USERDATA

compute_concept

Computes a custom company concept from reported facts and stores it to support traceable equity research and XBRL concept validation.

Instructions

Computes an own concept from a company's facts and stores it.

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": {
      "concept_name": "<concept_name>",
      "ticker": "<ticker>"
    },
    "name": "compute_concept"
  }
}

Input Schema

Name	Required	Description	Default
`ticker`	Yes
`concept_name`	Yes

Output Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Nov 8, 2026

TDQS

C2.2/5.0

Behavior2/5

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

No annotations are supplied, so the description carries the full disclosure burden. It does state the result is stored, hinting at a write side effect, but says nothing about whether an existing concept is overwritten, what permissions or prior state (pulled facts) are required, or how the computation is scoped to a company.

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

Conciseness3/5

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

It is a single front-loaded sentence with no waste, which is structurally good. The brevity, however, comes at the cost of under-specification rather than being genuinely concise.

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

Completeness2/5

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

An output schema exists, so return values need not be described. But for a two-parameter, annotation-free tool that mutates stored state and depends on a facts pipeline, the description leaves required inputs and side effects entirely unexplained.

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

Parameters1/5

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

Schema description coverage is 0% — both parameters appear only with bare titles. The description does not clarify what form `concept_name` takes (an identifier, an expression, a free-form label?) or that `ticker` identifies the company whose facts are used, so it fails to compensate for the coverage gap.

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

Purpose3/5

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

The description names a verb ("computes") and a resource ("concept") and adds that it persists the result, which is more than a tautology. However, "own concept" is unexplained jargon and the description gives no indication of how this differs from the sibling tool `define_concept`, which an agent would plausibly confuse it with.

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

Usage Guidelines2/5

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

There is no when-to-use guidance, no prerequisites (e.g. that company facts must be pulled first), and no mention of `define_concept` as the alternative. The agent must guess which of the two concept tools applies to its situation.

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