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Glama

Mbta

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?

Beyond the annotations (read-only, idempotent), the description reveals important behavioral details: it fans out to two external sources, can have partial failures with graceful degradation, and bundlephobia's first measurement may take 5-30 seconds. 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 fairly concise for its complexity, using a single paragraph with natural separation of ideas. It front-loads the core value proposition and includes all necessary details without significant 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?

Given the lack of an output schema, the description thoroughly explains the return structure (summary block, per-advisory detail, links, alternatives). It also covers edge cases like partial failures and performance caveats, making it contextually complete.

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 descriptions for both parameters, so baseline is 3. The description adds value by clarifying that scoped packages are accepted for 'package' and that 'version' defaults to latest when omitted, plus the ecosystem restriction.

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 as a composite check for npm packages, covering safety, popularity, and size. It distinguishes itself by noting the NPM-only scope and referencing deps.dev for other ecosystems, making it unambiguous.

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?

The description explicitly tells when to use it: when an agent asks about a package's safety, popularity, or cost. It also provides alternatives by noting that for other package ecosystems, users should use deps.dev:version directly.

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

A3.9/5.0
Disambiguation2/5

ask_pipeworx_beta is explicitly stated to be currently identical to ask_pipeworx, which is a direct duplication. The polymarket cluster (bet_research, polymarket_edges, polymarket_arbitrage, polymarket_fill_risk, polymarket_edge_tracker, polymarket_kalshi_spread) has heavily overlapping purposes around finding and validating betting edges, and the ask_pipeworx / ask_pipeworx_grounded / deep_research / validate_claim tools all handle natural-language 'look up X' queries, making misselection likely without reading lengthy descriptions.

Naming Consistency4/5

snake_case is uniform and helpful prefixes (mbta_, polymarket_, pipeworx_, ask_pipeworx) create recognizable families. However, verb style is inconsistent — imperative verbs like ask/compare/discover/validate mix with noun-first names like bet_research, entity_profile, and search_within, and the memory trio (remember/recall/forget) doesn't share a common prefix.

Tool Count2/5

At 35 tools this exceeds the comfortable range, and the count is inflated by near-duplicates (ask_pipeworx_beta) and a dense 6-tool polymarket family. The server also mixes unrelated domains — only 4 of 35 tools are MBTA transit tools while the rest are Pipeworx data research, prediction markets, memory, and subscriptions — making it a kitchen sink rather than a well-scoped set.

Completeness4/5

The Pipeworx research surface is thorough: query, grounded verification, deep research, entity resolution, profiles, comparisons, change feeds, claim validation, and subscriptions are all covered with few dead ends. Minor gaps exist — the MBTA portion lacks schedule/line-detail tools beyond departures and alerts, and the AI-visibility feature feels bolted on without deeper integration.

Resources