hub-equity-mcp

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get_fact_decomposition

Read-onlyIdempotent

Break down an XBRL financial fact into rollup components, calculation linkbase children, and dimensional slices by entity and fiscal year.

Instructions

Decompose a financial fact into its constituent parts.

Pass EITHER hub_concept_code (preferred - e.g. TOTAL_ASSETS) OR qname (raw XBRL like 'us-gaap:DeferredRevenueNoncurrent') for concepts without a hub mapping.

Returns three layers:

rollup_children - the components this issuer reported for the figure, with their sign, on a paid plan; empty below it, with a note.
linkbase_children - XBRL calc linkbase children, RECURSIVE up to depth (max 3). Each child has its own children list.
dimensional_breakdown - axis/member slices (segment, geography), on a paid plan; empty below it, with a note.

depth=1 is open to every caller. On a paid plan, use depth=2 for aggregates (TOTAL_ASSETS, TOTAL_LIABILITIES) to get the full breakdown tree in ONE call instead of N round-trips.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "entity_id": "<entity_id>",
      "fiscal_year": "<fiscal_year>"
    },
    "name": "get_fact_decomposition"
  }
}

Input Schema

Name	Required	Default
`depth`	No
`qname`	No
`entity_id`	Yes
`fiscal_year`	Yes
`hub_concept_code`	No
`fiscal_period_type`	No	FY

Output Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv2.0.0Oct 19, 2026

TDQS

A3.9/5.0

Behavior5/5

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

Goes well beyond the annotations, which only cover safety (readOnly/idempotent/non-destructive). It discloses entitlement behavior (two of three layers are paid-plan gated and return 'empty... with a note' below it), a hard recursion cap ('depth max 3'), and the round-trip tradeoff that motivates depth=2 — all operationally valuable and not derivable from structured fields.

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?

Front-loads the one-line purpose, then uses a numbered list for the three layers and a closing line for depth/plan guidance — a clean structure that earns its length. Slightly verbose in places, but each clause carries information.

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?

An output schema exists so return shapes needn't be restated, yet the description helpfully sketches the three layers and their gating. The remaining gap is the unexplained identity/period parameters, which an agent must guess at for a required-parameter operation.

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 0%, so the description bears the full burden and it clearly explains the hub_concept_code/qname tradeoff and depth semantics, including a worked qname example. But entity_id, fiscal_year, and fiscal_period_type receive no explanation at all, so half the parameters remain undocumented in both places.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb ('decompose') and resource ('financial fact') followed by a concrete overview of the three output layers. However, it never distinguishes itself from the sibling `get_calculation_tree`, which also surfaces XBRL calc linkbase structure, so an agent must infer which one to reach for.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Gives explicit parameter-selection guidance ('Pass EITHER hub_concept_code (preferred) OR qname') and depth guidance tied to plan tier, which is useful context. But it never says when to prefer this tool over alternatives like get_calculation_tree, get_fact, or roll_up_metric, leaving the routing decision implied.

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