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lts_trends

Show LTS issuance trends over time with annual or quarterly granularity. Returns period counts with law breakdown, peak period, and year-over-year growth percentage. Use for housing supply pipeline analysis and market timing. Capped at 25k rows; check truncated flag and narrow filters if true.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
lawNoFilter by housing law: BP220 (socialized/economic) or PD957 (open market)
regionNoFilter to a specific DHSUD region
to_yearNoEnd year (inclusive)
from_yearNoStart year (inclusive)
granularityNoTime bucket granularityannual

Schema Changelog

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

  1. First observed

TDQS

A4.2/5.0
Behavior4/5

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

With no annotations provided, the description carries the full burden. It discloses a 25k row cap, truncated flag behavior, and return content (period counts, law breakdown, peak period, YoY growth), providing essential behavioral context beyond the 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?

The description is two sentences, front-loaded with purpose and granularity, then return details, use case, and limit. Every sentence earns its place with no filler.

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?

Since there is no output schema, the description explains key return fields and truncation behavior, which is sufficient for an agent. It lacks precise field names/type structure but provides enough context for decision-making.

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 has 100% coverage with descriptions for all 5 parameters, so the baseline is 3. The description adds marginal reinforcement of granularity and law breakdown but does not introduce new parameter meaning.

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 uses a specific verb 'Show' and resource 'LTS issuance trends over time' with granularity, which clearly distinguishes it from sibling tools that focus on breakdowns by city, developer, law, or 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 explicit use case 'for housing supply pipeline analysis and market timing,' giving clear context for when to use the tool. However, it does not explicitly reference alternative tools or exclusions, so it falls short of a 5.

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.

Resources