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Glama

Fda Regulations

by io.github.pipeworx-io

Discover Tools

discover_tools
Read-onlyIdempotent

Find tools by describing the data or task. Use when you need to browse, search, look up, or discover what tools exist for: SEC filings, financials, revenue, profit, FDA drugs, adverse events, FRED economic data, Census demographics, BLS jobs/unemployment/inflation, ATTOM real estate, ClinicalTrials, USPTO patents, weather, news, crypto, stocks. Returns the top-N most relevant tools with names, descriptions, and full input schemas (with curated examples) — each result is ready to call directly, no second schema lookup needed. Call this FIRST when you have many tools available and want to see the option set (not just one answer).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
qNoAlias for query.
taskNoAlias for query.
limitNoMaximum number of tools to return (default 20, max 50)
queryYesNatural language description of what you want to do (e.g., "analyze housing market trends", "look up FDA drug approvals", "find trade data between countries"). Accepts task, q, description, search as aliases.
searchNoAlias for query.
descriptionNoAlias for query.

Schema Changelog

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

  1. First observed

TDQS

A4.5/5.0
Behavior4/5

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

Annotations already indicate read-only, idempotent, non-destructive behavior. The description adds value by specifying that results include 'full input schemas (with curated examples)' and are 'ready to call directly, no second schema lookup needed.' No contradictions 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.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences with no filler. The first sentence states the core function and scope, the second provides return format and usage guidance. Every sentence adds value.

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?

Despite lack of output schema, the description explains what is returned (names, descriptions, schemas, examples) and how to use the results. It covers the tool's purpose, usage guidance, return format, and scope completely.

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% with descriptions for all 6 parameters. The description supplements by explaining query aliases and providing natural language examples, adding meaning beyond the schema's basic parameter descriptions.

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 purpose: 'Find tools by describing the data or task.' It lists specific domains (SEC filings, FDA drugs, etc.) and distinguishes itself from siblings by calling itself the first tool to use for discovery, unlike more targeted tools.

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

Usage Guidelines4/5

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

The description explicitly advises 'Call this FIRST when you have many tools available and want to see the option set (not just one answer).' This provides clear when-to-use guidance, but it does not explicitly contrast with sibling tools like direct search or entity lookup tools.

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

All Fda Regulations toolsInspect
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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