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Glama

Ecosystems

by io.github.pipeworx-io

Get Package

get_package
Read-onlyIdempotent

Get a package's open metadata from ecosyste.ms — description, homepage, repository, license, latest version, monthly downloads, and dependent/usage counts. Works across 100+ registries. Keyless.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYesPackage name, e.g. "react", "@vue/cli", "django". Scopes/slashes are handled automatically.
registryNoRegistry name (default "npmjs.org"). e.g. "pypi.org", "crates.io", "rubygems.org", "nuget.org". Use list_registries to discover names.

Schema Changelog

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

  1. Changed1 schema field changed
    • addedInput schema / examples
      Added value: +[
+  {
+    "name": "lodash"
+  },
+  {
+    "name": "django",
+    "registry": "pypi.org"
+  }
+]
  2. First observed

TDQS

A4/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint. The description adds valuable context: 'Keyless' (no authentication needed) and 'Works across 100+ registries'. This goes beyond what annotations provide, but the bar is lower due to rich annotations. 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?

The description is two sentences, front-loaded with the main purpose, and includes essential scope and authentication info. Every word adds value, with no 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?

The description fully covers the tool's purpose, parameters, scope, and auth requirements. Although there is no output schema, the description lists the specific metadata fields returned, providing sufficient completeness for agent invocation.

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%, so the baseline is 3. The description adds useful commentary: scopes/slashes are handled automatically for 'name', and for 'registry' it suggests using 'list_registries'. This enhances understanding beyond the schema alone.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the verb 'Get' and resource 'package's open metadata' with a specific list of fields (description, homepage, repository, etc.). It is specific about what it retrieves. However, there is a sibling tool 'lookup_package' that may have similar functionality, and the description does not explicitly differentiate from it, slightly reducing clarity.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides broad usage context: works across 100+ registries and requires no key. However, it does not give explicit guidance on when to use this tool versus alternatives (e.g., 'lookup_package'), nor does it mention any prerequisites or limitations. Usage is implied but not fully explicit.

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

All Ecosystems toolsInspect
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TDQS

A3.8/5.0
Disambiguation3/5

Several tools form tight clusters that are easy to confuse: ask_pipeworx/ask_pipeworx_beta/ask_pipeworx_grounded are near-identical in routing, and the six polymarket_* tools (edges, arbitrage, edge_tracker, fill_risk, kalshi_spread, bet_research) all operate on prediction markets with overlapping names and purposes. While each has a distinct job, an agent will need to read descriptions very carefully to pick the right one, especially when many are similar.

Naming Consistency2/5

Naming conventions are mixed throughout the set: many tools use verb_noun (ask_pipeworx, compare_entities, discover_tools, resolve_entity, validate_claim), but there are also noun_noun (polymarket_edges, entity_profile, bet_research), adjective_noun (deep_research, recent_alerts), and bare verbs (forget, recall, remember, subscribe). No consistent pattern emerges, making tool names harder to predict and remember.

Tool Count2/5

At 34 tools, this server is heavy and tries to serve many unrelated domains—data research, prediction markets, package registries, memory, subscriptions, and AI visibility. The prediction-markets cluster alone accounts for six highly specialized tools that could be consolidated. The scope feels bloated rather than focused, which will overwhelm agents exploring the toolset.

Completeness4/5

Each domain within the server has solid coverage: data lookups offer stable, beta, grounded, and deep-research variants; entity analysis has profile, compare, changes, and resolve; subscriptions support create/list/delete/alert-read; memory has set/get/list/delete. Minor gaps exist (e.g., no direct package search by keyword, no tool to update a subscription), but the major workflows are covered and there are no dead ends.

Resources