Skip to main content
Glama

download_dart_report

Download and parse a single DART filing, caching results as JSON. Use when keyword_search or get_report fails with 'file not found'.

Instructions

Download and parse a single DART filing. Combines the SEC flow's download_sec_report + parse_sec_report into one step.

Args: rcept_no: 14-digit DART receipt number (from list_dart_filings) stock_code: optional 6-digit stock code for the JSON filename ({stock_code}_{rcept_no}.json). When omitted, resolves from an existing cache or uses {rcept_no}.json. rcept_dt: optional receipt date YYYYMMDD (informational) report_type: DART detail type code, default "A001". Any valid type from types.yaml is accepted; the parser auto-detects the document format. force_parse: re-parse even if a cached JSON exists

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
rcept_dtNo
rcept_noYes
stock_codeNo
force_parseNo
report_typeNoA001

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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

With no annotations, the description fully carries the transparency burden. It discloses caching behavior, auto-detection of document format, and parsing routes for different report types. It lacks explicit mention of side effects like network usage, but covers core behavioral traits.

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?

The description is longer but well-structured with strategy and critical rules in XML tags. Every sentence adds value, and the purpose is front-loaded. Minor room for cutting verbosity without losing meaning.

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?

Given the tool's complexity (5 parameters, multiple report types, caching), the description covers workflow, error handling, auto-detection, parameter usage, and sibling relationships. The presence of an output schema complements the description.

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

Parameters5/5

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

Schema coverage is 0%, but the description provides rich parameter details: rcept_no's format and source, stock_code's role in file naming, rcept_dt's informational nature, report_type's default and flexibility, and force_parse's meaning. This adds significant value over the bare 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?

The description clearly states the tool's action ('Download and parse a single DART filing') and distinguishes it from siblings by noting it combines two SEC flow steps. This provides specificity and uniqueness.

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?

The description includes an explicit strategy that tells the agent when to invoke this tool (only on 'file not found' errors from other tools) and critical rules that prevent misuse (never assume download before search). This provides thorough usage guidance.

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

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/agentladle/mcp-dart'

If you have feedback or need assistance with the MCP directory API, please join our Discord server