problemsOutstanding maintenance problems for a project.
| Name | Required | Description | Default |
|---|---|---|---|
| project | Yes |
| Name | Required | Description | Default |
|---|---|---|---|
| count | Yes | Number of items returned. | |
| items | Yes | Array of outstanding maintenance problems |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / examplesAdded value: +[
+ {
+ "project": "firefox"
+ },
+ {
+ "project": "nginx"
+ }
+]Output schema / (root)Previous value: -nullNew value: +{
+ "properties": {
+ "count": {
+ "description": "Number of items returned.",
+ "type": "integer"
+ },
+ "items": {
+ "description": "Array of outstanding maintenance problems",
+ "items": {
+ "properties": {
+ "maintainer": {
+ "description": "Maintainer identifier",
+ "type": "string"
+ },
+ "problem": {
+ "description": "Problem description",
+ "type": "string"
+ }
+ },
+ "type": "object"
+ },
+ "type": "array"
+ }
+ },
+ "required": [
+ "items",
+ "count"
+ ],
+ "type": "object"
+}Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, covering the safety profile. The description adds a small behavioral nuance with 'outstanding' (implying a filter for unresolved problems) but does not disclose other traits like pagination, sorting, or whether problems are returned in a specific order. This aligns with the annotations, with no contradiction.
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 short sentence, immediately conveying the core purpose. There is no wasted text, and the wording is front-loaded with the key noun phrase. It earns full marks for conciseness and clear structure.
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?
Given the tool has one parameter and an output schema, the description still leaves significant gaps. It does not clarify what counts as 'outstanding', whether the result is a list, or how it relates to sibling tools like 'recent_changes' or 'maintainer'. The description is too minimal to fully equip an agent to decide when and how to invoke this tool.
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 description coverage is 0%, and the description only states 'for a project', which adds little beyond the parameter name 'project'. The examples in the schema ('firefox', 'nginx') hint at project names, but the description does not explain what format or identifier is expected. With low schema coverage, the description should provide more meaningful guidance, but it does not.
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 names the resource ('outstanding maintenance problems') and its scope ('for a project'), making the tool's purpose identifiable. However, it lacks a verb like 'Get' or 'List', so it reads as a noun phrase rather than an explicit action. It distinguishes from siblings by the unique 'problems' resource, since no other tool mentions maintenance problems.
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 provides no information about when to use this tool versus alternatives. It does not mention any exclusions, prerequisites, or related sibling tools. Usage must be inferred solely from the tool name and brief noun phrase, which is insufficient for clear selection guidance.
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.