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Glama

Adsb

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

A4.5/5.0
Behavior4/5

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

Annotations show idempotentHint=true, destructiveHint=false. Description adds persistence details (24hr for anonymous vs persistent for authenticated) and scoping by identifier, which annotations don't cover.

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?

Three sentences: purpose, usage, behavior. No wasted words. Front-loaded with action and examples.

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 key-value store, covers all needed aspects: usage, persistence, pairing with recall/forget. No output schema needed.

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 100% with good descriptions. Description reinforces key-value pattern and provides key naming conventions (e.g., 'subject_property'), adding value beyond schema.

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?

Description clearly states the tool saves data for later reuse across conversations/sessions, with concrete examples (resolved ticker, target address). Distinguishes from siblings like 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 Guidelines4/5

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

Explicitly tells when to use ('when you discover something worth carrying forward') and mentions companion tools (recall, forget). Lacks explicit negative cases but sufficient for context.

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

A3.6/5.0
Disambiguation2/5

Several clusters of tools have unclear boundaries: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, validate_claim, and bet_research all overlap as query/research entry points, and ai_visibility_check vs. scan_competitor_ai_presence plus the six polymarket_* tools create further confusion. An agent would often need deep description reading to pick the right tool.

Naming Consistency2/5

Naming mixes consistent verb_noun forms (get_aircraft, resolve_entity, validate_claim) with noun phrases (aircraft_near, military_aircraft, recent_alerts), adjective-led names (polymarket_arbitrage), and conversational names (ask_pipeworx, suggest_questions). Subgroups like polymarket_* are internally consistent, but overall there is no unified pattern.

Tool Count2/5

35 tools is high for any cohesive server, especially one named 'Adsb' where only 4 tools relate to aircraft tracking. The count is inflated by many overlapping meta-research, prediction-market, memory, and subscription tools, making it feel like several servers were merged into one.

Completeness2/5

For the implied 'Adsb' domain, only live ADSB positioning is covered; airport info, flight schedules, route search, and aviation weather are missing. Even as a general data/betting server, there are gaps like a direct stock-quote tool and unclear lifecycle coverage across the mixed feature set, so agents will likely hit dead ends.

Resources