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lts_by_law

Break down LTS records by housing law (BP220 socialized/economic vs PD957 open market). Shows regional distribution per law and year-over-year shift in BP220 share (when no year filter). Use for housing policy analysis and socialized housing supply tracking. Capped at 25k rows; check truncated flag and narrow filters if true.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
yearNoFilter by LTS issue year. Omit for YOY shift calculation
regionNoFilter to a specific DHSUD region

Schema Changelog

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

  1. First observed

TDQS

A4.5/5.0
Behavior5/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure and does so well. It reveals the 25k row cap, the truncated flag, the advice to narrow filters, and the conditional YOY calculation—details that are not visible in the input schema.

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?

Three sentences, each earning its place: purpose, use case, and operational caveat. The description is front-loaded with the core function and contains no filler or redundant jargon.

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?

Despite having no annotations or output schema, the description provides sufficient context for an agent to decide when to call this tool and interpret potential output flags. It covers the tool's analytical purpose, the conditional behavior without a year filter, and the row cap/truncation handling—making it complete for a 2-parameter, 0-required tool.

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?

The input schema already provides 100% coverage for both parameters, so the baseline is 3. The description does not add new parameter-specific details beyond the schema; it merely restates the year-omit behavior that the schema already documents.

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 immediately states the tool's function with a specific verb ('Break down') and resource ('LTS records by housing law'), and explicitly distinguishes the two law categories (BP220 vs PD957). This clearly differentiates it from sibling tools like lts_by_city or lts_by_region.

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?

Provides clear usage context: 'Use for housing policy analysis and socialized housing supply tracking.' It also explains the conditional year-over-year shift when no year filter is applied. However, it does not explicitly state when not to use this tool or name alternative sibling tools for exclusions.

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

A4.2/5.0
Disambiguation5/5

Each tool targets a distinct aspect of LTS data (by city, developer, law, region, etc.) with no overlapping purposes. The descriptions clearly delineate their unique use cases.

Naming Consistency5/5

All tools follow the consistent pattern 'lts_<descriptive_noun_or_phrase>' using snake_case. The naming is uniform and predictable, aiding agent selection.

Tool Count5/5

With 12 tools, the set is well-scoped for a data query and analysis server. Each tool serves a clear function without redundancy or overload.

Completeness5/5

The tool set covers all necessary query dimensions (city, developer, law, region, expiry, stats, trends) and includes a search, check, and project-level view. No obvious gaps for a read-only analytics server.

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