edgar-mcp

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get_concept

Retrieves a time series for an XBRL concept like Revenues or Assets from SEC EDGAR filings, showing values reported across multiple company filings.

Instructions

Time series for one XBRL concept (e.g. Revenues, Assets, NetIncomeLoss) as reported by a company across filings.

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": {
      "query": "<query>",
      "tag": "<tag>"
    },
    "name": "get_concept"
  }
}

Input Schema

Name	Required	Default
`tag`	Yes
`limit`	No
`query`	Yes
`taxonomy`	No	us-gaap

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Jul 31, 2026

TDQS

C2.9/5.0

Behavior2/5

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

No annotations are provided, so the description must carry the full burden of behavioral disclosure. It does say the tool returns a time series across filings, but it does not disclose the return format, pagination behavior, authentication needs, or how errors are handled. This is insufficient for a read tool with no annotations.

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 description is a single, front-loaded sentence with no fluff. However, its brevity comes at the cost of important detail, so it is concise but perhaps too sparse for the tool's complexity.

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

Completeness1/5

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

With four parameters, no annotations, and no output schema, the description is far too minimal to allow correct invocation. It does not explain how to specify the company, what the query vs. tag are for, or what the returned time series looks like. Significant gaps remain.

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%, and the description does not clarify the roles of the four parameters (query, tag, limit, taxonomy). It offers examples of concept names but fails to explain which parameter is the concept tag vs. the query, or how limit and taxonomy affect results.

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 that this tool returns a time series for a single XBRL concept (e.g., Revenues, Assets, NetIncomeLoss) as reported by a company across filings. This specific verb+resource combination distinguishes it well from siblings like list_concepts and compare_concept.

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?

The description implies when to use this tool—when you need a single concept's history over time—but it does not explicitly mention alternatives or exclusions. Sibling tools like compare_concept exist, but no direct guidance is provided on when to prefer one over the other.

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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