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

With no annotations, the description carries the transparency burden. It discloses immediate server-side storage, batch processing during consolidation, and detailed deduplication behavior (same-session immediate drop, cross-session rejection at consolidation). It adequately conveys side effects and timing without hiding any obvious operational details.

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 spans three paragraphs and repeats deduplication information across the second and third paragraphs. While structured and readable, it could be tightened by merging overlapping statements without losing key guidance.

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 lack of an output schema, the description appropriately focuses on input semantics, storage behavior, and deduplication. It covers the asynchronous consolidation aspect and references get_schemas for domain exploration, making the tool's behavior fully understandable in context. Minor gaps like error handling or return behavior are absent but not critical here.

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 meaningful extra guidance beyond the schema, such as explaining that domain_hint is not a fixed list (referencing get_schemas) and that importance is high for medical/safety versus low for casual preferences, which clarifies parameter usage beyond the short schema 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 'Store a durable fact worth remembering across sessions' with a specific verb and resource, and explicitly distinguishes from ephemeral statements. It effectively differentiates this write tool from sibling query and event tools by focusing on stable 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?

Provides explicit when-to-use guidance ('Call this proactively whenever you learn something this store should keep') and when-not-to-use ('Ignore ephemeral statements'). It also reassures frequent use with latency and deduplication notes, though it does not directly compare to alternative sibling tools like log_event.

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 and does substantial work: it discloses the 50-line extraction cap, the 'oldest unexamined lines' ordering, the events_remaining result field, and the all/limit overrides. However, it does not explain whether integrating duplicates and contradictions mutates or destructively merges knowledge-graph data, which is a meaningful gap for a tool that 'integrates' content.

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 structure is sensible: purpose, then when to call, then cap behavior and parameters. The opening fragment 'Copy, extract, and integrate.' is slightly redundant with the fuller sentence that follows, but every other sentence earns its place and the critical batch-limit detail 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 no annotations and no output schema, the description compensates well by mentioning events_remaining and explaining partial-processing semantics. Remaining gaps are the undefined 'named sources' and no clarification of how this relates to capture_fact as an alternative ingestion path, but the core operational picture is complete enough 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 coverage is 100%, so the baseline is 3. The description adds value beyond the schema by explaining all: true means taking the 'whole backlog in one call' and limit: N means 'the oldest N,' tying both parameters to the 50-item cap behavior described in prose.

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 concrete pipeline with specific verbs and resources: copies new lines from named sources, extracts candidate facts, and integrates pending facts into domains, entities, duplicates, contradictions, and the knowledge graph. This clearly distinguishes it from siblings like log_event, capture_fact, and get_events, though the vague phrase 'named sources' leaves some ambiguity about where inputs come from.

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 trigger conditions are given: 'after capturing several facts, at a topic change, or before the conversation ends.' This gives clear when-to-use context, though it does not name alternatives or state when not to use the tool, which would fully earn 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?

No annotations are provided, so the description carries the full burden. It implies a read-only operation through 'Get everything known' and 'traversal,' but does not explicitly state the absence of side effects, auth requirements, or rate limits. Given the tool name and context, a slight inference is needed, but it is acceptable.

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 into two paragraphs: the first explains functionality, the second provides usage guidance. Every sentence contributes necessary information, and there is no fluff 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?

No output schema is present, so the description need not specify return format. It adequately describes the scope of results (facts and connections) and provides enough context for an agent to decide when to invoke the tool. Slight ambiguity remains about the exact structure of the returned data, but it is not critical.

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 description for 'topic' is present and clear ('Topic, person, project, or domain to explore'), providing 100% coverage. The tool description further elaborates on the parameter by relating it to the tool's purpose, adding moderate value beyond 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's purpose: 'Get everything known about a topic, combining search with entity relationship traversal.' It also explicitly differentiates from search_knowledge, making the tool's unique value obvious.

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 when-to-use guidance: 'Call this when you need the COMPLETE picture...' and 'Prefer search_knowledge when you want one fact fast; prefer this when missing a connection would make your answer wrong.' This leaves no ambiguity about the appropriate scenarios.

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 fully discloses edge cases: multiple types sharing a name return combined facts, punctuation normalization rules, missing names returning mention facts with is_subject false, and the definition of 'found'. This gives the agent a precise behavioral model.

