Sovereign AI Blog

search_blog

Read-onlyIdempotent

Search the Sovereign AI Blog for articles matching a natural language query,
optionally filtered by tag and sorted by relevance or date.

Behaviour matrix:
  - query='', sort=*           -> list newest-first, optionally tag-filtered
  - query!='', sort=relevance  -> TF-IDF ranked, optionally tag-filtered
  - query!='', sort=date_desc  -> TF-IDF filtered (score > 0.001), then sorted by date

Pure read-only, deterministic for a given KB snapshot.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`n`	No	Maximum number of results to return
`tag`	No	Optional tag filter (e.g. 'setup', 'fixes', 'strategy'). Only articles with this tag are considered. Use list_tags to discover available tags.
`sort`	No	Result ordering. 'relevance' uses TF-IDF score (default for non-empty query). 'date_desc' sorts newest first (default behaviour when query is empty). When query is empty, 'relevance' is treated as 'date_desc'.	relevance
`query`	No	Natural language search query (e.g. 'flashinfer OOM on GB10'). Multi-word queries are tokenized and TF-IDF ranked. Pass empty string to list articles without ranking by relevance.

Output Schema

TableJSON Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

Changed10 schema fields changedApr 30, 2026

changedInput schema / properties / n / maximum
```
Previous value: -10New value: +20
```
addedInput schema / properties / query / default
```
Added value: +""
```

changedInput schema / properties / query / description

Previous value: -"Natural language search query (e.g. 'flashinfer OOM on GB10'). Multi-word queries are tokenized and TF-IDF ranked."New value: +"Natural language search query (e.g. 'flashinfer OOM on GB10'). Multi-word queries are tokenized and TF-IDF ranked. Pass empty string to list articles without ranking by relevance."

addedInput schema / properties / sort

Added value: +{
+  "default": "relevance",
+  "description": "Result ordering. 'relevance' uses TF-IDF score (default for non-empty query). 'date_desc' sorts newest first (default behaviour when query is empty). When query is empty, 'relevance' is treated as 'date_desc'.",
+  "enum": [
+    "relevance",
+    "date_desc"
+  ],
+  "title": "Sort",
+  "type": "string"
+}

addedInput schema / properties / tag

Added value: +{
+  "anyOf": [
+    {
+      "type": "string"
+    },
+    {
+      "type": "null"
+    }
+  ],
+  "default": null,
+  "description": "Optional tag filter (e.g. 'setup', 'fixes', 'strategy'). Only articles with this tag are considered. Use list_tags to discover available tags.",
+  "title": "Tag"
+}

removedInput schema / required
```
Removed value: -[
-  "query"
-]
```

addedOutput schema / $defs / SearchResult / properties / date

Added value: +{
+  "default": "",
+  "description": "Publication date (ISO 8601)",
+  "title": "Date",
+  "type": "string"
+}

changedOutput schema / $defs / SearchResult / properties / quality_score / description

Previous value: -"Build-time quality score, unbounded weighted composite across 13 signals from the editorial pipeline (style-aware, higher is better; thresholds depend on style)"New value: +"Build-time editorial quality score"

changedOutput schema / $defs / SearchResult / properties / quality_style / description

Previous value: -"Editorial style category (e.g. 'best_practice_learnings', 'werthaltige_code_beispiele', 'conclusion', 'smart_infotainment'). Empty if not categorised."New value: +"Editorial style category"

changedOutput schema / $defs / SearchResult / properties / relevance_score / description

Previous value: -"TF-IDF cosine similarity score, range 0.0 to 1.0 (higher is more relevant)"New value: +"TF-IDF cosine similarity score, 0.0 to 1.0 (1.0 = direct date-sorted hit when query is empty)"

First observedApr 30, 2026

TDQS

A4.7/5.0

Behavior5/5

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

The behavior matrix explicitly documents three query/sort combinations and the TF-IDF threshold (score > 0.001), going well beyond the readOnlyHint and idempotentHint annotations. It also notes determinism for a given KB snapshot, adding meaningful execution context.

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 compact, front-loaded with purpose, and uses a behavior matrix for efficient structured communication. Every sentence earns its place, with no redundant restatement of the schema.

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

Completeness5/5

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

Given rich annotations and a detailed input schema, the description covers the core behavior variations, read-only nature, determinism, and filtering/sorting. An output schema is present, so return-value documentation is unnecessary; there are no significant gaps for an agent to select and invoke the tool correctly.

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

Parameters4/5

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

Schema coverage is 100% with detailed per-parameter descriptions, so the baseline is 3. The description adds cross-parameter semantics (e.g., query='' with any sort yields newest-first, query!='' with date_desc applies TF-IDF filtering then date sorting), which is valuable beyond the schema.

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 opens with 'Search the Sovereign AI Blog for articles matching a natural language query,' clearly identifying the verb, resource, and scope. It distinguishes itself from siblings like get_article and list_tags, and the behavior matrix reinforces its search/filter/sort purpose.

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 clearly establishes when to use the tool: when searching blog articles by natural language, optionally filtered by tag. It does not explicitly mention alternatives or exclusions (e.g., using get_article for a single article), so it misses a 5, but the context is otherwise unambiguous.

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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