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Ecos Kr

by io.github.pipeworx-io

Ecos Get Series

ecos_get_series
Read-onlyIdempotent

Fetch a time series from Bank of Korea ECOS. Pass stat_code + cycle (A/Q/M/D) + start/end period. Date format matches the cycle: yearly = "2024", quarterly = "2024Q3", monthly = "202403", daily = "20240315". Optionally narrow to specific item_code(s) via item1/item2/item3/item4. Returns rows with stat_name, item_name, unit, time, data_value. Use after ecos_search_tables + ecos_series_items to discover codes.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
endYesEnd period (same format as start).
cycleYesCycle: A (annual), Q (quarterly), M (monthly), D (daily), SM (semi-monthly).
item1NoOptional item filter (e.g., "0000001" for USD in the exchange-rate table).
item2NoOptional second item filter.
item3NoOptional third item filter.
item4NoOptional fourth item filter.
limitNoMax rows to return (default 100, max 1000).
startYesStart period in cycle's native format (e.g., "2024" / "2024Q1" / "202401" / "20240101").
stat_codeYesStatistic code (e.g., "901Y009" for CPI, "731Y001" for exchange rates).

TDQS

A4.5/5.0
Behavior4/5

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

Annotations already mark readOnlyHint and idempotentHint. The description adds value by specifying the return schema (stat_name, item_name, unit, time, data_value) and critical date-format mapping, which are beyond the annotations. 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: purpose, required parameters with format, optional parameters, output, and usage context. Every sentence adds unique information without redundancy. It is front-loaded and efficiently packed.

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?

Given 9 parameters, no output schema, and 4 required, the description covers purpose, parameters, output fields, and workflow. It omits the limit parameter and potential error states but provides sufficient guidance for typical use. A bit more detail on optional parameters would push it to 5.

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?

Schema coverage is 100%, but the description adds substantial meaning by explaining the date format per cycle (e.g., yearly='2024', quarterly='2024Q3') and providing examples. This clarifies how to construct parameter values, which is not fully evident from the schema alone.

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 fetches a time series from Bank of Korea ECOS with specific verb and resource. It distinguishes from sibling tools by prescribing a workflow (use after ecos_search_tables and ecos_series_items) and mentions key input components.

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 guidance: 'Use after ecos_search_tables + ecos_series_items to discover codes,' which tells the agent when to use this tool. It lacks an explicit when-not-to-use or alternative comparisons but gives clear context for proper invocation.

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

A3.8/5.0
Disambiguation3/5

Several tool families overlap heavily: ask_pipeworx and ask_pipeworx_beta are functionally identical today, and the polymarket_edges/arbitrage/fill_risk/kalshi_spread family plus entity_profile/recent_changes/compare_entities cover adjacent jobs. The descriptions are detailed enough to separate them with careful reading, but an agent could easily select the wrong one without deep inspection.

Naming Consistency3/5

The set has recognizable prefixes like ecos_, ask_pipeworx, and polymarket_, but it also mixes verb_noun names (validate_claim, discover_tools), bare verbs (remember, forget, recall), reversed/gerund forms (bet_research, pipeworx_trending), and special tokens (generate_llms_txt). The naming is readable on a per-family basis but not predictable across the full surface.

Tool Count2/5

35 tools is above the comfortable range for a coherent tool set, and several entries are near-duplicates or wrappers: ask_pipeworx_beta is currently identical to ask_pipeworx, and scan_competitor_ai_presence wraps ai_visibility_check. The prediction-market and company-research families could be consolidated without losing capability.

Completeness4/5

For the server's broad scope, lifecycle coverage is strong: ECOS has search/items/get/indicators, subscriptions have create/list/read/cancel, memory has save/read/delete, and the data-research surface covers lookup, grounded verification, comparison, profiles, changes, and discovery. Minor gaps exist, such as no direct tool to fetch a pipeworx:// record by URI or execute a single catalog tool directly, but these are workable.

Resources