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?

While long, every sentence adds essential information about behavior, edge cases, or parameter interaction. The main purpose is front-loaded, and subsequent details are logically organized, earning their place 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?

Given no output schema, the description explains return semantics (facts, is_subject, found) sufficiently. It also addresses ambiguity, missing entities, and type variance, making the tool's behavior fully predictable for an agent.

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 descriptions cover both parameters thoroughly (name resolution guidance, type as a disambiguation filter). The description reinforces type behavior ('facts from all of them come back') and adds context on resolving indirect references, slightly exceeding the high coverage baseline.

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 a specific verb ('Get') and resource ('everything known about a named thing'), and clearly distinguishes itself as the 'tell me about X' tool. It contrasts implicitly with sibling tools by focusing on entity retrieval with fuzzy matching and is_subject semantics.

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?

Provides explicit when-to-use guidance: 'Call this WHENEVER a named thing is mentioned or alluded to' and 'Call it before advising on anything involving that thing, and before asking who or what something is.' It also explains behavior for missing entities and ambiguous names, reducing guesswork.

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. It transparently describes what the tool returns (raw episodic record in sequence order) and the kind of content included. It does not mention side effects, but this is a read-only retrieval operation and the description makes that implicit.

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 with no redundant information. The first sentence states purpose and return content; the second provides usage guidance. 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 tool with 3 simple parameters and no output schema, the description is complete: it explains what is returned, when to use it, and differentiates from sibling tools. No additional context is needed for an agent to invoke 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% and each parameter already has a clear description (limit, session_id, after_sequence). The tool description itself adds no extra parameter semantics beyond the schema, so the baseline of 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?

States a specific verb ('Retrieve events') and resource ('events from the current or a previous session'). Clearly differentiates from sibling tools by describing the raw episodic record (messages, tool calls, tool results, artifacts) rather than context or 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?

Explicitly tells when to use the tool: 'Use this to recall what happened earlier in a conversation (especially after context compaction), or to review a previous session.' This gives clear situational guidance without ambiguity.

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 does this well by revealing that the domain set is not fixed, grows to fit the user, and reflects domains actually used — non-obvious behavior that matters for an agent deciding whether to cache or assume domains. It does not mention return format or explicit read-only status, but the verb 'List' makes the basic behavior clear for a zero-parameter tool.

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 and front-loaded. The first sentence states exactly what the tool does, the second explains why the result may vary, and the third gives concrete use cases and alternatives. Every sentence earns its place without repetition or 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?

For a simple, zero-parameter listing tool with no output schema and no annotations, this description provides everything an agent needs: what is returned, why the result is not static, when to call it, when not to call it, and which sibling tools to use instead. There are no significant gaps for correct selection and 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?

There are zero parameters and the schema description coverage is 100%, so the schema fully documents the parameter surface. The baseline for a no-parameter tool is 4, and the description appropriately adds no unnecessary parameter-level detail.

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 uses a specific verb and resource: it explicitly says the tool will 'List the knowledge domains this user's memory actually uses, and their subdomains.' This clearly distinguishes it from sibling retrieval tools like search_knowledge and get_context, which retrieve content rather than enumerate the domain vocabulary.

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 guidance on when to call this tool: before filtering a search by domain, before choosing a domain_hint for capture_fact, or when wanting to understand how the user's knowledge is organized. It also explicitly states when it is not needed — rarely needed mid-conversation — and names alternatives that work without it: search_knowledge and get_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?

No annotations exist, so description carries full burden. It discloses the two return payloads (briefing and unconsolidated facts), which is important context. However, it doesn't state side-effect freedom or any limitations (e.g., staleness, performance), and the read-only nature is only implied by the name.

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 tight but not terse: first paragraph states the mandatory use case and equivalence to memory://briefing; second paragraph adds the fact-dedup rule. Every sentence carries operational meaning, and the most important usage instruction 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?

For a one-optional-param getter with no output scheema, it covers what, when, and why, plus a dup-avoidance nuance. It stops short of describing the response format of the briefing or facts, but an agent aware of memory://briefing has enough to proceed. No side-effect or error info is necessary for this simple call.

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 covers 100% of the single parameter with 'Session to query. Omit for the current session.' The description adds no extra param detail beyond referencing 'this session'; thus 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?

