most_readGet the most-read Wikipedia articles for a specific date.
| Name | Required | Description | Default |
|---|---|---|---|
| day | Yes | Two-digit day number (e.g., "01", "15") | |
| year | Yes | Four-digit year (e.g., "2024") | |
| month | Yes | Two-digit month number (e.g., "01", "12") |
| Name | Required | Description | Default |
|---|---|---|---|
| date | Yes | Date in YYYY-MM-DD format | |
| articles | Yes | Top 20 most-read articles |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Output schema / (root)Previous value: -nullNew value: +{
+ "properties": {
+ "articles": {
+ "description": "Top 20 most-read articles",
+ "items": {
+ "properties": {
+ "description": {
+ "description": "Short description or null",
+ "type": [
+ "string",
+ "null"
+ ]
+ },
+ "display_title": {
+ "description": "Formatted display title",
+ "type": "string"
+ },
+ "rank": {
+ "description": "Ranking position",
+ "type": "number"
+ },
+ "title": {
+ "description": "Wikipedia article title",
+ "type": "string"
+ },
+ "url": {
+ "description": "Wikipedia page URL or null",
+ "type": [
+ "string",
+ "null"
+ ]
+ },
+ "views": {
+ "description": "Number of views",
+ "type": "number"
+ }
+ },
+ "required": [
+ "rank",
+ "title",
+ "display_title",
+ "views",
+ "description",
+ "url"
+ ],
+ "type": "object"
+ },
+ "type": "array"
+ },
+ "date": {
+ "description": "Date in YYYY-MM-DD format",
+ "type": "string"
+ }
+ },
+ "required": [
+ "date",
+ "articles"
+ ],
+ "type": "object"
+}Input schema / examplesAdded value: +[
+ {
+ "day": "01",
+ "month": "03",
+ "year": "2024"
+ }
+]Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide robust safety hints (readOnly, idempotent, non-destructive). The description adds no behavioral context beyond what the schema and annotations convey, such as pagination, date validation, or return format limitations. It only restates the date constraint.
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, front-loaded sentence with no unnecessary words. It efficiently states the tool's purpose without redundancy.
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 read-only tool with an output schema and comprehensive annotations, the description is sufficient. It doesn't explain return values (covered by output schema) or safety (covered by annotations), but it could optionally mention that the date must be valid/historical. Overall, it meets the needs of a straightforward data-retrieval 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 descriptions cover 100% of the parameters with format examples. The description merely says 'for a specific date,' adding no extra semantics beyond the schema's detailed parameter explanations.
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's action ('Get') and resource ('most-read Wikipedia articles') for a specific date. This differentiates it from siblings like featured_article or on_this_day, which focus on other content.
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 usage when a specific date is known and the user wants top articles, but it does not explicitly mention when to prefer this tool over alternatives or discuss exclusions. The context is clear but not fully elaborated.
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.
Most tools have clearly distinct purposes, but some overlap exists: ask_pipeworx and ask_pipeworx_grounded are very similar (one grounded), and multiple Polymarket tools (arbitrage, edges, tracker, fill_risk, kalshi_spread) could be confused despite distinct roles. Overall, an agent can usually differentiate with careful reading.
Tool names follow mixed conventions: some use verb_noun (ask_pipeworx, compare_entities), others start with prefixes (pipeworx_, polymarket_, scan_), and a few are nouns (most_read, on_this_day). While readable, there is no consistent pattern, making it harder to predict tool names.
34 tools is high but not extreme for a broad-scope server. However, the server name 'wikifeed' suggests a Wikipedia focus, yet only 4 of 34 tools relate to Wikipedia (featured_article, most_read, on_this_day, picture_of_day). The tool count feels excessive relative to the name, but the actual breadth may justify it.
The toolset covers a wide range of domains (company data, prediction markets, factual queries, Wikipedia) with reasonable depth. Minor gaps exist: no Wikipedia search or edit tools, no direct tool for simple web search (relying on ask_pipeworx). Overall, agents can accomplish most tasks without hitting dead ends.