hub-equity-mcp

Official

by Hub-Equity

Overview Schema Source Related Servers Discussions Admin

Python

Remote

roll_up_metric

Read-onlyIdempotent

Reconstruct untagged aggregate figures from an issuer's reported components when a direct fact lookup returns nothing.

Instructions

Rebuild a figure from the components this issuer reported when the figure itself is not tagged. Use when get_fact finds nothing for an aggregate. Below a paid plan the value comes without algorithm and contributions.

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>",
      "hub_concept_code": "<hub_concept_code>",
      "fiscal_year": "<fiscal_year>"
    },
    "name": "roll_up_metric"
  }
}

Input Schema

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

Output Schema

Name	Required	Description
`notes`	No
`value`	No
`source`	No
`currency`	No
`algorithm`	Yes	How the value was obtained for this entity, on a paid plan: tagged, equivalent_concept or sum_of_components ('unknown' otherwise).
`fiscal_year`	Yes
`parent_code`	Yes
`parent_label`	No
`contributions`	No
`fiscal_period_type`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv2.0.0Oct 19, 2026

TDQS

A4.2/5.0

Behavior4/5

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

Annotations already establish that this is a read-only, idempotent, non-destructive, closed-world computation, so the safety profile is covered. The description adds genuinely non-obvious behavior: below a paid plan the response omits `algorithm` and `contributions`, which changes how an agent should interpret results.

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 short sentences, front-loaded with the action and the trigger, closing with the plan-gating caveat. Every sentence adds information with no restatement of the name or annotations.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

An output schema exists, so return-value explanation is not required, and the description usefully flags the tier-gated omissions from that output. However, for a domain-specific aggregation tool with zero schema description coverage, the inputs an agent must supply are entirely unexplained, leaving a real gap.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0% for four parameters (entity_id, hub_concept_code, fiscal_year, fiscal_period_type with its FY default), and the description explains none of them. The only identifiers it mentions, `algorithm` and `contributions`, are output fields, not inputs, so it does not compensate for the schema gap.

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 action on a specific resource: rebuild a reported figure from its components when the figure is untagged. It also names the sibling it substitutes for (get_fact), so an agent can separate the two without opening schemas.

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?

Gives an explicit routing rule: 'Use when get_fact finds nothing for an aggregate.' The alternative tool and the condition that selects this one are both named, leaving nothing to inference.

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