Mnemoverse Memory

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

Annotations only say the tool is not read-only, not open-world, not idempotent, and not destructive. The description adds meaningful behavior: default invite scope is read_write, other assistants can read and write, and the return value is the room's address. This goes well beyond annotation hints, though it does not cover edge cases like duplicate names or failure behavior.

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 longer than average, but every sentence earns its place: shared semantics, default permissions, when to use it, return-value usage, and the next recommended action. No filler or redundant restatement of the schema.

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 what the tool does, why it exists, how to use the result across sibling tools, and what action to take next. With only two parameters and an output schema present, nothing essential is missing for an agent to invoke this 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 description coverage is 100%, so the input schema already fully documents the `name` and `description` parameters. The description does not add significant new parameter-level meaning beyond restating the name example and the uniqueness constraint already present in the schema. 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 opens with a specific verb and resource: 'Create a SHARED memory room.' It clearly distinguishes this from other sibling tools by emphasizing the shared/collaborative aspect and by explaining that the returned address is used as a `domain` on memory_write/memory_read and memory_list_recent. The example 'make a room for me and Olya' anchors the intent unambiguously.

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 use this tool ('Use when the user wants to share context or collaborate with someone else'), and it names the next logical step, memory_invite_to_room. It also explains how to use the result with sibling tools. It does not explicitly state when not to use it or contrast it against a close alternative, but the shared-vs-private distinction is strongly implied.

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 only say readOnlyHint=false, idempotentHint=false, destructiveHint=false; the description goes well beyond by explaining the ranking consequence of positive vs. negative feedback, that it applies across all of the user's tools, and that nothing is erased or time-decayed. It also discloses the authorization constraint that a read-only room member cannot rate room memories, which the annotations do not capture.

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 core purpose and the ranking consequence are front-loaded in the first two sentences, with the mechanics (ids, domain, permission caveat) following. It is a little dense at four sentences, but every sentence carries distinct information and none is redundant with the schema.

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?

With an output schema present, return values need no explanation, and the description still covers the trigger, the effects, the parameter sourcing, the domain rule, and the permission boundary. Nothing an agent needs to invoke this mutation correctly 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 real meaning on top by specifying where memory_ids come from (the id: line of memory_read results) and precisely when domain must be supplied versus omitted. It stops short of adding numeric guidance for the outcome range, which the schema already handles well.

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 first sentence gives a specific verb and resource ('Report whether memories returned by memory_read were actually helpful') and immediately frames the tool's role as a learning signal rather than a log, which separates it from memory_read and memory_write in the sibling set. An agent can identify the tool's function without opening the schema.

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 gives explicit timing ('Call it right after you act on (or reject) recalled memories') and an explicit prerequisite mapping ('passing the ids from the memory_read results as memory_ids'), plus the domain condition and the read-only-room exclusion. When-to-use, when-not, and the required upstream tool are all stated.

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 provide readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the explicit 'Read-only' adds no new safety information. The description does add scoping context (edges around given concepts) and the returned edge attributes, but deeper behavioral quirks such as depth-based weight flooring and the domain no-op live in the schema rather than the description.

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 tight sentences that front-load the core operation and then state concrete use cases. The trailing 'Read-only' is redundant with annotations but negligible, and there is no 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?

Between the rich schema documentation, the output schema, and the annotations, an agent has everything needed to call this tool correctly. The description supplies the conceptual purpose and use cases without needing to repeat return-value details.

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%, with all five parameters fully documented including defaults, bounds, and special cases. The tool description does not add parameter-level meaning, so the baseline 3 applies.

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 names a specific verb ('Reads'), a specific resource ('association edges around given concepts'), and the concrete output fields (weight, outcome valence, co-activation count). It is clearly distinct from siblings like memory_read by focusing on the graph structure rather than raw memory entries.

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 second sentence gives concrete use cases: inspecting what a memory store has learned and explaining why a read expanded to a concept. It does not explicitly name alternatives or state when not to use this tool, so it falls just 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 annotations carrying almost no behavioral detail, the description properly carries the burden: it explains the single-use default, the max_uses override, the fact that the user forwards a prepared message, and the recipient's two join paths (link or code). It does not cover error cases or rate limits, but it covers what an agent needs to invoke safely.

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 sentences front-load the core action and output, then provide workflow and trigger context. There is no filler; every sentence contributes distinct operational 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 that all parameters are documented, an output schema exists, and annotations are present, the description supplies the missing context: ownership prerequisite, default single-use behavior, the forwarding flow, and when to invoke. An agent has enough to call this tool correctly without guessing.

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 meaning beyond the schema by explaining max_uses as 'let several people join with the same one' and room_id as requiring a room the user owns. It does not add extra semantics for scope or expires_in_days, but the schema already documents those clearly.

