maintainerMaintainer summary by canonical maintainer id (often email).
| Name | Required | Description | Default |
|---|---|---|---|
| maintainer | Yes |
| Name | Required | Description | Default |
|---|---|---|---|
| name | No | Maintainer name or email | |
| projects | No | List of projects maintained by this maintainer | |
| packages_count | No | Total number of packages maintained |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / examplesAdded value: +[
+ {
+ "maintainer": "user@example.com"
+ },
+ {
+ "maintainer": "john.doe@linux.org"
+ }
+]Output schema / (root)Previous value: -nullNew value: +{
+ "description": "Maintainer summary and their projects",
+ "properties": {
+ "name": {
+ "description": "Maintainer name or email",
+ "type": "string"
+ },
+ "packages_count": {
+ "description": "Total number of packages maintained",
+ "type": "number"
+ },
+ "projects": {
+ "description": "List of projects maintained by this maintainer",
+ "items": {
+ "properties": {
+ "name": {
+ "description": "Project name",
+ "type": "string"
+ },
+ "repos": {
+ "description": "List of repositories where maintained",
+ "items": {
+ "type": "string"
+ },
+ "type": "array"
+ }
+ },
+ "type": "object"
+ },
+ "type": "array"
+ }
+ },
+ "type": "object"
+}Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and non-destructive behavior, so the description does not need to restate these. It adds the detail that the identifier is canonical and often an email, but it omits any additional behavioral traits like output format, pagination, or potential error conditions. 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 with no wasted words. It front-loads the tool's purpose and immediately specifies the input key and format, making it easy to parse and act upon.
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 simple one-parameter lookup tool with a rich output schema and clear annotations, the description is mostly sufficient. It tells the agent what input to provide and what kind of data will be returned. It does not explicitly address potential edge cases or relationships to sibling tools, but the available schema and context fill most gaps.
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 0%, so the description carries the parameter-documentation burden. It explains that the 'maintainer' parameter is a 'canonical maintainer id (often email)', which adds meaning beyond the parameter name. The schema examples reinforce this, but the description itself provides essential clarification.
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 states that the tool provides a 'Maintainer summary' keyed by 'canonical maintainer id (often email)', which clearly identifies the resource and input. It does not use an explicit verb like 'retrieves' or 'returns', but the meaning is unambiguous enough to distinguish it from sibling tools like entity_profile or project.
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 that this tool is used when you have a canonical maintainer ID, but it does not explicitly state when to prefer it over alternatives or mention any exclusions. There is no naming of sibling tools or a when-not-to-use clause, so the guidance is implied rather than explicit.
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.
There is notable overlap among the ask_pipeworx variants (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded) and among the prediction market tools (bet_research, polymarket_arbitrage, polymarket_edges, polymarket_fill_risk, polymarket_kalshi_spread). While descriptions differentiate them, agents may struggle to choose the appropriate one without careful reading.
Most tools follow a snake_case verb_noun pattern (e.g., ask_pipeworx, compare_entities), but the Repology-specific tools break this pattern with simple nouns like maintainer, problems, project, and repositories. This inconsistency makes the overall naming feel mixed.
With 36 tools, the set is too large for a server focused on Repology package queries. Many tools are from the Pipeworx platform and include redundant variants (e.g., ask_pipeworx_beta, polymarket_edge_tracker), inflating the count without adding substantial new functionality.
For a server named Repology, the tool surface is severely incomplete: it lacks fundamental Repology operations like detailed package comparisons, version history exploration, and repository-specific queries. Even as a general data platform, there are gaps such as no batch export or aggregate statistics.