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Glama

Climate Feeds

by io.github.pipeworx-io

Scan Dependency

scan_dependency
Read-onlyIdempotent

Composite "should I add this npm package to my project" check in ONE call — fans out across deps.dev (license + advisories + version history) and bundlephobia (gzipped/minified bundle size, dependency count, ESM/tree-shake support). Use whenever an agent asks "is X safe / popular / small" or "what does adding lodash cost me". Returns a summary block (is_latest, license, published_at, advisory_count, bundle_kb_min, bundle_kb_gz, dependency_count, has_esm, tree_shakeable), per-advisory detail, links, and a list of recent alternative versions. NPM ecosystem only in v1; PyPI / Maven / Cargo / Go fall under deps.dev:version directly. Partial failures degrade gracefully — bundlephobia's first measurement on a new version can take 5-30s; sources_failed will list it if it times out, the rest still returns.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
packageYesnpm package name. Scoped packages (e.g. "@types/node") are accepted.
versionNoSpecific version to check (e.g., "18.3.1"). Defaults to the latest published version when omitted.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A4.8/5.0
Behavior5/5

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

The description adds significant behavioral detail beyond annotations: partial failures degrade gracefully, bundlephobia's first measurement can take 5-30 seconds, and sources_failed lists timeouts. This enhances transparency despite annotations already providing readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured and front-loaded with the main purpose. Every sentence adds value, though it is slightly long. The information is efficiently presented without significant waste, earning a 4.

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?

Given the lack of an output schema, the description thoroughly explains the return value: a summary block with multiple fields, per-advisory detail, links, and recent alternative versions. It also covers partial failures, timeout behavior, and ecosystem scope, making it complete for the tool's complexity.

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% with clear descriptions for both parameters. The description adds context by stating that version defaults to latest when omitted, which is not in the schema. While it doesn't elaborate on parameter values or constraints beyond that, it provides enough additional meaning to justify a score above the baseline of 3.

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 the tool's purpose: a composite check for evaluating npm packages by combining deps.dev and bundlephobia data. It specifies the verb (scan), resource (dependency), and the composite nature, distinguishing it from sibling tools like deep_research or validate_claim.

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?

Explicit usage guidance is provided: 'Use whenever an agent asks "is X safe / popular / small" or "what does adding lodash cost me".' It also clarifies the scope ('NPM ecosystem only in v1') and directs to 'deps.dev:version directly' for other ecosystems, offering clear alternatives.

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

All Climate Feeds toolsInspect
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TDQS

A3.9/5.0
Disambiguation3/5

Most tools have clearly distinct purposes with thorough descriptions, but ask_pipeworx_beta is currently identical to ask_pipeworx, and the five polymarket_* tools plus three ask_pipeworx variants form overlapping clusters that could cause misselection without careful reading.

Naming Consistency3/5

Names fall into recognizable families (list_*, read_*, fetch_*, polymarket_*, ask_pipeworx_*), but conventions are mixed: bare verbs like remember/forget, noun phrases like entity_profile/recent_alerts, and product-prefixed names. Predictable within families but inconsistent across the full set.

Tool Count2/5

34 tools far exceeds the 25+ threshold and the typical well-scoped server. The bulk of tools—Pipeworx data access, Polymarket analysis, memory, subscriptions—go far beyond what a 'Climate Feeds' server name implies, making the set feel bloated and unfocused.

Completeness4/5

For its actual (much broader-than-named) scope, the surface is remarkably complete: query, grounded verification, research, comparison, entity resolution, memory, subscriptions, feedback, and discovery all exist. Minor gaps remain—subscriptions cannot monitor arbitrary RSS feeds, and there is no update operation for subscriptions.

Resources