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Ecos Search Tables

ecos_search_tables
Read-onlyIdempotent

Search Bank of Korea ECOS statistic tables — 800+ official Korean economic series across monetary policy, exchange rates, prices, balance of payments, GDP, real estate, household credit, etc. Returns stat_code + Korean name + cycle (A annual / Q quarterly / M monthly / D daily). Use as a directory before ecos_get_series. q is matched substring-wise against the Korean name; pass blank to browse top-N.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
qNoKorean substring to match against stat names. Empty = browse top-N.
limitNoMax rows to return (default 30, max 100).

TDQS

A4.3/5.0
Behavior4/5

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

Annotations already provide read-only, open-world, idempotent, and non-destructive hints. The description adds context on the return format and matching behavior, which is helpful beyond 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?

Two concise sentences with essential information. It is front-loaded with the purpose and quickly lists key details. No unnecessary words.

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 tool's simplicity (search directory with two parameters, no output schema), the description fully covers what the tool does, how to use it, and what it returns. Annotations cover safety and idempotency.

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 100%, so the description adds no new meaning beyond what is already in the schema. It rephrases the same info (e.g., 'q is matched substring-wise' matches schema). Baseline 3 is appropriate.

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 searches 'Bank of Korea ECOS statistic tables' and lists the types of economic series. It specifies the return fields (stat_code, Korean name, cycle) and distinguishes itself from ecos_get_series by saying 'Use as a directory before ecos_get_series.'

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 explains the usage as a directory before ecos_get_series and describes how the 'q' parameter works (substring match, blank for top-N). It provides clear context but could be more explicit about when not to use this tool vs. other 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

A3.8/5.0
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.