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

Annotations already declare readOnlyHint=true, and the description adds value beyond that by disclosing 'Does not consume OpenAI quota' and clarifying the GET operation has no semantic processing. No contradiction with annotations, and the extra cost/behavioral detail is genuinely useful for agent decision-making.

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?

Three concise sentences, each earning its place: the core action, the best use cases, and a cost-relevant detail. Information is front-loaded and there is no redundant filler.

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 read-only list tool with two optional parameters and an existing output schema, the description covers purpose, usage scenarios, and a notable behavioral trait (no quota consumption). Nothing critical is missing 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?

Schema description coverage is 100% and both parameters (limit, project_id) are fully documented in the schema with defaults and constraints. The description does not add parameter-level details, 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 states a specific verb and resource: 'List the project's memories' and explicitly notes the non-semantic nature with 'without semantic search (GET /memories)' and gives concrete use cases. This differentiates it from the semantic-search siblings (solucortex_search, solucortex_recall) without ambiguity.

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 provides explicit use contexts: 'quick inspection/audit' and 'confirm a just-created memory was stored'. It also implies that semantic search is NOT this tool's function, steering agents toward siblings for that need. It does not name the sibling tools explicitly, so there is a small gap, but the context is clear enough.

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?

Beyond the readOnlyHint and openWorldHint annotations, the description adds valuable behavioral detail: it returns only approved/active memories, ranks by semantic similarity plus importance, and warns about OpenAI embeddings being slower with a 20 req/min limit. This gives the agent important expectations about latency and throttling without needing to discover it at runtime.

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 compact: one sentence states the action and endpoint, one sentence gives usage timing and return value, and one sentence notes performance characteristics. Every sentence adds meaningful information and the most important usage guidance is front-loaded.

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 rich input schema, annotations, and presence of an output schema, the description covers the essential operating context: when to call it, what it returns, how results are ranked, and its performance constraints. No critical missing information prevents an agent from selecting and invoking 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?

The schema already fully documents both parameters (query and project_id) with clear descriptions, so the schema coverage is 100%. The tool description adds no extra parameter-level meaning beyond what the schema provides, which matches the baseline of 3 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?

The description clearly identifies a specific action ('Build living context for a task') and an endpoint ('POST /context/build'), and explains it returns approved, active memories ranked by similarity and importance. This is not a tautology and conveys the tool's read-oriented purpose, though it does not explicitly compare itself to sibling tools like solucortex_search.

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 a clear timing directive: 'Call this at the START of a task, before touching code.' It establishes the intended context of use effectively, but it does not provide explicit exclusion criteria or name alternatives for cases where this tool would not be appropriate.

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?

Given annotations already indicate this is a non-read, non-destructive, non-idempotent operation, the description adds valuable behavioral context: the memory is stored with status 'approved' and is traced, and a bearer API key is required. It also discloses the safe-handling policy for secrets, which goes beyond the structured 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 four concise sentences with no filler. The core action is front-loaded, followed by when-to-use, behavioral context, and the security caveat. Every sentence 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 is complete for a memory-creation tool: it covers action, trigger conditions, authentication, post-condition (approved/traced), and secret-handling policy. With an output schema present and comprehensive parameter schemas, nothing essential is 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?

Schema description coverage is 100%, so the baseline is 3. The description adds meaning by explaining how to handle sensitive content in the `content` parameter: record location/type/severity and action taken with a redacted reference. This is practical guidance beyond the schema's 'NEVER include real secrets' note.

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 a specific verb and resource: 'Record a memory in SoluCortex (POST /memories)'. It clearly distinguishes this write operation from its siblings (list, recall, search) by naming the creation endpoint and memory domain.

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 states when to call the tool: 'Call when closing a task or making a relevant technical decision.' It does not mention exclusions or alternatives, but the sibling tools are clearly different operations, so the primary usage context is sufficient.

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?

Annotations already declare readOnlyHint and openWorldHint, so the description only needs to add context beyond that. It adds the rate limit ('20 req/min'), the embedding model dependency ('OpenAI embeddings'), and the ad-hoc nature. This is useful behavioral context without contradicting the 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?

Three short sentences, each earning its place: the first defines the operation, the second gives usage context and sibling differentiation, the third discloses operational constraints. The most important distinction is front-loaded.

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 simple search operation, full schema coverage, readOnly/openWorld annotations, and the presence of an output schema, the description is complete. It covers purpose, usage context, sibling differentiation, and rate limiting — nothing essential is missing for an agent to call it 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 description coverage is 100%, so the schema already documents all three parameters. The description does not add parameter-specific semantics, but it sets general context (semantic search over memories). This meets the baseline for full 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?

The description states a specific verb and resource: 'Ad-hoc semantic search of memories (POST /search/semantic).' It also gives a concrete example ('how is authentication implemented?') and explicitly distinguishes itself from solucortex_recall, so an agent can tell them apart.

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 says precisely when to use this tool — 'for specific questions during a task' — and when not to, by contrasting with recall, which 'builds the full startup context.' This gives the agent an explicit selection rule between the two most similar siblings.

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