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

No annotations provided, but description fully covers behavior: read-only operation, costs 1 operation, returns up to max_memories, empty list if no relevant memories, and internal similarity to recall. No contradictions.

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?

Description is front-loaded with key purpose and usage, then provides additional details. Each sentence adds value, but it could be slightly more concise. Still efficient.

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?

No output schema, but description covers return behavior (up to max_memories, empty list not error). For a read-only retrieval tool with good parameter guidance, this is sufficiently 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?

Schema coverage is 100%, so baseline is 3. Description adds value beyond schema: examples for specific context query, usage suggestions for max_memories (e.g., 10-15 at start).

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 it loads relevant memories for session bootstrapping, distinguishes from recall by noting it's optimized for broad context vs specific questions. Verb 'load' plus resource 'memories' is specific.

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?

Explicitly instructs to call at start of every conversation and when switching topics. Contrasts with recall for specific queries, providing clear when-to-use and when-not-to-use guidance.

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 provided, but the description fully discloses the destructive, irreversible nature of the operation, the soft-delete behavior, the exact ID requirement, operation cost, and return behavior (confirmation or error).

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 thorough and every sentence adds value, but it is relatively long. It could be slightly more compact without losing information, but it remains clear and 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 absence of output schema and annotations, the description covers all critical aspects: purpose, parameters, behavior, usage constraints, and return values. No gaps.

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 already describes memory_id, but the description adds valuable context: the source of the ID (recall or context results), its format (UUID), and the requirement for exact match.

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 action 'permanently delete a memory by ID'. It also contrasts with sibling tools like 'context', 'recall', 'remember', and 'share' by being the dedicated deletion tool.

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?

Explicit guidance on when to use ('before storing a corrected version of a fact'), when not to use ('bulk cleanup', 'if unsure'), and implied alternatives (e.g., 'remember' for storing, 'recall' for retrieval).

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?

Despite no annotations, the description thoroughly discloses behavior: it's a read-only operation, uses a 4-way hybrid search, reranks with cross-encoder, costs 1 operation per call, returns empty list on no match, and returns relevance scores between 0-1. No contradictions with annotations (none provided).

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 well-structured and concise. It starts with purpose, then algorithm, usage guidance, examples, return format, cost, and error handling – all in logical order. Every sentence contributes meaning; no filler. Front-loaded with key information.

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 no output schema, the description explains return format (memories with relevance scores 0-1, up to limit, empty list on no match). It covers behavioral context (cost, algorithm). However, it does not detail the structure of each memory (e.g., fields like text, timestamp). A minor gap, but overall complete for most use cases.

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 baseline is 3. The description adds little beyond what the schema already provides for parameters. It mentions 'limit' in context of results, but the schema already describes each parameter similarly. No net gain in parameter understanding from the description.

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's purpose: 'Search persistent memory by meaning, returning the most relevant past memories ranked by semantic similarity.' It identifies the specific verb (search) and resource (memory), and distinguishes it from siblings (context) by explaining when to use recall vs context.

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 explicit usage guidance: 'Use recall (not context) when you need to answer a specific question... Do not use for broad session bootstrapping (use context instead).' It also gives concrete query examples like 'what language does the user prefer?' and explains when to use higher or lower limit values.

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?

Details write operation, encryption at rest, vector embedding generation, return values (ID and timestamp), and API quota cost. No annotations present, so description fully covers 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.

Is the description appropriately sized, front-loaded, and free of redundancy?

Six succinct sentences, each adding distinct value. Front-loaded with purpose, followed by usage, behavior, parameters, and cost. No unnecessary words.

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 write operation with 5 parameters and no output schema, description covers return value, quota, security features, and optimal usage patterns. No gaps identified.

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?

With 100% schema coverage, baseline is 3, but description adds valuable guidance: content format (self-contained with context), tag conventions (lowercase, consistent terms), scope visibility, and agent_id consistency for session persistence.

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?

Clearly states it stores facts/decisions/preferences in persistent memory as a write operation. Distinguishes from recall and forget by name, making purpose unambiguous.

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?

Explicitly advises when to use (learned lasting info) vs not (ephemeral, secrets, large files). Names sibling tools recall and forget as alternatives for reading and deleting.

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?

Despite no annotations, the description fully discloses behavioral traits: it's a write operation that changes scope, does not duplicate memory, is one-directional, and costs 1 operation. It also mentions prerequisites (memory ID from recall/remember) and scope limitations.

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 concise but dense, with every sentence contributing useful information. It could be slightly better structured with separators for use cases, but it remains clear and front-loaded with purpose.

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 low complexity (3 params, no output schema, no annotations), the description covers all essential aspects: purpose, usage, behavior, parameters, side effects, and constraints. No gaps remain for an agent to understand the tool correctly.

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%, and the description adds meaning by explaining where memory_id comes from, defining target_scope enum values with usage context, and clarifying when user_id is required. It adds value beyond the schema's property descriptions.

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's purpose: 'Widen a memory's visibility scope so other agents can access it.' It specifies the verb (widen/share) and resource (memory), and distinguishes itself from sibling tools like recall and remember by focusing on visibility changes.

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 explicit guidance on when to use the tool (e.g., for user preferences share to user scope, for team conventions share to org scope) and when not to use it ('Do not use to restrict scope'). It also explains the directional nature (agent to user to org).

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