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Glama

Fda Regulations

by io.github.pipeworx-io

Recent Changes

recent_changes
Read-onlyIdempotent

"What's new with X" / "latest on Y" / "what happened to Z this week / month / quarter" / "updates on Acme" / "news on Tesla recently" / "what's happening with Apple" — change feed for a company in the last N days/weeks/months in ONE parallel call. Fans out to SEC EDGAR (filings since since), GDELT→GNews fallback (news mentions in window — GDELT preferred, GNews when rate-limited or 5xx), USPTO (patents granted; PatentsView API sunset May 2025 so this soft-fails until reactivated). since accepts ISO date ("2026-04-01") or relative shorthand ("7d", "30d", "3m", "1y"). Returns structured changes[] grouped by source + total_changes count + pipeworx:// citation URIs. Use entity_profile instead when you want the static profile (filings + fundamentals + LEI + patents) regardless of window.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
typeYesEntity type. Only "company" supported today.
sinceYesWindow start — ISO date ("2026-04-01") or relative ("7d", "30d", "3m", "1y"). Use "30d" or "1m" for typical monitoring.
valueYesTicker (e.g., "AAPL") or zero-padded CIK (e.g., "0000320193").

Schema Changelog

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

  1. First observed

TDQS

A4.7/5.0
Behavior5/5

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

Annotations already convey safe, read-only, idempotent behavior. The description adds critical details: fan-out to multiple sources with fallback (GDELT→GNews), soft-fail for patents, and parallelism ('ONE parallel call'), all consistent with annotations.

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 front-loaded with relatable examples, then covers sources, parameters, and an alternative tool. It is moderately concise; every sentence adds value, though slightly long. No redundancy.

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?

Given 3 required parameters and no output schema, the description adequately explains the return structure ('changes[] grouped by source + total_changes count + citation URIs'). The fan-out and fallback behavior are fully described, leaving minimal ambiguity.

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%, baseline 3. The description adds meaningful context: explains `since` format (ISO date or relative shorthand like '7d'), provides a usage recommendation ('Use '30d' or '1m' for typical monitoring'), and clarifies `value` accepts ticker or CIK.

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 it provides a 'change feed for a company in the last N days/weeks/months' and lists specific sources (SEC EDGAR, GDELT→GNews, USPTO). It explicitly distinguishes from the sibling tool 'entity_profile' by noting the static profile alternative.

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 offers explicit when-to-use guidance with example queries like 'What's new with X' and 'latest on Y'. It provides a clear exclusion: 'Use entity_profile instead when you want the static profile... regardless of window.'

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

A3.5/5.0
Disambiguation1/5

The tool set contains near-duplicate query tools (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded) and many overlapping accessors (deep_research, validate_claim, fda_search, fda_regulation). The server name 'Fda Regulations' is also misleading because the vast majority of tools (e.g., polymarket_*, generate_llms_txt, remember) have nothing to do with FDA regulations, making correct selection extremely difficult.

Naming Consistency2/5

Most names use snake_case, but the verb/noun pattern is inconsistent: some are verb-first (ask_pipeworx, generate_llms_txt, validate_claim), some are noun-first (entity_profile, recent_changes, pipeworx_trending), and the ask_pipeworx_beta/grounded variants break the convention. Some names are also semantically misleading (scan_dependency checks an npm package rather than scanning a dependency).

Tool Count1/5

With 33 tools, this is far too many for a server nominally about FDA regulations; only two tools directly address that domain. Even as a general-purpose data platform, 33 tools is excessive and includes many unrelated utilities (e.g., generate_llms_txt, scan_dependency), making the server's scope unclear and bloated.

Completeness2/5

For the stated FDA regulations purpose, only fda_regulation (get by citation) and fda_search (keyword search) exist, providing basic read coverage but no access to FDA data (drug labels, adverse events, recalls), guidance documents, or regulatory history. The many unrelated tools do not fill these gaps, so the surface is severely incomplete for its apparent purpose.

Resources