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Glama

Open Meteo

by io.github.pipeworx-io

Remember

remember
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

A4.2/5.0
Behavior4/5

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

Discloses scoping by identifier and retention policy. IdempotentHint=true is annotated; description aligns by describing overwrite-on-same-key behavior implicitly. Adds value beyond 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?

Efficient and front-loaded. Every sentence adds value, no redundancy. Clear structure: purpose, usage, specifics, partner tools.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Complete for a key-value store tool. Schema fully covers params, annotations present. Description adds scope and retention. Slight gap on size limits, but not critical.

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%, and the description restates the parameter meanings without adding significant new detail beyond the schema examples.

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?

Clearly states the tool saves data for reuse later. Specifies key-value storage, scoped by identifier. Distinguishes from siblings by mentioning pairing with 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?

Provides clear usage scenarios with examples (resolved ticker, user preference). Notes persistence duration for authenticated vs anonymous users. Lacks explicit when-not-to-use 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.

All Open Meteo toolsInspect
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TDQS

A3.8/5.0
Disambiguation3/5

Many tools have overlapping purposes, such as ask_pipeworx vs ask_pipeworx_grounded and the multiple Polymarket tools (polymarket_edges, polymarket_arbitrage, bet_research). While some tools are clearly distinct (e.g., geocode vs forecast), the high number of similar tools increases the risk of agent misselection.

Naming Consistency2/5

Naming conventions are inconsistent. Some tools use a consistent verb_noun pattern (e.g., ask_pipeworx, resolve_entity), while others have no prefix (remember, recall) or use a domain prefix (polymarket_arbitrage, pipeworx_feedback). The mix of styles and lack of a unified pattern reduces predictability.

Tool Count2/5

With 33 tools, the set is too large for a focused server, especially given the name 'Open Meteo' which implies weather tools only. Many tools are redundant or cover vastly different domains, making the count feel bloated and difficult for an agent to navigate efficiently.

Completeness4/5

The tool set covers an impressively wide range of capabilities: weather data, SEC filings, Polymarket analysis, memory management, and more. For the actual scope of data querying and analysis, there are few obvious gaps (e.g., no direct database query tool beyond ask_pipeworx). However, the completeness relative to the implied weather domain is poor.

Resources