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Glama

Microsoft Todo

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

A4.6/5.0
Behavior5/5

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

Beyond annotations (readOnly, idempotent), the description discloses that bundlephobia's first measurement can take 5-30s, partial failures degrade gracefully, and sources_failed will list timeouts. No contradictions 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.

Conciseness4/5

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

The description is dense and front-loaded with the main purpose, but it is slightly long. However, every sentence provides useful information, and there is no redundancy. Could be slightly more structured but efficient.

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 complexity (composite tool, two services, partial failures, timeout behavior), the description thoroughly covers what the tool does, what it returns, its limitations (npm only), and edge cases. No output schema exists, so the description adequately explains return values.

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 already covers both parameters (100% coverage) with descriptions. The tool description adds marginal value by mentioning scoped packages and default version behavior, but does not significantly enhance understanding beyond the 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 tool's composite nature, combining deps.dev and bundlephobia to answer 'should I add this npm package?' It distinguishes itself from siblings by being npm-specific and aggregating multiple concerns.

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 says when to use it ('is X safe / popular / small') and when not to (other ecosystems, pointing to deps.dev:version directly). Provides clear guidance on 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

B3.4/5.0
Disambiguation1/5

The set is dominated by overlapping Pipeworx and prediction-market tools, with ask_pipeworx and ask_pipeworx_beta explicitly described as currently identical, and ask_pipeworx_grounded, deep_research, and discover_tools serving heavily overlapping lookup purposes. The four Microsoft To Do tools are buried among 31 unrelated tools, making it very hard for an agent to select the right tool for the server's apparent domain.

Naming Consistency2/5

Most names are snake_case, but the naming conventions are otherwise mixed: there are verb_noun tools like list_tasks and get_task, bare verbs like remember/forget/recall, prefixed families like polymarket_*, and noun-phrase names like entity_profile, recent_changes, and pipeworx_trending. The lack of a consistent pattern across the set, especially relative to the Microsoft To Do server name, makes naming unpredictable.

Tool Count1/5

35 tools is already heavy, but the bigger problem is that only 4 of them are for Microsoft To Do, the server's stated name and purpose. The remaining 31 tools belong to unrelated Pipeworx data, prediction-market, and memory-management domains, which is an extreme scope mismatch.

Completeness1/5

For a Microsoft To Do server, the surface is severely incomplete: list_task_lists, list_tasks, get_task, and find_due_tasks cover reading and browsing only, with no create, update, complete, or delete operations. Even the broader Pipeworx functionality is scattered and redundant rather than forming a coherent domain surface.

Resources