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xultrax-web

DogPolicies MCP Server

by xultrax-web

Changes since a date

dogpolicies_changes_since
Read-onlyIdempotent

List dog-policy verification events and rule changes since a given date, newest first, with place, outcome, checker, and record link so agents can poll updates instead of rereading every record.

Instructions

Everything that changed in the whole data set since a date: verification events and rule changes everywhere, newest first, so an agent can poll cheaply instead of re-reading every record. Each entry names the place, what happened (confirmed, changed, source_gone, blocked, disputed), who checked, and links the record.

Input Schema

NameRequiredDescriptionDefault
limitNoMaximum results to return
sinceYesDate, YYYY-MM-DD
offsetNoResults to skip, for pagination
outcomeNoOnly events with this outcome
response_formatNo'markdown' for readable text, 'json' for structured datamarkdown

Output Schema

NameRequiredDescriptionDefault
as_ofYesDate the data was built (YYYY-MM-DD)
countYes
totalYes
eventsYes
licenseYes
has_moreYes
rule_changesYes
attribution_urlYes
attribution_textYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.2.0

TDQS

A4.1/5.0
Behavior4/5

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

The annotations already declare this is a read-only, idempotent, non-destructive operation. The description adds useful behavior beyond that: results are newest first, entries include place, outcome, who checked, and record links, and the outcome set is enumerated. It does not cover auth or rate limits, but it provides solid behavioral context.

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 a single, front-loaded sentence that quickly states the purpose before adding detail. It is dense but every clause is relevant, though the outcome enumeration duplicates schema information slightly.

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?

With annotations covering safety and an output schema present, the description provides enough context to call the tool correctly: what changes are included, ordering, entry contents, and the polling use case. It need not explain return values further because the output schema exists.

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 schema already documents parameters like since, limit, offset, outcome, and response_format. The description reinforces the date scope and outcome categories but does not add syntax or format details beyond what the schema provides; baseline 3 is appropriate.

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 states a specific verb and scope: changes in the whole data set since a date, covering verification events and rule changes. The phrase 'whole data set' and 'everywhere' implicitly distinguishes it from the location-scoped sibling dogpolicies_changes_near, so an agent can tell which change-feed tool to use.

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?

It gives a clear usage context: an agent can poll this cheaply instead of re-reading every record. It does not explicitly name alternatives or exclusions, but the contrast with 'whole data set' versus a near-scoped feed is clear enough for an agent to infer when to use this tool.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.