Skip to main content
Glama

US Economic, SEC EDGAR & On-Chain Data (x402)

by net.agentfund

SEC Full-Text Filing Search

edgar_full_text_search
Read-onlyIdempotent

Full-text search across all SEC EDGAR filings since 2001 for a keyword or phrase.

Wraps EDGAR's own full-text search index, so it covers every filer and form type, not just a single company. Useful for finding who is disclosing a particular risk, technology, litigation, or event across the entire market.

When to use: cross-company research ("who is disclosing AI-related risk factors"), finding filings that mention a specific term, litigation or regulatory tracking.

When NOT to use: you already know the company (use edgar_filings_feed, which is company-scoped and cheaper), or you need results from before 2001 (EDGAR full-text search does not cover that far back).

Args:

  • query (string, required): search text. Wrap an exact phrase in double quotes, e.g. ""material weakness"".

  • forms (string[], optional): restrict to form types, e.g. ["10-K"].

  • dateFrom (string, optional): ISO start date (YYYY-MM-DD).

  • dateTo (string, optional): ISO end date (YYYY-MM-DD).

  • limit (integer, optional, default 10): maximum hits to return (1-50).

Returns structuredContent: { "query": "material weakness", "totalMatches": 10000, "totalIsApproximate": true, "count": 2, "hits": [ { "id": "0001193125-26-123456:doc.htm", "entity": "Example Corp.", "form": "10-K", "filedAt": "2026-03-01", "cik": "0000320193" } ], "source": "https://www.sec.gov/edgar" }

"totalMatches" is a lower bound and "totalIsApproximate" is true once EDGAR's own count exceeds its display cap (10,000) — narrow with forms/dateFrom/dateTo for a precise count.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
formsNoRestrict to form types, e.g. ["10-K"].
limitNoMax hits to return. Default 10.
queryYesSearch text. Quote an exact phrase, e.g. "material weakness".
dateToNoISO end date (YYYY-MM-DD).
dateFromNoISO start date (YYYY-MM-DD).

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A4.9/5.0
Behavior5/5

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

Annotations already declare read-only, idempotent, and non-destructive behavior, and the description adds substantial context: it wraps EDGAR's own index, covers filings since 2001, and explains that totalMatches is a lower bound with totalIsApproximate true once EDGAR's count exceeds 10,000. This goes well beyond the structured annotations.

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?

The description is effectively structured with a clear definition, usage sections, parameter list, and return example. Every sentence is informative; the included structuredContent example is useful and not redundant. It is detailed yet remains focused.

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

Completeness5/5

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

Even without an output schema, the description fully specifies the return shape with a concrete example, explains the approximate total behavior, and includes the source URL. It covers coverage limits, parameter usage, and result interpretation, making it self-sufficient for agent invocation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so baseline is 3. The description adds practical usage details such as quoting exact phrases, limiting to form types with examples, and specifying the 1–50 range for limit. While some repetition exists, the consolidated Args section and range clarification provide marginal added value.

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 opens with a specific verb and scope: "Full-text search across all SEC EDGAR filings since 2001 for a keyword or phrase." It clearly distinguishes this tool from siblings by noting it covers every filer and form type, and explicitly contrasts with edgar_filings_feed.

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?

Dedicated 'When to use' and 'When NOT to use' sections provide explicit guidance. It names the alternative tool (edgar_filings_feed) and gives concrete scenarios like 'cross-company research' and 'litigation or regulatory tracking', plus a specific exclusion (pre-2001 results).

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All US Economic, SEC EDGAR & On-Chain Data (x402) toolsInspect
Try in Browser
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

A4.5/5.0
Disambiguation5/5

Every tool targets a distinct resource and action. The macro_* tools each cover one economic indicator, the edgar_* tools cover different SEC filing types, and the onchain_* tools are split by chain scope (single vs multi), asset type, and operation. Even the two data-cleaning tools are clearly distinct (JSON repair vs table parsing). No two tools appear to do the same thing.

Naming Consistency4/5

Names follow a mostly consistent snake_case pattern with domain prefixes: macro_*, edgar_*, onchain_*. The exceptions are bls_cpi (could be macro_cpi) and the utility tools structured_json_repair and tabular_to_json, which break the prefix pattern but are still descriptive and predictable. Overall, the convention is clear with minor deviations.

Tool Count3/5

21 tools is in the 'heavy' range (16-25). However, the server spans three distinct domains (US economic data, SEC EDGAR, on-chain data), and each tool serves a unique purpose within its domain. While it feels dense, the breadth is justified by the server's stated multi-domain scope.

Completeness4/5

The tool surface covers the major needs in each domain: key macro indicators, common EDGAR filings and searches, and core on-chain reads. Minor gaps exist (e.g., no PPI, no historical on-chain balances, no company CIK lookup), but agents can work around these with existing tools or by combining them.

Resources