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Glama

Todoist

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

A5/5.0
Behavior5/5

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

Beyond annotations (readOnly, idempotent), the description discloses potential 5-30s latency for bundlephobia's first measurement and graceful degradation via sources_failed. No contradictions.

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?

Packed with information in a single paragraph, front-loaded with main purpose, every sentence adds value without 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?

Covers composite nature, data sources, return structure (summary block, advisories, links, versions), ecosystem limitation, and partial failure behavior. No output schema needed as description explains return comprehensively.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, and the description adds value: 'Scoped packages accepted' for package, and 'Defaults to latest published version when omitted' for version, enhancing understanding 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?

The description clearly states the composite check purpose, lists data sources (deps.dev, bundlephobia), and specifies the returned information types. It distinguishes from siblings by noting NPM-only scope and referencing alternative tools.

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?

Explicitly tells when to use (agent asks about safety, popularity, size) and ecosystem scope (NPM only) with clear alternatives for other ecosystems.

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

All Todoist toolsInspect
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TDQS

A3.8/5.0
Disambiguation3/5

Tools have overlapping purposes, e.g., 'ask_pipeworx' and 'validate_claim' both handle factual queries; 'bet_research' and 'polymarket_edges' both analyze betting markets. However, most tools target distinct domains (Todoist, data lookup, betting), so some separation exists.

Naming Consistency3/5

Naming uses snake_case and often follows verb_noun, but prefixes are inconsistent: some tools have 'todoist_', 'pipeworx_', 'polymarket_', while others like 'compare_entities' and 'discover_tools' lack prefixes. This mix reduces predictability.

Tool Count2/5

25 tools is high, and only 5 are Todoist-specific despite the server name. The inclusion of many unrelated tools (Pipeworx, betting, etc.) makes the set feel bloated and misaligned with the server's stated focus.

Completeness2/5

For Todoist, the set lacks create/update/delete operations, leaving gaps. For the broader data domains, coverage is extensive but unfocused. The server's purpose is unclear, leading to an incomplete surface for any single use case.

Resources