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Slacking.biz — SEC Financial Data + US Economics + Demographics + FX

by io.github.Th3Slack3r

health_drug_lookup

Look up an FDA drug label by NDC or brand/generic name — active ingredients, purpose, indications, warnings, dosage, manufacturer. Source: openFDA (U.S. FDA), public data. Attribution: Data provided by the U.S. Food and Drug Administration (openFDA).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
typeNoInterpretation override: auto (default — NDC if numeric, else name), ndc, name
identifierYesNDC code (e.g. '0113-0611') or brand/generic name (e.g. 'Aspirin', 'Ibuprofen')

Schema Changelog

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

  1. Added

TDQS

A3.6/5.0
Behavior3/5

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

No annotations are provided, so the description carries the full burden. It adds valuable context by citing the openFDA data source and public nature, but it does not mention rate limits, pagination, multiple-match behavior, or exact NDC formatting expectations. The read-only nature is implied but not explicitly stated.

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 concise and front-loaded with the action and resource. The source and attribution are stated in a second sentence without redundancy or fluff.

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?

For a simple lookup tool, the description provides a clear list of returned data fields, source, and attribution, which is adequate. However, it does not mention whether ambiguous names return multiple results or any output format, which would be useful given no output schema.

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?

The input schema covers both parameters with clear descriptions, so baseline is 3. The description reinforces that the identifier can be an NDC or name but adds no additional syntax, type nuances, or examples beyond what the schema provides.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states it looks up an FDA drug label by NDC or brand/generic name, and lists the specific data fields returned. It distinguishes from sibling search tools by emphasizing exact identifier lookup, though it does not explicitly name alternatives.

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

Usage Guidelines3/5

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

The implied usage is for retrieving a drug label using an exact NDC or name, but there is no explicit guidance on when to use this tool versus alternatives like health_drug_search or health_drug_ndc. The description gives context but no direct exclusions or comparisons.

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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TDQS

C2.9/5.0
Disambiguation2/5

Several tool groups have overlapping purposes: fda_drug_labels vs health_drug_search, fda_recalls vs health_recalls, fx_official_rates vs treasury_fx_rates, treasury_debt vs us_debt_current, and get_gdp vs get_bea_gdp. These near-duplicates create real ambiguity for an agent deciding which tool to call.

Naming Consistency2/5

Names mix verb-led styles (get_, search_, compare_, screen_) with domain-led styles (fx_, treasury_, uk_, health_, eurostat_, datausa_). Within the same domain, similar actions use different patterns (get_gdp vs eurostat_gdp vs imf_indicator), making the set feel inconsistent and hard to predict.

Tool Count1/5

75 tools is extreme for any MCP server, especially when many tools are redundant or cover unrelated domains (weather, earthquakes, scholarly search, air quality) outside the stated SEC/economics/demographics/FX focus. This overwhelms agents and burdens context windows.

Completeness3/5

Core domains like SEC financials, major economic indicators, basic demographics, and current FX rates are well covered. However, gaps remain: no historical FX rates, no stock price/quote tool, limited demographic breakdowns, and no ability to fetch full SEC filing text. Some operations end in dead ends.

Resources