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 action ('Mint an invite') and resource ('for a room you own'), and immediately distinguishes it from sibling tools like memory_create_room and memory_join_room. It also names the concrete outcome, a ready-to-forward message, so an agent can tell exactly what this tool does.

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 says 'Use after memory_create_room' and maps a user trigger phrase ('invite <someone>') to this tool, which is strong routing guidance. It does not explicitly name when not to use it or point to an alternative, but the triggers given are specific and actionable.

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 annotations, the description explains the post-join behavior: the returned address should be used as the domain on memory_read, and whether memory_write succeeds depends on membership scope. It even handles the unknown-scope case instead of promising either outcome, which is valuable 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?

Three dense sentences, with the action and trigger front-loaded. The later permission/scope guidance earns its place because it directly affects how an agent should use the result. No filler is present.

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?

With only one required parameter, 100% schema coverage, and a provided output schema, the description still adds important context about what to do after joining and how memory_write authorization is determined. No essential calling information 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 coverage is 100% and the schema already says the code is 'The invite code (mnvr_...)'. The description repeats the 'mnvr_' prefix and adds user-trigger context, but provides little additional semantic meaning beyond what the schema already supplies.

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 ('Join') and resource ('shared memory room'), and immediately identifies the invite-code format ('mnvr_'). This cleanly distinguishes it from sibling tools like memory_create_room and memory_invite_to_room.

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 explicit triggers: 'when the user pastes an invite code or says something like join room with code ...'. It does not spell out when-not-to-use or name alternative tools, but the trigger conditions are clear enough for confident selection.

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 mark the tool read-only and idempotent, and the description adds genuinely useful behavioral context: pages are complete within a scope, size-bounded pages can return fewer than `limit`, cursors are required to avoid dropped entries, and rooms are separate stores. No contradiction with 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?

Dense and front-loaded: the core action appears in the first sentence, and every caveat earns its place by either explaining a consequence or a required condition. Warnings such as size budgeting and room separation are actionable rather than 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?

The description covers everything needed to invoke correctly: when to use it, when to scope by domain, how paging works, and where room addresses come from memory_list_rooms. Since an output schema exists, the lack of return-format explanation is not a gap.

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?

Even with 100% schema coverage, the description adds meaning beyond the schema: `since` is framed as a novelty watermark, cursors continue listings without skips/duplicates, `limit` is a ceiling affected by page size, and `domain` unlocks shared rooms. The schema already documents syntax well, and the description reinforces behavior around it.

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?

States the exact behavior—lists the newest memories first—and immediately distinguishes itself from semantic search by contrasting 'what do I know about X' with 'what happened lately.' This also cleanly separates it from sibling tools like memory_read and memory_list_rooms.

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?

Gives explicit use cases (resuming work, catching up on a shared room, reviewing recent activity), tells when to pass `since`, and when `domain` is mandatory. It even specifies the exclusion condition: an unscoped call never covers rooms, so an agent knows exactly when to supply the room address.

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 the tool as read-only, idempotent, and non-destructive. The description adds meaningful behavioral context by explaining that returned addresses are suitable for memory_read and conditionally for memory_write, including that read-only membership will have writes refused. This goes beyond the annotation hints and provides practical behavioral expectations.

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, but each clause carries essential information: ownership/joined scope, how returned addresses are used, the read-only write caveat, and concrete usage examples. There is no filler or redundancy.

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 zero-parameter tool with an output schema present, the description is complete. It explains what the tool lists, how the results should be consumed, and when to invoke it across sessions. It also implicitly clarifies membership semantics, so an agent has enough context to call and interpret 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 tool has zero parameters, so the input schema fully documents everything. The description adds no parameter information, which is appropriate and unnecessary here. A baseline of 4 is appropriate for a parameterless tool with complete 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 clear, specific action: listing the shared memory rooms the user owns plus those they've joined. It also explains that each result includes the address to pass as `domain` on memory_read and memory_write, which distinguishes this tool from sibling list tools like memory_list_recent or memory_graph.

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 says to use this tool to re-find a room in a new session, giving concrete examples like 'what rooms do I have?' and 'resume the room with Olya'. It also contrasts it with creating or re-joining a room, making the intended use case and alternatives clear.

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 this tool read-only, non-destructive, and idempotent; the description adds valuable behavioral context beyond that: memory persists across sessions and across all connected AI tools, results are ranked by relevance or recency, and each result carries an id usable by memory_feedback. It also explains the correction workflow via memory_write, which helps an agent understand the broader memory ecosystem. No contradiction with 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 front-loaded with the core purpose and includes useful guidance, alternatives, and behavioral notes, but it is somewhat dense and includes a few sentences that could be trimmed (e.g., the 'ALWAYS check here first' emphasis could be shortened). Still, every sentence adds value, so it earns a 4 rather than a 5.