Description uses specific verb 'returns' and names the resource: session context / working briefing. It clearly explains the tool returns the same briefing as memory://briefing plus unconsolidated session facts. It doesn't explicitly differentiate from sibling get_context, so not a 5.

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 an explicit when: at the start of every conversation before answering, unless memory://briefing already loaded. It also gives a specific trigger for the facts portion: call before re-capturing a fact that may already be stored. This is direct, actionable guidance with an exclusion condition.

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 of behavioral transparency, but it never explicitly states that this tool is read-only and has no side effects. It clarifies the meaning of fields and that token fields are omitted rather than zero, but it stops short of confirming that calling get_stats does not modify data or trigger background processes.

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 strictly necessary, but the extra detail is purposeful and well structured with inline-code field names and separate paragraphs for each metric. It front-loads the main purpose and then handles potential confusions like embeddings versus intelligence, which justifies the length.

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?

Since there is no output schema, the description provides a thorough explanation of the statistics returned, including edge cases such as omitted token fields, unlimited token budgets, and behavior over the cap. This gives enough context for an agent to understand what the results mean without needing a formal output schema.

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 the schema is complete, so there are no parameter semantics left unexplained. The baseline for zero parameters is 4, and the description correctly adds no irrelevant parameter information.

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: 'Get knowledge base statistics' and enumerates exactly which statistics are returned. It also differentiates the tool from siblings like search_knowledge, get_entity, and get_context, making its purpose unmistakable.

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 call the tool: when the user asks what is known, how much is stored, or whether memory is working. It also names alternatives for actual recall, saying to use search_knowledge, get_entity, or get_context, which gives clear 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?

The description discloses that it records raw exchanges, that duplicates are reconciled at consolidation, and that it is for episodic memory. It also clarifies the distinction from fact storage, giving a clear behavioral picture despite lacking 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 concise, using three short sentences that sequentially cover purpose, usage conditions, and alternative tool. It is well-structured and free of unnecessary detail.

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 gives sufficient context for an agent to decide when to invoke the tool, what parameters to fill, and how it differs from related tools. It even mentions automatic logging when hooks exist, which is important operational context.

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?

All 7 parameters have descriptive names and schema-level explanations. The tool description adds practical guidance for ambiguous cases, such as using the 'speaker' parameter for named participants and defaulting content_type to 'text'.

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 records a raw exchange for episodic memory, using the verb 'record' and specifying the resource (raw exchange). It also differentiates from capture_fact and explains its role in consolidation.

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 is provided: call when no hooks are configured or when an exchange matters enough to preserve verbatim; otherwise, it is automatic. It also notes duplicates are safe and directs to capture_fact for storing facts instead.

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 full behavioral disclosure, and it delivers: it explains the three return fields, the reliability difference between results and pending, the conditions under which episodes is filled, and the semantic-vs-keyword matching behavior. It also cautions that episodes should not be reported as integrated facts. This is exemplary 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 long but every sentence earns its place: purpose is front-loaded, usage guidance follows, then return semantics are explained in a structured, scannable way. The prose moves from what to do first, to what comes back, to how to interpret edge cases. 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?

There is no output schema, so the description must explain what the tool returns — and it does so thoroughly, covering results, pending, episodes, and the semantic-search distinction. It also addresses the prerequisite relationship with get_session_context, making the tool safe and effective to invoke in context. Given the tool's complexity, this is 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 the baseline is 3. The description adds meaningful nuance beyond the schema: it explains that query may be a paraphrase when semantic search is enabled, and that domain biases ranking rather than filtering, reinforcing the schema's own explanation. This helps an agent form better queries even though the schema already documents both parameters 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 description opens with a clear verb-resource pair: 'Search the knowledge base.' It then distinguishes its purpose from related operations by detailing what it returns (integrated knowledge vs pending vs episodes), which sets it apart from siblings like get_context and get_session_context. The role of the tool as the pre-answer retrieval step is explicit.

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 the tool ('Call this BEFORE answering questions that might benefit from what this store knows') and gives a concrete prerequisite: call get_session_context or load memory://briefing first. It also explains how to interpret results when deciding whether to rely on pending vs results, giving clear operational guidance.

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