Return 787daily articles that mention a specific named entity (person, organization, or place), newest-first. Match is on the entity's canonical name (case-insensitive). Returns { name, count, articles[] }.
| Name | Required | Description | Default |
|---|---|---|---|
| name | Yes | Entity name to search for, e.g. 'LUMA Energy', 'Ricardo Rosselló' |
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"Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It discloses the sorting order (newest-first), the matching logic (canonical name, case-insensitive), and the return structure ({ name, count, articles[] }). This exceeds simple read-only intent by explaining important behavioral traits. It does not mention pagination or limits, but that is not critical for a simple lookup tool.
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 extremely concise: two sentences, no fluff. The main purpose is stated first, followed by matching behavior and return shape. Every sentence earns its place, making it easy for an agent to parse quickly.
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?
With only one parameter, no output schema, and no annotations, the description provides all essential information: it states what the tool returns, in what order, with what matching logic, and the exact return structure. This is fully complete for a tool of this simplicity, and the sibling context does not reveal any missing requirements.
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 for the single parameter 'name' is 100%, with the schema already describing it as 'Entity name to search for.' The description adds extra semantic value by clarifying that matching is on the entity's canonical name and case-insensitive, which goes beyond the schema's generic definition. This justifies a score above the baseline of 3.
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 purpose: 'Return 787daily articles that mention a specific named entity (person, organization, or place), newest-first.' This is a specific verb (return) and resource (articles filtered by entity), with explicit scope. It distinguishes from siblings like search_news and keyword_search_news by focusing on entity-based lookup and canonical name matching.
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 clear context for when to use the tool: for searching by named entities such as people, organizations, or places. It also adds a key usage detail: match is on the entity's canonical name, case-insensitive. However, it does not explicitly mention alternatives or when not to use it, so it falls short of a 5.
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: ask synthesizes answers, search_news does semantic retrieval, keyword_search_news does keyword matching, and the get_/list_ tools target specific data types. However, search_pr_news is a redundant alias, and get_by_entity vs get_by_municipality overlap on place queries, creating minor ambiguity.
Tool names use a mix of prefixes (get_, list_, search_) and include a bare verb (ask), which is not fully consistent. The legacy alias search_pr_news further deviates from the pattern, though the rest are readable and predictable.
With 13 tools spanning news, Q&A, weather, cost-of-living, and safety, the count fits the server's broad purpose without being excessive. Each major domain has dedicated tools, and despite one redundant alias, the set remains well-scoped.
The tool set covers article discovery, synthesis, weather, demographics, and safety statistics, providing solid domain coverage. Minor gaps include no full-text article retrieval and no dedicated section browsing, but summaries and links make these workable.