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ph-civic-data-mcp

by xmpuspus
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Python
Remote

Philippine consumer-price inflation

get_inflation_stats
Read-onlyIdempotent

Get year-on-year headline inflation for the Philippines or any region from PSA official data.

Instructions

Headline consumer-price inflation (year-on-year, all items) from PSA.

Source: PSA OpenSTAT Consumer Price Index, 2018-based. The tool discovers the current CPI series by text, never a hardcoded table id, and returns the most recently published month's year-on-year change. PSA publishes with a lag, so the reported period is the latest one PSA has, not always the current month. Examples:

get_inflation_stats() national headline inflation, latest month get_inflation_stats(area="NCR") one region

On failure: every failure, including an area name PSA does not list, sets data_status "unavailable" and upstream_error true, with the real error or a not-found message in caveats. validation_error stays false in every case, because a bad area name is treated as an outage here, not a caller mistake.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
areaNoRegion or "Philippines". None returns the national figure. For example "NCR", "Region VII", "Davao Region".

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changedv0.8.0
    • changedInput schema / properties / area / description
      Previous value: -"Region or \"Philippines\". None returns the national figure.\n  e.g. \"NCR\", \"Region VII\", \"Davao Region\"."New value: +"Region or \"Philippines\". None returns the national figure.\n  For example \"NCR\", \"Region VII\", \"Davao Region\"."
  2. Addedv0.4.0

TDQS

A4.6/5.0
Behavior5/5

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

Annotations mark readOnly, idempotent, and not destructive, and the description adds explicit failure semantics: any error sets data_status to 'unavailable' and upstream_error true, while validation_error stays false even for an invalid area name. This goes beyond the annotations and fully discloses edge-case behavior.

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 somewhat verbose but includes necessary repetition of examples and failure modes in a structured manner. It stays focused and does not include irrelevant information, though it could be tightened without losing clarity.

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?

Given the simple one-parameter tool and the presence of an output schema (per context), the description adequately explains the return concept (year-on-year change) and failure behavior. It does not enumerate specific output fields, but the output schema and simple structure make this unnecessary.

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?

The schema fully describes the single 'area' parameter with default null and examples. The description repeats and clarifies that null returns the national figure, and the schema coverage is 100%, leaving no ambiguity.

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 clearly states the tool returns the most recently published month's year-on-year inflation change from PSA, with national and regional options. It distinguishes from siblings like get_health_indicators and get_weather_forecast by explicitly naming inflation and the PSA source.

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 gives concrete examples and notes the publication lag, helping the agent decide when to call it. It does not explicitly compare to a sibling tool for inflation-related queries, but the purpose is evident from the title and examples.

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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