Skip to main content
Glama
xmpuspus

ph-civic-data-mcp

by xmpuspus

World Bank indicator for the Philippines

get_world_bank_indicator
Read-onlyIdempotent

Retrieve Philippine World Bank macro and social indicators by code or alias, with validation and clear error reporting.

Instructions

World Bank macroeconomic and social indicator for the Philippines.

Accepts a World Bank indicator code, such as "NY.GDP.MKTP.CD", or a friendly alias, such as "gdp", "poverty_ratio", "inflation", or "urban_population_pct". The tool checks the indicator code shape before it reaches the URL, so a bad value cannot redirect the request to another country's data. Examples:

get_world_bank_indicator("gdp") # GDP, latest 20 years get_world_bank_indicator("NY.GDP.MKTP.CD", per_page=5) # same indicator, 5 years get_world_bank_indicator("poverty_ratio") # poverty headcount ratio

On failure: an indicator code or alias that fails the shape check returns data_status "invalid_request", with validation_error true and observations []. An upstream fetch failure returns data_status "unavailable", with upstream_error true, observations [], and the real error text in caveats. A row with a non-numeric or non-finite value (NaN, inf) is skipped and counted in caveats. A response where every row is non-numeric or non-finite returns data_status "unavailable" instead of a false empty answer.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
per_pageNoNumber of observations to return (latest first, default 20).
indicatorYesWB code or alias. See INDICATOR_ALIASES in source for the curated list of common indicators.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv0.4.0

TDQS

A4.7/5.0
Behavior5/5

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

The description discloses every failure mode in detail: invalid request shape, upstream unavailability, skipped non-numeric rows, and the special case where all rows are skipped. It also mentions the pre-validation of indicator codes to prevent URL redirects. This aligns fully with the readOnly and idempotent annotations, with no contradictions.

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 well-structured: it starts with a clear purpose, provides concrete examples, then lists all failure conditions in a readable bullet-like paragraph. It is detailed but not wordy, with every sentence contributing to understanding the tool's behavior.

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?

The description covers all necessary aspects for correct usage, including input formats, output expectations through failure cases, and edge cases. Although the output schema is not shown in the prompt, the description explains the key fields (data_status, observations, caveats) and the conditions under which they appear, making it self-sufficient.

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

Parameters4/5

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

The schema already describes both parameters with 100% coverage. The description adds value by explaining alias behavior, giving examples of per_page values, and clarifying that aliases are curated in INDICATOR_ALIASES. This goes beyond the schema's basic type and default information.

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 fetches a World Bank macroeconomic or social indicator for the Philippines, with concrete examples for both code and alias input. It distinguishes itself from siblings by focusing specifically on World Bank data, though it does not name alternative tools.

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 provides explicit usage examples and explains the behavior for valid and invalid inputs, including error handling. It does not explicitly contrast with sibling tools like get_poverty_stats, but the purpose is unambiguous enough that an agent can infer when to use it.

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