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 complexity (7 parameters, an output schema, and several siblings), the description provides the essential context for correct invocation: shared-memory semantics, search vs. list distinction, alternatives, and feedback routing. The output schema already covers return values, so the description need not explain them. Nothing an agent needs to select and call this tool correctly 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% and each parameter has a rich, self-explanatory description (e.g., domain, order_by, since/until). The tool description itself adds minimal parameter-level meaning—only re-mentioning order_by 'recency' in the context of output ranking. With the schema carrying the full semantic load, the 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 opens with a specific verb and resource: 'Search your long-term memory' and lists concrete example contents (user preferences, past decisions, project setup, people, earlier context). It names sibling tools (memory_list_recent, memory_feedback, memory_write) and distinguishes search from listing/writing, so an agent can tell it apart from memory_list_recent and memory_write without opening the schema.

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 explicit when-to-use guidance: 'ALWAYS check here first when you're unsure whether you already know something,' and a clear exclusion: 'no need to call it for general world knowledge you already hold.' It also directs agents to memory_list_recent for complete, bounded listings and to memory_write for correcting stale memories, providing concrete alternatives and conditions.

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 cover read-only, idempotent, and non-destructive behavior, so the description's 'Read-only — changes nothing' restates the schema. However, it adds valuable non-obvious context: the memory is shared across all AI tools connected to Mnemoverse. It also describes what the overview includes, providing behavior beyond what annotations specify.

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 sentences with no filler. It front-loads the operation and its main outputs, then adds the shared-memory context and usage guidance, ending with a clear read-only note. Every sentence contributes distinct 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?

For a zero-parameter read-only tool with an output schema available, the description covers the operation, the returned data categories, the shared-memory scope, and practical use cases. Nothing needed for correct invocation 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?

The tool has zero parameters and an empty input schema, so no parameter documentation is needed. The baseline of 4 applies because the description cannot add parameter meaning to a tool that takes no inputs.

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 ('Get an overview') and the resource ('stored memory'), then enumerates the specific outputs: total count, episode vs. prototype breakdown, learned associations, domains, and average quality scores. This is specific enough to be distinguished from all sibling tools such as memory_read or memory_write, even without naming 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?

The description provides explicit usage contexts: 'to orient yourself, to confirm the exact domain name before writing to it, or when the user asks what you remember.' This gives clear routing guidance, though it does not explicitly state when not to use the tool or name alternative tools beyond the implied writing context.

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 annotations, the description discloses that an importance gate may filter low-value writes and that the result indicates whether the memory was stored or filtered. It also warns about exact byte-for-byte domain matching creating separate permanent stores, which is critical behavioral context not available in the schema or 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 dense but every sentence carries operational value: outcome, triggers, exclusions, gate behavior, and content formatting guidance are all included. It is front-loaded with the core purpose, and the structured lists keep constraints scannable.

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 all essential dimensions for correct invocation: when to use, what not to store, how to format `content`, how `domain` matching behaves, how to reference rooms via memory_list_rooms, and what to expect from the filtering gate. With an output schema present and these details included, 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?

Even though schema coverage is 100%, the description adds significant meaning: the `domain` parameter's exact-match and namespace behavior, the `content` requirement to be a self-contained statement with examples, and `concepts` as link keys for related memories. This goes well beyond the schema's basic field 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 states a specific action — 'Store a long-term memory' — and clearly defines its unique scope: persistence across sessions and across multiple connected AI tools. It distinguishes itself from sibling tools like memory_read and memory_list_rooms by being the write destination for durable 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?

The description gives explicit when-to-use guidance: call proactively when the user states a preference, makes a decision, or reveals a durable fact, and don't wait to be asked. It also provides strong exclusions — never store passwords, API keys, payment data, MFA codes, government IDs, health records, or transient chatter — which helps an agent decide correctly.

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 readOnly, idempotent, and non-destructive annotations, the description adds meaningful behavioral context: secret values are never returned, listing is limited to aliases and purposes, and account scoping applies. This gives the agent a clear model of the tool's safety and output boundaries.

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 and well-structured: the core purpose is front-loaded, followed by the key value-scoping caveat, then a concrete usage example. Each sentence contributes distinct, necessary information without redundancy.

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 zero-parameter list tool with an output schema and strong annotations, the description is fully adequate. It covers what is returned, what is explicitly not returned, the intended use case, and the account scope, leaving no critical ambiguity for an agent selecting or invoking the 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?

The tool has zero parameters and schema coverage is 100%, so the baseline of 4 applies. There are no parameter semantics to add, and the description does not need to compensate for undocumented inputs.

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: listing secrets stored in the Mnemoverse Vault. It further specifies that results are by ALIAS and purpose only and explicitly notes that the secret VALUE is never returned, which distinguishes it from any value-returning counterpart.

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 says when to use the tool: to check which secrets the user has stored and under what alias, with a concrete example. It also sets expectations by stating only the user's account secrets are listed and that no tool on the connector returns the secret value.

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