Skip to main content
Glama
Bigred97

Reserve Bank of Australia

search_tables

Find RBA F-tables by searching keywords like 'cash rate' or 'AUD/USD'. Returns relevant table IDs, names, and descriptions to identify the right table for your query.

Instructions

Fuzzy-search RBA F-tables by name and topic.

Use this when you don't know the exact table ID. The 5 curated F-tables (F1.1, F4, F6, F11, F11.1) cover the most-asked indicators: cash rate, money-market rates, household lending rates, FX rates.

Examples: # Find the F-table that publishes the cash rate results = await search_tables("cash rate") # → [{id: 'F1.1', name: 'Interest Rates and Yields - Money Market', ...}]

# Discover what's available on FX
results = await search_tables("aud usd", limit=5)
# → top 5 FX-related tables, curated F11/F11.1 first

When to use: - You have a natural-language question and need to identify the table - You want to discover what RBA publishes on a topic - You're enumerating the F-table catalog programmatically

Returns: List of TableSummary (id, name, frequency, description), ranked by relevance. Curated tables surface above the rest.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMaximum number of results to return, ranked by relevance.
queryYesFree-text search query. Matches against F-table IDs, names, and topic keywords. Case-insensitive.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior5/5

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

No annotations provided, so description carries full burden. It discloses fuzzy matching, ranking by relevance, curated tables surfaced first, and return structure (List of TableSummary). Behavior is clearly read-only and transparent.

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?

Well-structured with distinct sections: one-liner summary, context, examples, when-to-use, returns. Every sentence adds value. Appropriate length for the complexity.

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 the presence of output schema (TableSummary) and comprehensive input schema, the description fully covers purpose, parameters, and behavior. No gaps identified; it addresses common usage scenarios and tool selection.

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?

Schema already covers both parameters with descriptions and examples (100% coverage). Description adds value by explaining matching logic (case-insensitive, against IDs/names/topics) and providing usage examples, but does not drastically improve the semantics.

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 it performs fuzzy-search on RBA F-tables by name and topic. It uses specific verb and resource, and distinguishes from sibling tools like list_curated (catalog listing) and describe_table (specific ID). Examples reinforce the purpose.

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?

Explicitly states when to use: 'when you don't know the exact table ID', 'natural-language question', 'discover what RBA publishes'. Implies alternatives when ID is known. Also notes curated tables for common indicators, aiding tool selection.

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

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/Bigred97/rba-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server