knowledge_list_documentsList the documents stored in a knowledge base (most recent first).
Args: namespace: The knowledge-base namespace.
Returns: dict with keys: documents (list of {doc_id, title, ...}).
| Name | Required | Description | Default |
|---|---|---|---|
| namespace | Yes | The knowledge-base namespace |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, and destructiveHint, so the safety profile is covered. The description adds useful behavioral context about ordering ('most recent first') and the general return shape, but it does not disclose pagination, limits, or behavior for an invalid namespace. With annotation coverage, a middle score is appropriate.
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 main action, and easy to scan. The Args and Returns sections are slightly redundant with the schema and surrounding tool definition, but the overall length is still appropriate.
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?
This is a simple tool with one required, well-documented parameter and strong safety annotations. The description covers the key return structure and ordering, though the '...' leaves additional document fields unspecified. Given the low complexity, the description is nearly complete for invocation purposes.
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 schema already describes the namespace parameter fully at 100% coverage, and the description simply restates 'The knowledge-base namespace' without adding constraints, defaults, or examples. Per the baseline for high schema coverage, a score of 3 is appropriate.
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 verb and resource: 'List the documents stored in a knowledge base' with the additional specificity of 'most recent first.' It is easy to distinguish from sibling tools like knowledge_ingest or knowledge_query, though it does not explicitly name or contrast them.
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?
There is no explicit guidance about when to use this tool versus alternatives such as knowledge_query or knowledge_ingest. The usage context is only implied by the purpose; no exclusions, prerequisites, or alternative routing are provided.
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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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.