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Glama

Fda Regulations

by io.github.pipeworx-io

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Idempotent

Save data the agent will need to reuse later — across this conversation or across sessions. Use when you discover something worth carrying forward (a resolved ticker, a target address, a user preference, a research subject) so you don't have to look it up again. Stored as a key-value pair scoped by your identifier. Authenticated users get persistent memory; anonymous sessions retain memory for 24 hours. Pair with recall to retrieve later, forget to delete.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
keyYesMemory key (e.g., "subject_property", "target_ticker", "user_preference")
valueYesValue to store (any text — findings, addresses, preferences, notes)

Schema Changelog

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

  1. First observed

TDQS

A4.7/5.0
Behavior4/5

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

The description discloses behavioral traits beyond annotations, such as session-specific memory duration ('authenticated users get persistent memory; anonymous sessions retain memory for 24 hours'). It aligns with annotations (idempotentHint: true, destructiveHint: false) and adds contextual value about storage limits.

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 a single, well-structured paragraph of about 100 words. It front-loads the primary purpose, then flows logically into usage guidance, scoping, and pairing with other tools. Every sentence serves a purpose without extraneous detail.

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 simplicity and the lack of an output schema, the description covers all essential aspects: purpose, when to use, storage mechanics, persistence, and relationship with sibling tools. No critical gaps are present.

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?

With 100% schema description coverage, the baseline is 3. The description adds value by providing example keys ('subject_property', 'target_ticker', 'user_preference') and clarifying that the value can be 'any text,' which reinforces the schema details without redundancy.

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 identifies the tool as a persistent data storage mechanism, using phrases like 'save data the agent will need to reuse later' and 'across this conversation or across sessions.' It distinguishes itself from sibling tools by name (recall, forget), making the purpose unambiguous.

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 provides explicit guidance on when to use the tool: 'when you discover something worth carrying forward... so you don't have to look it up again.' It also details scoping ('scoped by your identifier') and persistence behavior for authenticated vs. anonymous sessions, leaving no ambiguity.

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