packageFetch deps.dev metadata for an open-source package by ecosystem (NPM/PYPI/GO/MAVEN/NUGET/CARGO/RUBYGEMS) and name, returning version list, latest version, and linked source repository.
| Name | Required | Description | Default |
|---|---|---|---|
| name | Yes | ||
| system_ | Yes | NPM | PYPI | GO | MAVEN | NUGET | CARGO | RUBYGEMS |
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / examplesAdded value: +[
+ {
+ "name": "lodash",
+ "system_": "NPM"
+ },
+ {
+ "name": "requests",
+ "system_": "PYPI"
+ }
+]Output schema / (root)Previous value: -nullNew value: +{
+ "description": "Package metadata from deps.dev API",
+ "properties": {},
+ "type": "object"
+}Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare the tool read-only, idempotent, and non-destructive. The description adds that it fetches metadata and the type of return data, but does not disclose additional behavioral aspects like error handling on invalid package names or rate limits. Given the strong annotation coverage, this is adequate but not rich.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence that is front-loaded with the core action ('Fetch deps.dev metadata'), enumerates the ecosystem values, and lists the return values. Every phrase adds value with no wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool has only two parameters and an output schema, so the description covers the essential aspects: what it does, how to call it, and what to expect. It omits edge-case details like invalid ecosystem handling, but for a simple read-only lookup, this is sufficiently complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is only 50% because 'name' lacks a description. The description compensates by explicitly explaining that 'name' is an open-source package name and 'system_' is one of the supported ecosystems, listing all allowed values. This adds meaning beyond the schema, especially for the undocumented 'name' parameter.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool fetches deps.dev metadata for an open-source package by ecosystem and name, and specifies the exact return values (version list, latest version, linked source repository). This distinguishes it from sibling tools like 'dependencies' or 'scan_dependency' by focusing on package metadata rather than dependency relationships.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies its usage context: when you need package metadata from deps.dev. However, it does not explicitly mention alternatives or when not to use it, such as 'use dependencies instead for dependency analysis'. This is clear context without exclusions, so a 4 is appropriate.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Multiple tools overlap heavily: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded are nearly identical routers, the six polymarket tools cover similar prediction-market territory with fuzzy boundaries, and the deps.dev tools (package/version/dependencies/query/scan_dependency) all deliver dependency metadata. An agent would frequently need the lengthy descriptions to pick the right one.
Tool names mix several conventions: noun-only names (package, version, query, project, dependencies), verb_noun names (scan_dependency, validate_claim, resolve_entity), and family-prefixed names (ask_pipeworx_*, polymarket_*, pipeworx_*). There is no single consistent pattern across the set.
With 36 tools, the server is well past the 'heavy' threshold. The scope is also sprawling: general data querying, dependency lookup, memory management, subscriptions, prediction markets, claim verification, and AI-visibility scanning. Many tools could be consolidated or moved to separate servers.
The tool set is individually broad and covers many workflows, but the server's stated identity ('Deps Dev') does not match the dominant Pipeworx data surface, creating an unclear core purpose. Within the dependency sub-domain it is fairly complete, and the data-research workflows have decent coverage, but gaps like subscription updates and true deps.dev ecosystem coverage suggest the surface is improvised.