SEC company facts (EDGAR) — Latest key financial facts for a US public company by ticker or CIK: revenue, net income, assets, EPS from XBRL filings. Source: SEC EDGAR. JSON. Price: $0.01 USDC (Base, via x402).
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes | ticker (AAPL) or CIK number |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The annotations already establish read-only and non-destructive behavior. The description adds meaningful context beyond annotations by naming the source (SEC EDGAR), the underlying data type (XBRL filings), the response format (JSON), and the cost ($0.01 USDC via x402). This goes beyond what annotations convey.
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?
The description is compact and front-loads the core purpose before listing source, format, and price. Minor redundancy exists because the description begins with 'SEC company facts (EDGAR)', which matches the title, and later repeats 'Source: SEC EDGAR', but overall it is efficient and well-ordered.
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 single-parameter read-only tool, the description covers the input type, data source, returned metric fields, output format, and cost. There is no output schema, so the listed fields partially compensate for the missing return structure, though it does not specify period, currency, or whether all listed facts are always present.
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 documents the single parameter id as 'ticker (AAPL) or CIK number', and the description repeats this by saying 'by ticker or CIK'. Since schema description coverage is 100%, the description adds no significant 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?
The description clearly identifies the tool as providing SEC EDGAR company facts, specifically latest key financial metrics like revenue, net income, assets, and EPS for a US public company. It is specific about the resource and data source, though it does not explicitly differentiate itself from the closely named sibling sec_company_snapshot.
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?
The phrase 'Latest key financial facts... by ticker or CIK' implies the intended use case: retrieving core financial metrics for a US public company. However, it does not explicitly state when to prefer this over related siblings such as sec_company_snapshot or sec_filings_search, leaving some selection ambiguity.
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 are clearly separated by prefix and data source, but several bundled products overlap heavily: vehicle_deal_check vs vehicle_report, realestate_property_report vs realestate_site_risk, finance_company_360 vs finance_health_scan, and domain_due_diligence vs email_domain_check/business_vet. An agent would frequently struggle to pick the correct premium bundle.
Tool names overwhelmingly follow a consistent snake_case category-prefix pattern like weather_, crypto_, vehicle_, finance_, and geo_. Minor deviations such as bare names (domain, ip) and noun-verb forms (dns_lookup, url_check) are easy to learn and don't create real confusion.
50 tools is far beyond the typical well-scoped 3–15 range and will require heavy filtering to navigate. The broad multi-domain data marketplace partially justifies the size, but it would be more coherent split into per-domain servers or consolidated further.
For a read-only data/diligence marketplace, the surface is quite comprehensive: weather, vehicle, crypto, SEC/finance, domain/email, sanctions, and geo workflows all have core operations plus fused verdict bundles. Minor gaps exist—such as a simple crypto price lookup or vehicle market value—but agents can usually work around them.