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SIP News MCP

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build_semantic_index

Builds or updates a vector index of SIP news for semantic search. Embeds recent articles to enable relevance-based retrieval.

Instructions

Build or update the local vector index that powers semantic_search.

Pages through the most recent SIP news, embeds each item (title + summary)
via OpenRouter, and stores the vectors in a local ChromaDB collection. Safe
to run repeatedly: by default it only embeds items not already indexed. Run
it once before using semantic_search, and again periodically to pick up new
news. This call fetches from SIP and the embedding API, so it can take a
little while for large `max_items`.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
languageNoInterface language to pull news from: de, fr or en.de
max_itemsNoHow many recent items to (re)index (1-2000).
refreshNoRe-embed items already in the index instead of skipping them.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior4/5

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

No annotations are provided, so the description carries full burden. It discloses that the tool pages through SIP news, embeds via OpenRouter, stores vectors in ChromaDB, is safe to run repeatedly (only indexes new items by default), and that it can take time for large max_items. This covers key behavioral traits.

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 concise (four sentences) and front-loaded with the primary purpose. Every sentence adds value: purpose, process, safety/repeatability, and performance characteristic. No redundancy or fluff.

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

Completeness4/5

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

Given the tool's complexity (external API calls, indexing process), the description explains the essential steps and context. The existence of an output schema (not shown but indicated) means return values are documented elsewhere. The description is sufficiently complete for an agent to understand when and how to use the tool.

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 coverage is 100%, so baseline is 3. The description does not add meaning beyond the schema for parameters: language, max_items, refresh. The schema itself already describes them clearly. The description provides context about how they are used in the process, but no new semantic details.

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: 'Build or update the local vector index that powers semantic_search.' It uses specific verbs (build/update) and identifies the resource (local vector index). This distinctly differentiates it from sibling tools like browse_latest_news, get_article, or semantic_search.

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 explicit usage guidance: 'Run it once before using semantic_search, and again periodically to pick up new news.' It also notes the tool is safe to run repeatedly and describes default behavior. While it does not mention alternatives or when not to use it, the guidance is clear and context-specific.

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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