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

No annotations are provided, so the description must carry the full burden of disclosing destructive behavior. It only says 'Delete' and does not warn that deletion is permanent/irreversible, nor does it mention permissions or side effects. This is similar to update_drive which scored 2.

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 two concise sentences with the action front-loaded. Every word serves a purpose: it defines the action, resource, and input source.

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?

Given the presence of a detailed schema and output schema, the description provides sufficient context for a delete tool. It explains where to get IDs and what the tool does. It could mention bulk deletion or irreversibility, but these are not essential for correct invocation.

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 documents both parameters with 100% coverage, including 'Use the id from search-knowledge results.' The description adds no new information about the parameters beyond restating the same guidance, so baseline 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 tool deletes information from the knowledge base by point ID, using the verb 'delete' and resource 'knowledge base.' It distinguishes from siblings like search-knowledge and store-knowledge.

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?

It explicitly instructs to use the 'id' from search-knowledge results, providing a clear prerequisite and workflow context. It does not mention when not to use this tool, but the deletion purpose makes that largely implicit.

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

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

With no annotations provided, the description must carry the full burden of behavioral disclosure. It mentions 'full payload details' but does not explain what happens with invalid IDs, whether the operation is read-only (implied but not stated), or any limits on the number of IDs. This is a significant gap for a fetch operation.

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 three succinct sentences, each serving a purpose: stating core functionality, clarifying the intent to inspect complete information, and providing operational guidance. It is front-loaded and contains no fluff.

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?

Given the existence of an output schema and complete parameter descriptions, the description successfully covers the essential dimensions of purpose and usage. However, because annotations are absent, some behavioral expectations (error handling, authentication) are left unaddressed, preventing a perfect score.

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?

Schema coverage is 100%, so the baseline is 3. The description adds value by linking the 'ids' parameter directly to the 'id' field from search-knowledge, providing data flow context beyond what the schema offers. This elevates parameter understanding.

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 'Retrieve specific knowledge items by their point IDs' with 'full payload details', which distinguishes it from sibling tools like search-knowledge (which likely returns summaries) and store/delete operations. The verb 'retrieve' is specific and the resource is well-defined.

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 gives clear context: it is for inspecting complete information about stored items, and explicitly directs the user to use the 'id' field from search-knowledge results. While it doesn't explicitly name alternatives or exclude other tools, the usage scenario is well established.

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

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

No annotations are provided, so the description carries the full burden. It implies a read operation via 'inspect' and 'view', but does not explicitly state that it is read-only, what exactly gets inspected (e.g., collection list, metadata, sample entries), or whether there are any side effects. This lack of disclosure is a notable gap.

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 two sentences, front-loaded with the tool's purpose and followed by a practical use case. There is no redundant information, and every word contributes value.

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?

For a simple inspection tool with one optional parameter and an output schema, the description covers the core purpose and usage scenarios. It doesn't need to explain return values in detail because an output schema is present, though stating the read-only nature would have made it more complete.

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 fully describes the only parameter (collection_name with a default from env), and the description's reference to 'collection settings' aligns with the parameter. Since schema coverage is 100%, the description adds minimal extra value but is consistent with the schema.

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 tool 'inspects' the 'knowledge base configuration' and 'view sample data', which distinguishes it from sibling tools that store, search, get by ID, or delete knowledge. It is specific about what the tool does and what resource it operates on.

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 says it is 'useful for debugging collection settings or verifying stored content', providing clear use cases. It does not explicitly mention when not to use it or alternatives, but the context is easily inferred from the sibling tool names.

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

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

With no annotations provided, the description must disclose behavioral traits. It mentions semantics search and Qdrant storage, indicating a read-only retrieval operation, but it doesn't describe any side effects, result ordering, or limitations. The presence of an output schema covers return values, but additional behavioral context like 'results are ranked by relevance' would improve transparency.

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 two sentences long, front-loaded with the core action, and every word adds value. It wastes no space and is easy to parse.

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?

Given the tool's relative simplicity, an output schema, and full schema coverage, the description is mostly complete. It lacks a bit of behavioral nuance (e.g., that results are semantically ranked and not exact matches), but it serves its purpose for an agent to understand invocation.

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 covers all parameter descriptions (100% coverage), so the baseline is 3. The description doesn't add parameter-specific meaning beyond what the schema already provides, though 'long-term knowledge base' hints at the collection context.

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 opens with 'Search for relevant information in the long-term knowledge base using semantic search,' which clearly identifies a specific verb (search), resource (long-term knowledge base), and method (semantic search). It distinguishes itself from siblings like store-knowledge and delete-knowledge by focusing on retrieval.

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 provides clear usage context with 'Use this to retrieve context, facts, or past interactions stored in Qdrant,' which indicates when to use the tool. It doesn't explicitly mention alternatives or when not to use it, so it stops short of a 5.

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

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 adds valuable behaviors beyond a simple 'store' action: 'Automatically embeds the text and returns the stored ID.' This informs the agent about the embedding process and the return value. It does not mention edge cases like duplicate handling or potential errors, but for a store operation it is reasonably transparent.

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 two sentences, front-loaded with the primary action, and contains no filler. Every sentence adds value: the first states the core operation, the second explains the embedding and return ID. It is concise and well-structured.

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?

Given the output schema exists, the description does not need to detail return values. It covers the core purpose, persistence semantics, and automatic embedding. It could mention storage prerequisites or default collection behavior, but the schema covers collection_name defaults. The description is sufficiently complete for an agent to decide to use this tool.

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?

Schema coverage is 100%, so the input schema already documents all parameters. The description adds only a reference to 'text' being embedded, which relates to the 'content' parameter but does not clarify tags, title, metadata, or collection_name beyond what the schema provides. Baseline 3 is appropriate as the schema does the heavy lifting.

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 a specific verb and resource: 'Store useful information or knowledge into the long-term knowledge base (Qdrant).' It also conveys persistence for later retrieval by the agent or other agents. However, it does not explicitly distinguish itself from the sibling 'store-knowledge-bulk' tool, leaving that differentiation to the tool name.

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 gives implied usage context: 'This allows the agent to persist data that can be retrieved later or by other agents.' It does not explicitly state when to use this tool vs. alternatives, nor does it mention exclusions such as 'for bulk storage, use store-knowledge-bulk.' The guidance is present but not explicit.

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

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

With no annotations, the description carries the full burden. It discloses key behaviors: items are 'automatically embedded' and 'stored with a unique ID', plus the storage target (Qdrant). It does not cover error handling or idempotency, but the stated behaviors are meaningful and beyond what the schema provides.

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?

Two sentences, no wasted words. The first sentence states the core purpose, the second adds efficiency context and behavioral detail. All information earns its place.

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 description covers the main usage scenario, efficiency over alternatives, and key behaviors. An output schema exists, so return values need not be described. It lacks details on failure modes or limits, but for a bulk store operation this is largely sufficient.

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?

Schema description coverage is 100%, so the schema documents both items and collection_name. The description adds minimal parameter-specific meaning beyond mentioning 'items' implicitly; it does not explain collection_name, but the schema already covers it adequately.

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 states 'Store multiple pieces of information or knowledge into the long-term knowledge base (Qdrant) at once' – a specific verb and resource with clear bulk scope. It also differentiates from the sibling store-knowledge by noting it is more efficient than calling store-knowledge multiple times.

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 explicitly mentions an alternative ('more efficient than calling store-knowledge multiple times'), which implies use for batched storage. However, it does not explicitly state when NOT to use it (e.g., for a single item use store-knowledge) or other exclusions.

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