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Glama

airquality

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. Added

TDQS

A4.5/5.0
Behavior4/5

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

The description adds valuable behavioral context beyond the annotations: key-value scoping by user identifier, persistent memory for authenticated users vs. 24-hour retention for anonymous sessions. This complements the idempotentHint and readOnlyHint annotations without contradiction.

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 three sentences that efficiently cover the main purpose, usage context, and related tools. Every sentence earns its place with no redundancy or filler.

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?

For a simple two-parameter tool with strong annotations, the description fully covers purpose, when to use, persistence behavior, and sibling relationships. No output schema is needed, and the description is complete enough for an agent to invoke confidently.

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?

Schema coverage is 100% with clear parameter descriptions ('Memory key', 'Value to store'). The description reinforces the key-value nature but adds no new syntax or format details beyond the schema, so baseline 3 is appropriate.

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 uses a specific verb ('Save data') with a clear resource ('data the agent will need to reuse later') and scope ('across this conversation or across sessions'). It also distinguishes the tool from siblings by naming its counterparts recall and forget.

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 explicitly states when to use the tool ('Use when you discover something worth carrying forward') and provides alternatives ('Pair with recall to retrieve later, forget to delete'). This gives clear contextual guidance for tool selection.

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

All airquality toolsInspect
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TDQS

A3.5/5.0
Disambiguation3/5

Many tools have overlapping purposes, especially multiple ask_pipeworx variants and numerous Polymarket tools. Detailed descriptions help, but an agent could still confuse similar tools like ask_pipeworx and ask_pipeworx_beta.

Naming Consistency2/5

Tool names use a mix of styles: some are descriptive (get_air_quality), some use proprietary prefixes (pipeworx_feedback), and others are arbitrary (bet_research). No consistent verb_noun pattern.

Tool Count1/5

33 tools is excessive for a server named 'airquality', which only has 2 air-quality-specific tools. The count is appropriate for a general data platform, but mismatched with the server name.

Completeness1/5

For the air quality domain, the tool set is severely incomplete, missing historical data, pollution sources, and health recommendations. The overall set covers many other domains, but fails to address the server's apparent focus.

Resources