FinancialMD

Company identifiers (LEI, FIGI)

identifiers

Read-onlyIdempotent

Return a company's CIK, LEI and FIGI identifiers with their evidence: LEI accepted only when Form N-PORT filers agree and GLEIF has the record; FIGI from OpenFIGI by ticker (public domain). Metadata call: free on every tier. Use it to join FinancialMD data to another system by CIK, LEI or FIGI. If a dataset, field, company or analysis you need is missing, call request_data.

Example tool call

This call uses example arguments supplied in the tool’s input schema.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "company": "ticker:AAPL"
    },
    "name": "identifiers"
  }
}

Input Schema

Name	Required	Description	Default
`as_of`	No	Knowledge date or timestamp (YYYY-MM-DD or RFC 3339). The answer is what FinancialMD knew at that instant: only facts whose known interval contains as_of. Omit for the current view.
`company`	Yes	Company identifier: a FinancialMD id ("fmd_01H..."), an SEC CIK ("cik:320193"), or a ticker ("ticker:AAPL", optionally with the MIC "ticker:AAPL.XNAS"). A bare ticker like "AAPL" is accepted and sent as ticker:AAPL.

Output Schema

Name	Required	Description
`data`	No	{ cik, lei, items: [{ kind, value, listing, status, source, evidence, dissent, verified_name, proof }] }
`meta`	No	Request metadata: request_id, as_of, company resolution, coverage, pipeline_version, licence, terms, proof_base.
`error`	No	Present only on isError results: the API error code and message plus remedy hints.

Schema Changelog

Changes observed during successful MCP inspections.

First observedNov 13, 2026

TDQS

A4.1/5.0

Behavior4/5

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

Beyond the readOnly/idempotent/non-destructive annotations, it discloses provenance and acceptance gates: LEI only when Form N-PORT filers agree and GLEIF has the record, FIGI from OpenFIGI by ticker, and that the call is free on every tier. That pricing and source-conditional behavior is genuinely additive. It could still say what is returned when an identifier cannot be sourced.

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?

It is front-loaded with the resource and its provenance, then usage, then a fallback — a sensible order. The density is high and the provenance clauses are clause-heavy, but no sentence is pure filler.

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 an output schema present and rich annotations, the description carries only the parts structured fields cannot: source provenance, acceptance conditions, cost, and the join use case. That is sufficient for a two-parameter metadata call, though a note on partial-availability behavior would close the last gap.

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 100%, so both the company identifier formats and the as_of knowledge-date semantics are already fully documented in the schema. The description only indirectly touches parameters by mentioning 'by ticker', which the schema already covers. Baseline 3 applies.

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 opening sentence names a specific verb (Return) and a precise resource (a company's CIK, LEI and FIGI identifiers) with their evidence, which cleanly separates it from siblings like company_lookup or company_profile that return broader entity data.

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

Usage Guidelines4/5

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

It states the intended use explicitly — joining FinancialMD data to another system by CIK, LEI or FIGI — and routes the agent elsewhere (request_data) when something is missing. It does not explicitly say when *not* to use it versus company_lookup, but the join-oriented framing is clear.

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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