Look up a public company's CIK (Central Index Key) by ticker symbol or company name. CIK is required for all other EDGAR tools. Returns matches ranked exact-ticker first.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Maximum rows to return (default 25, max 100). | |
| query | Yes | Ticker (e.g. 'AAPL') or company-name fragment (e.g. 'Apple'). |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already cover read-only and non-destructive behavior, and the description adds the useful detail that results are ranked with exact-ticker matches first. It doesn't describe response shape or pagination, but the read-only annotations lower the burden and the extra ranking note is meaningful.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two tight sentences with no filler. The main purpose and the key prerequisite are front-loaded, and the ranking behavior is added second.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple two-parameter lookup with strong annotations, the description covers purpose, inputs, ranking, and its role in the EDGAR tool family. The absence of an explicit return-format note is a minor gap but not critical given openWorldHint and the straightforward lookup nature.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema already fully describes both query (ticker/name fragment) and limit (default/max) with examples. The description recaps the query types and adds the exact-ticker ranking hint, but no additional parameter semantics beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Description names a specific action (look up a public company's CIK), the input forms (ticker or company name), and notes the result is matches ranked exact-ticker first. It clearly distinguishes this from the other EDGAR tools by framing it as the required CIK resolver.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly says CIK is required for all other EDGAR tools, which tells the agent when to invoke this tool (before other EDGAR operations that need CIK). It doesn't explicitly name alternatives or when-not-to-use, but the context is strong.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Many tools overlap heavily across domains: caselaw_search vs court_case_search vs court_opinion_search, caselaw_citation_lookup vs court_citation_resolver, and a cluster of company due-diligence tools (company_trust_check, counterparty_risk_score, entity_dossier, issuer_diligence_dossier, kyb_aml_evidence_case_file) that all screen a company for sanctions/risk/standing. With 290 tools, an agent will frequently face multiple equally plausible choices for the same user intent.
The vast majority of tools follow a clean domain-prefix + snake_case pattern (census_, eia_, fmcsa_, npi_, cfpb_, etc.), but there are notable exceptions: entity_resolve and resolve_entity are reversed duplicates, reg_search (Federal Register) sits next to reg_cfr_search (CFR) with confusingly similar names, and carrier_monitor_recheck deviates from the carrier_vetting_* family.
290 tools is an extreme count under any rubric, far exceeding even the 50+ threshold for the lowest score. While the group-filtering mechanism and meta-tools like list_tool_groups and search_available_datasets mitigate the practical burden, the raw surface is still massively oversized for an agent to select from accurately and efficiently.
For a read-only data-aggregation server, coverage is remarkably comprehensive across 59 domains, and generic fallbacks like cdc_dataset_query, eia_series_lookup, fred_observations, and bls_series prevent most dead ends. Minor gaps exist (a single GitHub tool, demo-only property_lookup coverage, no write/update operations anywhere), but the stated data-access purpose is well served.