extract_key_phrasesStatistical key-phrase extraction — top-N ranked phrases.
Brainiall Key Phrases engine. Pure-statistical (TF + position + casing + stopword filter), no ML cost.
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes | Input text | |
| top_k | No | Number of phrases to return | |
| max_ngram | No | Max words per phrase (1-4) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint and idempotentHint, and the description adds valuable behavioral context: the algorithm uses TF, position, casing, and stopword filtering, and incurs no ML cost. This clarifies what the tool does and does not do without contradicting annotations.
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 short, front-loaded with the core purpose, and every sentence earns its place. The algorithm details are useful and add context without bloat.
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?
The tool has only three simple parameters, all documented in the schema, and annotations cover read-only safety and idempotency. The description adequately explains the extraction approach and ranking behavior; no critical invocation details appear missing.
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 already provides 100% coverage for all three parameters, so the description doesn't need to add much. It adds no specific parameter details beyond what the schema states, matching the baseline for high schema coverage.
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?
Description clearly states the operation: statistical key-phrase extraction returning top-N ranked phrases. It distinguishes itself from similar siblings like extract_entities by emphasizing key phrases and the pure-statistical approach.
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 indicates this is for lightweight, statistical extraction with no ML cost, which implies appropriate use cases. However, it does not explicitly state when to choose this over alternatives like extract_entities or when not to use it.
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 map cleanly to distinct capabilities, and the descriptions make the intended use clear. A few adjacent pairs—extract_entities vs link_entities_to_wikidata and detect_pii vs detect_conversational_pii—require careful selection, but they are distinguishable by their stated outputs.
Names are uniformly lowercase snake_case and mostly follow a verb_object pattern, such as analyze_*, detect_*, extract_*, summarize_text, and translate_text. A few outliers like aspect_sentiment, fraud_feedback, and knowledge_ingest break the verb-first feel, but the overall pattern remains predictable.
At 22 tools, this is on the heavy side of the borderline range. Each tool has a distinct job, but the mix of core NLP, safety, fraud, health-checking, and knowledge-base management makes the surface feel sprawling rather than tightly scoped.
The core NLP coverage is broad: sentiment, toxicity, PII, entities, QA, summarization, translation, and groundedness are all present. However, the knowledge-base tools support ingest/list/query but no delete or update, creating a dead end when documents need correction or removal.