get_newspaper_statsPer-newspaper article counts and date ranges.
| Name | Required | Description | Default |
|---|---|---|---|
| country | No | Exact country name: Benin | Burkina Faso | Côte d'Ivoire | Niger | Togo (accents optional) |
| Name | Required | Description | Default |
|---|---|---|---|
| view | Yes | ||
| newspapers | Yes | ||
| country_filter | No | ||
| total_articles | Yes | ||
| total_newspapers | Yes |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / $schemaPrevious value: -"http://json-schema.org/draft-07/schema#"New value: +"https://json-schema.org/draft/2020-12/schema"Output schema / $schemaPrevious value: -"http://json-schema.org/draft-07/schema#"New value: +"https://json-schema.org/draft/2020-12/schema"Output schema / properties / viewAdded value: +{
+ "type": "string"
+}Output schema / requiredPrevious value: -[
- "total_newspapers",
- "total_articles",
- "newspapers"
-]New value: +[
+ "view",
+ "total_newspapers",
+ "total_articles",
+ "newspapers"
+]Input schema / additionalPropertiesRemoved value: -falseOutput schema / (root)Previous value: -nullNew value: +{
+ "$schema": "http://json-schema.org/draft-07/schema#",
+ "additionalProperties": false,
+ "properties": {
+ "country_filter": {
+ "type": "string"
+ },
+ "newspapers": {
+ "items": {
+ "additionalProperties": {},
+ "properties": {},
+ "type": "object"
+ },
+ "type": "array"
+ },
+ "total_articles": {
+ "type": "number"
+ },
+ "total_newspapers": {
+ "type": "number"
+ }
+ },
+ "required": [
+ "total_newspapers",
+ "total_articles",
+ "newspapers"
+ ],
+ "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 minimal extra context by summarizing the output (counts and date ranges) but does not mention any behaviors like pagination, filtering, or required permissions. It does not contradict annotations, so a mid-range score is appropriate.
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 that conveys the core purpose without any wasted words. It is concise and well-structured.
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's simplicity (one optional parameter, full schema coverage, output schema present, and robust annotations), the minimal description is largely sufficient. However, it does not mention the optionality of the country filter or any usage caveats, which could be inferred from the schema but are not explicitly stated.
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 input schema provides 100% coverage for the single parameter 'country', including a clear description and allowed values. The description does not add any additional parameter semantics, so the baseline score of 3 is appropriate.
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 identifies the resource (newspaper statistics) and the scope ('per-newspaper'), which distinguishes it from collection-level stats. However, it lacks an explicit verb, relying on the tool name 'get' to convey the action. It doesn't explicitly differentiate from siblings like get_collection_stats, but 'per-newspaper' provides enough context.
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 guidance on when to use this tool versus alternatives. It does not mention any exclusions, prerequisites, or preferred use cases. Given the large sibling set, this omission weakens the agent's ability to select the correct tool.
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.
Each tool targets a distinct resource or analytical dimension: get_* tools are specific to item categories (article, audiovisual, document, image, publication, reference) or specific analyses (cooccurrence, field distribution, lexical metrics, semantic map, sentiment distribution, similar items, temporal distribution, topic distribution). Search tools are clearly separated by subset, with generic 'search' for cross-category discovery and search_* for filtered queries. The only potential overlap between 'fetch' and get_* is resolved by 'fetch' returning a standard format while get_* tools provide category-specific extra metadata.
All tools follow a consistent verb_noun pattern: fetch, get_*, list_*, search_*. The verbs are clear and the nouns precisely indicate the resource or action. There are no mixed conventions (e.g., no camelCase or inconsistent verb styles), making the naming predictable and easy to navigate.
At 34 tools, the count is well above the 25+ threshold that the rubric flags as too many. While the server's comprehensive scope for a digital archive with multiple subsets and analytical features explains the high count, the sheer number may overwhelm agents and makes the toolset feel heavy rather than well-scoped. Some grouping or consolidation (e.g., merging distribution tools or providing a single fetch with optional detail levels) could reduce the load without losing functionality.
The tool surface provides complete coverage for the domain: every content type (articles, publications, references, documents, audiovisual, images, index) has both search and get/retrieve tools, plus listing tools for key vocabularies. Analytical tools for statistics, distributions, sentiment, topics, and similarity are fully realized. There are no obvious dead ends—users can discover, retrieve, and analyze all parts of the collection, and even gaps in the underlying data (e.g., limited OCR coverage) are explicitly surfaced via tools like get_collection_stats.