MarketCode

Approval rate and decision time nearby

planning_precedents

Read-onlyIdempotent

What happened to applications like this one nearby — the planning
precedent record. Give a point (`lat`/`lon`) or an `lpa_code`, optionally
narrowed by `application_types` (substrings of the authority's own type
labels, e.g. ["Householder"]).

Returns the approval RATE with its denominator, the median days to a
decision, a breakdown by application type, and recent examples with their
outcomes and links.

TWO DENOMINATORS, AND THEY ARE NOT THE SAME. The rate is over approvals
plus refusals only. The decision time is over the much smaller set of
applications carrying BOTH a received and a decision date — about 13% of
the register. Quote each with its own n; never present the timing sample
as though it were the rate's.

IT IS A RATE, NOT A PROBABILITY. It says what a committee did nearby, not
what it will do with this proposal. Do not convert it into a chance of
approval.

Withdrawals, advisory outcomes (observations on another authority's
application, and confirmations that consent was not required) and
undecided applications are counted separately and excluded from the
denominator — they appear under `excluded_from_rate`.

COVERAGE IS LONDON. The official register is the 35 Greater London
authorities; elsewhere this returns nothing, which is a coverage gap and
NOT a quiet planning history. Say so rather than implying the site is
unprecedented.
UK only. Costs 10 credits.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`lat`	No
`lon`	No
`years`	No
`examples`	No
`lpa_code`	No
`radius_m`	No
`application_types`	No

Output Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

First observedOct 15, 2026

TDQS

A4.1/5.0

Behavior5/5

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

Annotations already flag a safe read (readOnly, idempotent, non-destructive), but the description adds substantial behavioral context beyond them: two different denominators, the ~13% timing sample, what is excluded from the rate, the London coverage gap, and the 10-credit cost. This is exactly the kind of interpretation-sensitive disclosure that prevents misuse.

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?

Front-loaded with the resource and inputs, then layered warnings. It is long, but the emphasized caveats (two denominators, rate-not-probability, London coverage) each carry decision-relevant content rather than padding, and the return summary sits early where it belongs.

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?

An output schema exists, so return values are largely covered elsewhere, and the description still summarizes them well. The main gap is undocumented parameters (years, examples, radius_m) and no explicit routing against sibling tools, but for a read-only aggregate the coverage is close to complete.

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 coverage is 0% across 7 parameters, so the description carries the burden. It meaningfully documents lat/lon, lpa_code, and application_types (substring matching with an example), but leaves years, examples, and radius_m entirely unexplained. Partial compensation, hence a mid score.

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 opening sentence states a specific resource and the two metrics computed (approval rate and median decision time), and the title/detail make clear this is the planning-precedent aggregate rather than a raw listing (planning_applications) or a single-site history (property_planning_history). An agent can tell what it produces 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 Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explains how to invoke it (give a point or an lpa_code, optionally narrowed by application_types) and states hard constraints (UK only, London-only coverage), but it never says when to choose this over sibling tools such as planning_applications or planning_check. Usage is implied by context rather than explicitly routed.

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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