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Opensensemap

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
Behavior5/5

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

Annotations already note idempotent and non-destructive; the description adds rich context: key-value pair scoping by identifier, persistent vs. 24-hour retention based on auth, and pairing with recall/forget. No contradiction 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?

Three concise sentences, front-loaded with purpose, then usage guidance, then storage details. Every sentence earns its place with no redundancy.

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 with no output schema and full parameter coverage, the description fully addresses scope, durability, and companion operations. The agent has enough context to use the tool correctly and decide when to invoke it.

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 detailed descriptions for both key and value. The description reinforces the key-value concept and gives examples, but adds no new syntax or formatting details beyond what the schema already provides.

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 opens with a specific verb and resource ('Save data the agent will need to reuse later'), clearly distinguishing it from recall (retrieve) and forget (delete). The scope ('across this conversation or across sessions') further clarifies its role.

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 states when to use: 'Use when you discover something worth carrying forward' with concrete examples (ticker, address, preference). Does not explicitly state when not to use, but the companion tools (recall, forget) are referenced, giving clear complementary 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

A3.7/5.0
Disambiguation2/5

Several tools have unclear boundaries: ask_pipeworx and ask_pipeworx_beta are explicitly identical in behavior today, while discover_tools and suggest_questions overlap as discovery/onboarding entry points. The Polymarket tools also blur together across arbitrage, edges, edge tracking, and fill risk, making tool selection prone to mistakes despite long descriptions.

Naming Consistency3/5

Names are consistently lowercase snake_case and readable, but they mix conventions: some are clear verb_noun actions like validate_claim and compare_entities, while others are noun-led like recent_alerts and entity_profile, or domain-prefixed like polymarket_edges and opensensemap_nearby. There is no single predictable naming pattern across the set.

Tool Count2/5

At 34 tools, this exceeds the 25+ threshold for a heavy tool set, and the tools span several unrelated domains: OpenSenseMap sensors, Pipeworx data access, Polymarket research, memory, subscriptions, and AI visibility. A server named Opensensemap hosting this much unrelated functionality feels poorly scoped.

Completeness3/5

The OpenSenseMap portion covers nearby discovery, box lookup, and area averages, but lacks historical/time-series access and station lifecycle operations, which are notable gaps for a sensor data server. The broader Pipeworx surface has strong lookup, research, validation, and subscription coverage, so the main incompleteness is in the named domain.

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