ghed-mcp

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search_indicators

Read-only

Search health expenditure headline indicators by substring fragment. Use short codes like 'che_gdp' to find relevant indicators across categories.

Instructions

Search headline indicators; use search_variables for detailed SHA series.

This is substring search, not semantic search. Use a short fragment such as 'out-of-pocket', 'GGE' or 'che_gdp', not a full research question. If no match, shorten the query or use topics_index. category_1=None searches all variables.

Input Schema

TableJSON Schema

Name	Required	Default
`top`	No
`query`	Yes
`category_1`	No	INDICATORS
`category_2`	No

Output Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

Changed2 schema fields changedv0.6.1Sep 22, 2026
- addedInput schema / properties / category_1 / anyOf
```
Added value: +[
+  {
+    "type": "string"
+  },
+  {
+    "type": "null"
+  }
+]
```
- removedInput schema / properties / category_1 / type
```
Removed value: -"string"
```
First observedv0.5.1Jun 4, 2026

TDQS

A4.6/5.0

Behavior5/5

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

Beyond the readOnlyHint and openWorldHint annotations, the description discloses the substring matching behavior, the need for short fragments, the no-match fallback strategy, and the special category_1=None behavior. This is substantial behavioral context that helps an agent know what to expect during a search.

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 the core action and sibling differentiation, followed by concise, high-value behavioral and fallback guidance. Every sentence adds operational value without repetition or filler.

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?

For a search tool with an output schema and read-only annotations, the description covers the main usage pitfalls: substring vs semantic search, query format, fallback routes, and the category_1=None behavior. It is slightly incomplete on the semantics of top and category_2, but the schema defaults and titles make those parameters reasonably inferable.

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 0%, so the description must compensate. It adds meaning for query (substring fragments with examples) and category_1 (None searches all variables), but it does not explain top or category_2. Partial compensation only, so the score is mid-range.

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 uses a specific verb and resource ('Search headline indicators') and immediately differentiates itself from search_variables for detailed SHA series. It also clarifies that this is substring, not semantic, search, which removes ambiguity about what the tool actually does.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It explicitly tells the agent when to use this tool vs search_variables, and when to fall back to topics_index if no match. It also gives concrete query-shaping guidance (short fragments, not full research questions), making the invocation context unmistakable.

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