Skip to main content
Glama

Meteostat

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

A4.7/5.0
Behavior5/5

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

Adds context beyond annotations: explains persistence duration (24h for anonymous, persistent for authenticated), scoping by identifier, and that it's a write operation (consistent with readOnlyHint=false). No 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?

Four sentences, front-loaded with purpose, no wasted words. Efficient and clear.

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 tool, the description covers lifespan, scoping, and complementary tools. No output schema needed, so completeness is high.

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 is 100%, so baseline is 3. The description provides concrete examples for keys (e.g., 'subject_property') and values, adding meaning beyond schema descriptions.

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 states 'Save data the agent will need to reuse later' with a specific verb and resource, and distinguishes itself from sibling tools 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?

Provides explicit when-to-use guidance ('when you discover something worth carrying forward') and mentions pairing with recall/forget, but does not explicitly state when not to use.

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

All Meteostat toolsInspect
Try in Browser
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

A3.6/5.0
Disambiguation2/5

The set contains multiple overlapping families: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, and validate_claim all answer natural-language data questions, while five polymarket tools and ai_visibility_check/scan_competitor_ai_presence further blur boundaries. The lengthy descriptions help, but an agent will still face genuinely ambiguous selection decisions across these clusters.

Naming Consistency2/5

Tool names mix verb-first patterns (find_stations, validate_claim), domain-first names (polymarket_edges, entity_profile), and brand-prefixed meta tools (pipeworx_trending, pipeworx_feedback). Snake_case is consistent, but there is no predictable verb_noun convention across the set, making the overall naming scheme feel more like a platform catalog than a coherent API.

Tool Count2/5

At 34 tools, this is too many for a well-scoped server, and most of the surface (prediction markets, AI visibility, npm scanning, llms.txt generation) is unrelated to the server's stated Meteostat identity. The count is driven by broad meta wrappers and overlapping data-access aggregates rather than a focused domain model.

Completeness2/5

For a server named Meteostat, the weather surface is notably incomplete: find_stations plus get_daily_history and get_monthly_normals covers stations, daily records, and normals, but there is no current-conditions, forecast, or hourly-history tool even though hourly availability is mentioned in station inventories. The rest of the set is a broad data-research layer, but it does not form a complete lifecycle for any single resource.

Resources