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ph-civic-data-mcp

ph-civic-data-mcp

by xmpuspus
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

Philippine labor-force indicators

get_labor_stats
Read-onlyIdempotent

Get the latest national labor-force indicators from the PSA Labor Force Survey: participation, employment, unemployment, and underemployment rates.

Instructions

Key labor-force indicators from the PSA Labor Force Survey.

Returns labor-force participation, employment, unemployment, and underemployment rates for the latest published reference period. The PSA key-indicator series is national only. Passing region does not filter the figure. It only adds an explanatory caveat, because this table has no regional breakdown. Examples:

get_labor_stats() national rates, latest reference period get_labor_stats(region="Cebu") same national rates, plus a caveat

On failure: an OpenSTAT discovery or query failure sets data_status "unavailable" and upstream_error true, with the real error in caveats. validation_error stays false in every case.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "get_labor_stats"
  }
}

Input Schema

NameRequiredDescriptionDefault
regionNoAccepted for API symmetry. The LFS key-indicator table is national only. Passing a region adds an explanatory caveat.

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changedv0.8.0
    • changedInput schema / properties / region / description
      Previous value: -"Accepted for API symmetry. The LFS key-indicator table is\n    national only; passing a region adds an explanatory caveat."New value: +"Accepted for API symmetry. The LFS key-indicator table is\n    national only. Passing a region adds an explanatory caveat."
  2. Addedv0.4.0

TDQS

A4.8/5.0
Behavior5/5

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

Beyond the readOnly and idempotent annotations, the description discloses failure behavior in detail, including data_status 'unavailable', upstream_error true, caveats carrying the real error, and validation_error always false. This gives agents a clear picture of success and failure modes.

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, starting with purpose, then return content, then parameter behavior, examples, and failure semantics. It is concise without redundant padding and each section adds useful information.

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?

For a simple one-parameter read-only tool, the description covers purpose, return indicators, parameter semantics, example calls, and failure behavior. It is fully sufficient for an agent to decide when and how to call it correctly.

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 sole parameter 'region' is fully described in both the schema and the description, with explicit clarification that it is accepted for API symmetry and does not filter results. The description adds meaningful behavioral detail beyond the schema.

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 key labor-force indicators from the PSA Labor Force Survey, including participation, employment, unemployment, and underemployment rates. It also notes the national-only scope, distinguishing it from other area-level statistics 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 clear context for when to use the tool: when national labor-force indicators for the latest period are needed. It explicitly explains that passing a region does not filter results but only adds a caveat, giving clear usage expectations without referencing alternatives.

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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