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Hub-Equity

hub-equity-mcp

Official
by Hub-Equity

get_extension_concepts

Read-onlyIdempotent

Retrieve issuer-specific XBRL extension concepts with labels, usage counts, and standard anchors, filtered by mapping status, to identify custom taxonomy items declared by a company.

Instructions

Issuer-specific qnames declared outside standard taxonomies (us-gaap / ifrs-full).

Returns up to limit (cap 100) extension concepts sorted by usage_count desc. status_filter ∈ {all, mapped}: mapped keeps the extensions Hub-Equity reads into a standardised hub.

For each extension: qname, label, item type, period type, balance, usage count, the issuer's own documentation and the standard concept it is anchored to (ESEF).

Use for 'what custom concepts has Apple declared?'.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNo
entity_idYes
status_filterNoall

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv2.0.0

TDQS

A4.3/5.0
Behavior4/5

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

Annotations already cover the safety profile (readOnly, idempotent, non-destructive, closed-world), so the bar is lower. The description adds real behavioral detail beyond them: a hard cap of 100 results, sort order by usage_count desc, and the semantics of the status_filter values. It does not mention pagination/truncation behavior when the cap is hit.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The definition is front-loaded with the core purpose and keeps parameter semantics compact. The return-field enumeration ('qname, label, item type, ...') is somewhat redundant given an output schema exists, which is minor padding.

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?

With an output schema present, the description need not explain return values, yet it still does, and it covers the limit cap, sort order, and filter semantics that the schema omits. It is complete enough to invoke correctly; only pagination/truncation edge behavior is unstated.

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 coverage is 0%, so the description must carry the load, and it largely does: it defines the `limit` cap (100) and enumerates `status_filter` values {all, mapped} with the meaning of `mapped` (extensions read into a standardised hub). Only `entity_id` is left unexplained, which is near-self-evident from the name.

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 names a specific resource ('issuer-specific qnames declared outside standard taxonomies') and scopes it against the standard taxonomies (us-gaap / ifrs-full), which cleanly separates it from siblings like search_concept or get_fact. An agent can tell what it returns and why it exists without opening the schema.

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?

It gives a concrete invocation cue ('Use for "what custom concepts has Apple declared?"'), which is strong positive guidance. It does not explicitly state when NOT to use it or name the alternative tool for standard concepts, so it stops short of full routing guidance.

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