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Rba

by io.github.pipeworx-io

Rba Series

rba_series
Read-onlyIdempotent

Fetch any RBA statistical series — pass a NAME or description in series (e.g. "90-day bank bill rate", "3-year government bond yield") and it resolves the table + series id for you by fuzzy-matching series titles/descriptions across the RBA catalog (the interest-rate and bond-yield tables are searched first), the same way rba_list_series does. Returns recent observations plus resolved_from/resolved_to showing what the name resolved to, or a candidates list if the name is ambiguous — never guesses a made-up id. Or pass table+series_id directly if you already know them exactly (CPI is g1, monetary aggregates d3, cash rate target is FIRMMCRT in f1.1, etc.). Use rba_cash_rate / rba_exchange_rates for those specific common series.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
tableNoRBA table id, e.g. "f1.1" (money market), "f2.1" (bond yields), "f11.1" (FX), "g1" (CPI), "d3" (monetary aggregates). Optional: narrows `series` resolution to this table; required alongside series_id for the exact legacy path.
recentNoRecent observations to return (1-120, default 12).
seriesNoA series NAME/description to resolve (e.g. "90-day bank bill rate", "3-year government bond yield") OR an exact series id (e.g. "FIRMMBAB90"). Preferred over table+series_id — resolves the id for you and returns resolved_from/resolved_to (or candidates if ambiguous).
series_idNoLegacy: exact RBA series id within `table`, e.g. "FIRMMCRT" (cash rate target), "FXRUSD" (A$/USD). Prefer `series`, which accepts a name and resolves this for you.

TDQS

A4.6/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false, so the description does not need to restate safety. It adds useful behavioral context: 'Returns recent observations' and implies that unknown IDs cause failing lookups, hence the routing to rba_list_series. No contradiction with annotations.

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?

Three sentences, each earning its place: purpose and scope, routing to common siblings, and the fallback lookup procedure. It is front-loaded with the action and resource, with zero 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 simple read-only fetch tool with a 100%-documented schema and no output schema, the description covers purpose, alternatives, failure avoidance, and return behavior ('Returns recent observations'). It does not specify the exact output format, but that is a minor gap given the low complexity and strong annotations.

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 description coverage is 100%, so the baseline is 3. The description adds semantic scope by calling the tool an 'escape hatch for the full RBA statistical-tables catalog' and provides domain examples (CPI is g1, monetary aggregates d3) that reinforce how to choose table ids. This exceeds what the schema already documents.

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?

States a specific verb and resource: 'Fetch any RBA statistical series by table id + series id' and describes itself as an 'escape hatch for the full RBA statistical-tables catalog.' It clearly distinguishes itself from siblings by naming rba_cash_rate / rba_exchange_rates and rba_list_series.

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 directs when to use alternatives: 'Use rba_cash_rate / rba_exchange_rates for the common ones.' It also provides a concrete if-then fallback: 'If you do not already know the table id and series id, call rba_list_series first.' An agent knows exactly when to select this tool versus its siblings.

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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TDQS

A3.7/5.0
Disambiguation2/5

Several tool clusters overlap heavily: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all route natural-language questions to the same underlying catalog, and polymarket_arbitrage, polymarket_edges, polymarket_edge_tracker, polymarket_fill_risk, and polymarket_kalshi_spread all target prediction-market opportunities. entity_profile, recent_changes, and compare_entities also share company-research territory, making misselection likely without reading long descriptions carefully.

Naming Consistency3/5

All names use snake_case, but conventions are mixed: some are verb_noun (list_subscriptions, generate_llms_txt, resolve_entity), some are noun phrases (entity_profile, rba_cash_rate), and some are brand-prefixed product names (ask_pipeworx, pipeworx_trending). The polymarket_* and rba_* families are internally consistent, but the overall surface has no single predictable pattern.

Tool Count2/5

35 tools is a large surface, well above the 25+ threshold that typically becomes unwieldy. While the server covers a broad domain (data lookup, prediction markets, memory, subscriptions, company research), many tools are niche variants (ask_pipeworx_beta, polymarket_edge_tracker, scan_competitor_ai_presence) that add cognitive load rather than earning their place.

Completeness3/5

Core flows are well covered: memory has remember/recall/forget, subscriptions have subscribe/list/unsubscribe/recent_alerts, and data access has ask_pipeworx plus grounded and research variants. However, the surface is sprawly and uneven — prediction markets get six tools while other domain areas rely on generic routing, and the server's overall purpose is diffuse enough that gaps are hard to assess cleanly.

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