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Glama

LTS MCP Server

by ph.godmode.lts

lts_by_region

Aggregate LTS records by DHSUD region. Returns count, market share, law breakdown (BP220/PD957), and active/expired/unknown split per region (unknown = no expiry date on record, not lapsed). Use for State of RE reports and regional housing market analysis. Cross-reference with PSGC MCP search for population data to compute per-capita density. 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)
yearNoFilter by LTS issue year
statusNoFilter by derived LTS status: active (expiry >= today), expired (expiry < today), or unknown (no expiry date on record). Records with no expiry date are NOT counted as expired

Schema Changelog

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

  1. Changed2 schema fields changed
    • changedInput schema / properties / status / description
      Previous value: -"Filter by derived LTS status: active (expiry >= today) or expired"New value: +"Filter by derived LTS status: active (expiry >= today), expired (expiry < today), or unknown (no expiry date on record). Records with no expiry date are NOT counted as expired"
    • changedInput schema / properties / status / enum
      Previous value: -[
-  "active",
-  "expired"
-]New value: +[
+  "active",
+  "expired",
+  "unknown"
+]
  2. First observed

TDQS

A4.1/5.0
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 of behavioral disclosure. It explains the meaning of 'unknown' status ('no expiry date on record, not lapsed'), discloses the 25k row cap, and instructs the agent to check the truncated flag and narrow filters when truncation occurs. It does not explicitly state read-only behavior, but 'Aggregate' and 'Returns' strongly imply a non-mutating query.

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?

The description is front-loaded with the core purpose and then flows logically through outputs, use cases, cross-reference, and limitations. Every sentence adds useful information, though the PSGC cross-reference tip is a helpful extra rather than essential for correct invocation, preventing a 5.

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?

Despite lacking an output schema, the description covers return values, the meaning of unknown status, row limits, and truncation handling. It does not specify exact field names, region definitions, or behavior when no filters are applied, but for a query tool with three optional filters this is a solid, actionable context.

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%, so the baseline is 3. The description adds minimal parameter meaning beyond the schema: it mentions law breakdown and status split, but the schema already documents the law enum, year range, and status semantics, including the 'not counted as expired' clarification. The 'narrow filters' advice is generic and does not teach parameter-specific details.

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 opens with a specific verb and resource: 'Aggregate LTS records by DHSUD region.' It then enumerates the exact outputs (count, market share, law breakdown, status split), making the tool's function unambiguous. The regional grouping plus 'regional housing market analysis' clearly distinguishes it from sibling lts_by_city, lts_by_developer, and lts_by_law tools.

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?

The description states when to use it: 'Use for State of RE reports and regional housing market analysis.' It does not explicitly name alternatives or give exclusion criteria, but the regional context is clear enough to route an agent correctly alongside the sibling by_* tools.

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