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Monthly transaction volume

market_volume_series
Read-onlyIdempotent
Monthly transaction VOLUME for a postcode district, postcode area or
county — every registered sale, 1995 to present, oldest month first.

Use this, not `market_facts`, for "how many sales" questions.
`market_facts` reads the area_facts cube, whose count is a UPRN-matched
sample that runs 17-66% short of the registry; this reads HMLR price paid
directly. Conversely use `market_facts` for price/rent/yield by asset
class and bedroom band, which this does not break down.

Provide exactly one of district ("SW11", or a full postcode, which is
truncated), area ("SW") or county ("GREATER LONDON"). `district` is a
POSTCODE district, not a borough — "NW1", not "Camden".

`start` and `end` are months: "2024-06" or "2024-06-01", either accepted.

Metrics default to txn_count + median_price. Prices are category A only —
a median including repossessions is not a market median.

ALWAYS check `provisional_from` in the response before reading a trend:
HM Land Registry registers sales months after completion, so the trailing
months are incomplete and will rise. UK only. Free.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
endNo
areaNo
startNo
countyNo
metricsNo
districtNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A5/5.0
Behavior5/5

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

Annotations already cover the safety profile (readOnly, idempotent, openWorld=false), and the description adds substantive behavior beyond that: data provenance, the `provisional_from` caveat that trailing months are incomplete and will rise, category A-only prices, and UK-only scope. This is exactly the context annotations cannot carry.

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?

Despite its length, it is front-loaded with the core purpose and every sentence carries actionable information — routing, parameter formats, and the provisional-data caveat. No filler or restatement of the title.

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?

An output schema exists, so return structure need not be documented, yet the description still flags `provisional_from`, the single most important field for correct trend reading, plus data scope and provenance. Nothing needed to invoke or interpret it correctly is missing.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must compensate and it does: exactly one of district/area/county, concrete examples ("SW11", "SW", "GREATER LONDON"), the note that a full postcode is truncated, the district-is-not-a-borough warning, accepted month formats ("2024-06" or "2024-06-01"), and default metrics.

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 specific resource (monthly transaction volume), the exact geographies covered (postcode district/area/county), and the temporal span (1995 to present, oldest month first). It explicitly distinguishes itself from the sibling `market_facts` in both directions, so an agent can route without opening the schema.

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?

Gives explicit when-to-use ("how many sales" questions) and when-not-to-use, naming the alternative `market_facts` and the reason (its count is a UPRN-matched sample 17-66% short vs HMLR price paid). It also redirects price/rent/yield queries back to market_facts.

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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