portugal-mcp

sns_surgery_wait_stats

Retrieve monthly Portuguese NHS surgery waiting-list data by hospital/ULS or region: patients waiting, compliance with guaranteed maximum times, mean days to surgery, and operations performed.

Instructions

Surgery access indicators per month: surgical waiting list, % within the guaranteed maximum time, mean days from admission to surgery, and surgeries performed.

Use for "how long is the wait for surgery at X?", "how many people are on the surgery list in the Norte region?", "how many operations did hospital Y do?". Official monthly statistics (latest month usually 2-3 months back, provisional). The open data has NO breakdown by specialty or procedure — say so if asked for one.

Per period (latest first):

waiting_list: patients on the surgical waiting list (LIC) at month end, how many are still within the legal maximum guaranteed response time (TMRG, 180 days for normal priority since 2018) and the % within it.
mean_days_before_surgery_in_month / _year_to_date: average days between hospital admission and the operation for scheduled inpatient surgery (this is NOT the time on the waiting list).
surgeries_in_month / surgeries_year_to_date: scheduled (= inpatient + day surgery) and urgent.

Args: institution: Hospital or ULS name ("Santa Maria", "Coimbra", "IPO Porto"); mapped to its ULS. region: Norte | Centro | LVT (Lisboa) | Alentejo | Algarve. Ignored if institution is given. period: Latest month to include, "YYYY-MM" (default: newest available). months: How many months back (default 6, max 24).

Input Schema

Name	Required	Description	Default
`months`	No
`period`	No
`region`	No
`institution`	No

Output Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.1Nov 2, 2026

TDQS

A4.8/5.0

Behavior5/5

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

With no annotations, the description carries the full burden and does so well: it discloses that figures are official monthly statistics, usually lagging 2-3 months, provisional, and lacking specialty/procedure breakdowns. It also clarifies metric definitions such as LIC, TMRG, and that mean days before surgery is not time on the waiting list.

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 front-loaded with purpose, use cases, and limitations, then organizes metric definitions and arguments into clearly labeled sections. Despite its length, every part earns its place by compensating for the absent annotations and undocumented schema parameters.

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?

Given four parameters, 0% schema coverage, no annotations, and the presence of an output schema, the description is complete enough for correct invocation. It covers input semantics, data freshness, limitations, and metric meanings without needing to duplicate return-structure documentation.

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?

Although schema description coverage is 0%, the Args section fully explains all four parameters: institution mapping to ULS, region values and precedence, period format and default, and months default/max. This adds clear semantics beyond the bare schema types.

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 resource and scope: monthly surgery access indicators including waiting list, TMRG compliance, mean days to surgery, and surgeries performed. It distinguishes itself from sibling tools like sns_consultation_wait_stats by focusing explicitly on surgical waiting statistics.

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 concrete usage examples for wait times, regional list sizes, and hospital operation volumes, and it explicitly notes the lack of specialty/procedure breakdown. However, it does not name alternative tools or state when to use another sibling instead, leaving routing slightly implicit.

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