list_registriesList the package registries ecosyste.ms supports (npm, PyPI, crates.io, Go, NuGet, Maven, RubyGems, Packagist, Docker Hub, and 100+ more) with their ecosystem and package counts. Keyless.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / examplesAdded value: +[
+ {}
+]Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate read-only and idempotent behavior. The description adds useful context beyond annotations by specifying 'Keyless' access and the output includes ecosystem and package counts, though it doesn't detail output structure.
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?
Two concise sentences with no redundancy: first sentence explains the action and examples, second adds a key access note. Every part earns its place.
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?
For a parameterless list tool, the description covers the purpose, output content, and access requirement. It lacks details on ordering or pagination but is sufficient for simple use.
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?
The tool has zero parameters and schema coverage is 100%. The description adds value by indicating what the response contains (ecosystem and package counts), compensating for the lack of output schema.
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 lists package registries with their ecosystem and package counts, providing specific examples (npm, PyPI, etc.) and distinguishing it from sibling tools like get_package or lookup_package.
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 a simple list operation and mentions 'Keyless' (no authentication), but does not explicitly state when to use this tool versus alternatives or provide exclusions.
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.
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 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.
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.
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.