filing-agent

xbrl_find_concepts

Read-only

Find the XBRL concepts a company uses for a described line item, returning tag names, labels, units and years covered. Run it before xbrl_get_facts, since tag names vary by company and year.

Instructions

Find the XBRL concepts (tags) a company reports that match a description, e.g. 'payments to acquire property plant and equipment' or 'revenue'. Returns concept names, labels, units and the years covered. Use it before xbrl_get_facts: tag names differ between companies and years.

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": {
      "company": "<company>",
      "query": "<query>"
    },
    "name": "xbrl_find_concepts"
  }
}

Input Schema

Name	Required	Description
`query`	Yes	Words describing the line item, e.g. 'revenue'.
`top_k`	No	Concepts to return (default 10).
`company`	Yes	Company name, ticker or CIK.

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Nov 13, 2026

TDQS

A4.4/5.0

Behavior4/5

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

Annotations already establish that this is a safe read (readOnlyHint=true, destructiveHint=false), so the description's job is to add context, which it does by naming the returned fields (concept names, labels, units, years covered). It does not mention result ordering/ranking, behavior on zero matches, or cost for large companies, so it stops short of full disclosure.

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: purpose, return payload, then workflow routing. Nothing is redundant and the primary intent is front-loaded.

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 no output schema, the description usefully tells the agent what comes back and how the result feeds the next tool, which is the key missing piece for this tool. Minor gaps remain around result ranking and what an empty result implies, but nothing essential to invoking it is absent.

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 coverage is 100%, so all three parameters and the top_k default are already documented. The description reinforces the intended natural-language style of "query" via examples, but adds no format rules (e.g. keyword vs. phrase, singular vs. plural) beyond the schema.

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 verb and resource ("Find the XBRL concepts (tags)") scoped to a company, and gives two concrete query examples. It also distinguishes the tool from xbrl_get_facts by positioning it as the tag-discovery step, so an agent can tell the two apart without opening either schema.

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?

Explicitly prescribes the call order ("Use it before xbrl_get_facts") and gives the reason ("tag names differ between companies and years"). That is an actionable alternative-selection rule rather than vague context.

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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