholders and machine name forms.

  • view=, or views=[…] for several in one call. Returns the columns with types and descriptions, an example filter that returns rows, and the joins into the view.

  • recipe=: a ready-to-run statement for a common question. Fill in the placeholders and run it with query_dataset. Recipes cover who uses a symbol, deprecated symbols per project, and a change record's adoption. Others cover symbols deprecated between minors, one project's deprecated uses, and change records between minors.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
viewNoOne view name. Returns its columns with types and descriptions, an example filter, and the joins into it. Leave empty for the overview.
viewsNoUp to 8 views at once, documented one after the other in one call.
recipeNoOne recipe id from the overview's index. Returns a ready-to-run query_dataset statement with its placeholders explained.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Changed3 schema fields changed
    • changedInput schema / properties / recipe / description
      Previous value: -"One recipe id from the overview's index: a ready-to-run query_dataset statement with its placeholders explained."New value: +"One recipe id from the overview's index. Returns a ready-to-run query_dataset statement with its placeholders explained."
    • changedInput schema / properties / view / description
      Previous value: -"One view name: its columns with types and descriptions, an example filter, and the joins that reach it. Leave empty for the overview."New value: +"One view name. Returns its columns with types and descriptions, an example filter, and the joins into it. Leave empty for the overview."
    • changedInput schema / properties / views / description
      Previous value: -"Several view names at once (up to 8), documented one after the other in one call."New value: +"Up to 8 views at once, documented one after the other in one call."
  2. Changed1 schema field changed
    • changedOutput schema / (root)
      Previous value: -{
-  "additionalProperties": false,
-  "type": "object"
-}New value: +null
  3. Changed1 schema field changed
    • addedInput schema / properties / views
      Added value: +{
+  "description": "Several view names at once (up to 8), documented one after the other in one call.",
+  "items": {
+    "type": "string"
+  },
+  "type": [
+    "null",
+    "array"
+  ]
+}
  4. Added

TDQS

A4.9/5.0
Behavior5/5

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

Annotations already indicate readOnly, idempotent, non-destructive behavior, and the description adds rich behavioral context: output varies by argument mode, no-args returns invariants and empty-result causes, view mode returns columns/filters/joins, and recipe mode returns ready-to-run statements. No contradictions with annotations.

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 dense but well-structured with a leading usage directive and clear bullet-style modes. Every sentence carries useful information, and the most important instruction ('call it with no arguments first') is front-loaded.

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 read-only metadata tool with no output schema, the description covers all three invocation modes, return contents, and connection to query_dataset. It also gives enough detail about invariants and empty-result causes to set agent expectations. Nothing critical is missing.

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 100%, so the baseline is 3. The description adds extra meaning by explaining the no-arg overview, how view vs. views relate, what each variant returns, and how recipes connect to query_dataset. This goes beyond the schema descriptions without needing to compensate for missing schema docs.

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 this tool learns/describes the dataset before SQL work, and distinguishes it from query_dataset and other typed siblings. It specifies exactly what the tool does: returns an overview, view metadata, or recipes depending on arguments.

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?

It explicitly says to call it before writing SQL for query_dataset, to call with no arguments first, and to prefer a typed tool when one answers the question. This provides clear when-to-use and when-not-to-use guidance relative to siblings.

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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TDQS

A4.6/5.0
Disambiguation4/5

Tools cluster into related families (change records, symbol usage, patch checks), but descriptions and cross-references clearly separate list vs detail vs diff operations. Some adjacent tools like lookup_core_symbol and list_symbol_users require careful reading, yet their purposes are distinct enough for an agent to select correctly.

Naming Consistency4/5

Most tools follow a clear snake_case verb_noun pattern: list_*, get_*, lookup_*, query_, scan_, search_, reroll_, describe_. A few names like project_profile, subsystem_coupling, and what_changed deviate from verb_noun but remain consistent in style and readable.

Tool Count4/5

At 16 tools, the set is slightly above the typical well-scoped range, but the domain is broad: patch lifecycle, composer scanning, symbol lookup, change records, project profiles, dataset queries, and code search. Each tool covers a distinct query surface, so none feels redundant.

Completeness5/5

The toolkit covers the full read-only analysis lifecycle: scanning composer constraints, checking and re-rolling patches, exploring datasets, looking up core symbols and their users, listing change records, profiling projects, and assessing upgrade readiness. No obvious dead ends or missing operations for its stated purpose.

Resources