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wlmwwx

Jina AI Remote MCP Server

by wlmwwx

search_jina_blog

Search Jina AI's official blog and news articles to find documentation, tutorials, product announcements, and technical content about AI, machine learning, neural search, and embeddings.

Instructions

Search Jina AI news and blog posts at jina.ai/news for articles about AI, machine learning, neural search, embeddings, and Jina products. Use this to find official Jina documentation, tutorials, product announcements, and technical deep-dives.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYesSearch terms to find relevant Jina blog posts (e.g., 'embeddings', 'reranker', 'ColBERT'). Can be a single query string or an array of queries for parallel search.
numNoMaximum number of blog posts to return, between 1-100
tbsNoTime-based search parameter, e.g., 'qdr:h' for past hour, can be qdr:h, qdr:d, qdr:w, qdr:m, qdr:y
Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden of behavioral disclosure. It describes what content is searched (Jina AI news and blog posts) and the types of content returned, but doesn't mention important behavioral aspects like rate limits, authentication requirements, pagination behavior, or error conditions. The description adds value but leaves significant behavioral questions unanswered.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is efficiently structured in two sentences that each earn their place. The first sentence establishes the core purpose and scope, while the second provides usage context. There's no wasted verbiage or redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a search tool with 3 parameters, 100% schema coverage, but no output schema and no annotations, the description provides adequate purpose and usage context. However, it doesn't describe the return format, result structure, or what happens when no results are found, which would be helpful given the lack of output schema.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the schema already documents all three parameters thoroughly. The description doesn't add any parameter-specific information beyond what's in the schema descriptions. This meets the baseline expectation when schema coverage is complete.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: searching Jina AI news and blog posts at a specific URL (jina.ai/news) for content about specified topics. It distinguishes this tool from sibling tools by focusing on Jina's official content rather than general web search or other academic/search tools.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides clear context about when to use this tool: to find official Jina documentation, tutorials, product announcements, and technical deep-dives. However, it doesn't explicitly state when NOT to use it or name specific alternatives among the sibling tools for different search needs.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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