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

With no annotations provided, the description carries the full burden. It clearly states it is read-only and does not modify stored data, and it discloses that orientation scores are structural checks, not guarantees of loaded context. This adds valuable context beyond the basic function, though it doesn't detail the output format or potential edge-case errors.

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 efficiently structured: the first sentence states the core purpose, the second sentence explains the data sources and operations, and the third provides workflow and caveats. Every sentence contributes meaningful 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?

Given the tool's complexity (multiple inputs, calculations, and output), the description covers inputs, actions, and output semantics (orientation score, open loops, captures). No output schema exists, so the description does well to mention what the report includes. It lacks explicit return format details, but the description is strong overall.

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 all three parameters are already well-documented. The description adds no additional parameter-specific details beyond what the schema provides, but it does reiterate the purpose of identity and transcript paths. This matches the baseline for high 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 states the tool generates a session orientation report, with a specific verb (generate) and resource (session orientation report). It distinguishes itself from sibling tools by detailing its unique function of reading identity, scanning memory, and calculating an orientation score, which none of the siblings perform.

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 provides a workflow directive: 'call boot_report first thing every session, before any substantive work.' This gives clear contextual guidance on when to use it. However, it does not explicitly mention alternatives or when not to use it, but none are needed given its unique role.

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?

Discloses file creation location, artifact equivalence with PreCompact, and optional parameters with auto-discovery. No annotations provided, so description carries full burden. Could mention idempotency or error handling but still provides solid behavioral context.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

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

Two sentences, no filler. First sentence clearly states action and output. Second provides usage and parameter notes. Perfectly front-loaded and 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?

Describes artifact and auto-discovery for parameters, but does not explicitly state return value or success indication. With no output schema, the description could mention what the tool returns (e.g., path to capture). Still, very informative overall.

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?

Input schema has 100% description coverage. Description adds value by noting that session_id and transcript_path can be omitted and auto-discovered, going beyond schema. Reason parameter is not elaborated in description but schema covers it. Extra context earns above 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?

Description states specific verb ('capture') and resource ('current session context') and target location ('.rekindle/captures/'). Clearly distinguishes from siblings like list_captures and read_capture, as this is the creation 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?

Explicitly states when to use: 'before a complex operation, when context feels at risk, or when the user requests it.' Also contrasts with automatic PreCompact hook. Lacks explicit 'when not to use' but positive guidance is strong.

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 most of it well: it declares irreversibility, that the FTS index entry is removed immediately, the blast radius (other memories and session records unaffected), and both success/failure return shapes. The one gap is the conditional write-provenance requirement, which is a real behavioral precondition disclosed only in the schema, not here.

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?

Front-loaded with the action and its irreversibility in the first two sentences, then usage, then return contract. Four sentences with essentially no padding; the 'Does not affect other memories or session records' line is the only one bordering on redundant given the singular-object framing.

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 destructive single-record deletion with no output schema, the description covers effect, reversibility, scope, trigger condition, and both return values — enough for an agent to call it safely. The only unaddressed element, provenance gating, is fully documented in the required schema field.

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%, including where to obtain the UUID and the full provenance contract, so the schema already does the heavy lifting. The description only adds the framing 'by its ID' and does not mention provenance at all, which is acceptable at the baseline but adds no surplus meaning.

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 and resource ('Permanently delete a single memory') plus the mechanism ('from the local SQLite database by its ID'), which cleanly distinguishes it from update_memory, store_memory, and search_memory. No ambiguity about what the 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?

Gives a clear trigger condition ('Use when a memory is outdated, incorrect, or no longer relevant') and a negative scope ('Does not affect other memories or session records'). It does not name an alternative for the common case of a memory being wrong rather than obsolete — e.g., pointing to update_memory when correction is preferable to deletion — so it stops short of full routing 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?

With no annotations, the description carries the burden well: it discloses that multiple rows are written across two tables, that each entry becomes a typed record with a default importance (constraints=9, warnings/relational_delta=8, decisions/open_loops/next_session_focus=7, preferences=6), and that session_id triggers a warning about unreviewed PreCompact captures. It does not discuss idempotency, duplicate handling on repeat calls, or error behavior on partial writes, which keeps it from a 5.

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?

Front-loaded with the purpose, then behavior, then workflow — a sensible ordering with no filler sentences. It is on the long side for a description, and the type/importance enumeration partly duplicates schema descriptions, but each sentence still carries information an agent needs.

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 12-parameter mutation tool with a nested object, no annotations, and no output schema, the description covers write targets, linkage, retrieval paths, and the one response caveat (unreviewed capture warning). What it omits is the actual response shape and failure/duplicate semantics, which matters somewhat since no output schema exists.

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 schema already documents all 12 parameters including the nested provenance object. The description mostly restates that shape (checkpoint required, the rest optional) and adds only marginal meaning beyond it, such as the fact that checkpoint is what boot_report loads next session. Baseline 3 is appropriate when the schema does the heavy lifting.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb and resource ('Capture a structured session handoff') and then enumerates exactly what gets written: one required checkpoint plus optional typed records and a session row. The typed-column / session_id linkage detail separates it cleanly from store_memory, which writes individual memories.

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 an explicit workflow directive — 'call end_session at the end of every substantive session' — which is clear positive guidance. It also names the downstream consumers (boot_report, search_memory, list_memories). It never says when NOT to use it or explicitly contrasts with store_memory for single-record writes, so the sibling routing is implied rather than 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?

No annotations are provided, so the description carries the burden. It reveals the behavioral context of PreCompact captures and the recovery workflow, but doesn't detail return format or side effects, which are minimal for a list operation. Adds value beyond a simple 'list captures'.

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, each earning its place: purpose, context, and workflow. Front-loaded with the primary action, concise with no fluff.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple listing tool with one optional parameter and no output schema, the description provides sufficient context for selection and invocation. It also integrates with sibling tools (boot_report, read_capture, end_session), making the workflow complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema covers 100% of the parameters, so baseline is 3. The description does not add any extra semantic detail about session_id; it is fully explained in 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 it lists PreCompact captures, with a specific verb and resource. It distinguishes from siblings like read_capture by focusing on listing, and adds context about preserving pre-compaction 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?

Explicit workflow is given: if boot_report lists PreCompact captures, call list_captures then read_capture to recover context before relying on the checkpoint; also check for unreviewed captures before end_session. This clearly indicates when to use the tool versus alternatives.

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 burden. It discloses read-only nature ('Read-only; does not modify any data'), ordering (newest first), and return fields. It does not cover potential pagination beyond the limit parameter or error handling, but overall provides solid 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 four sentences, front-loaded with the main action, and each sentence adds value. No unnecessary words or repetition. Highly 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?

Given the tool's low complexity (3 optional parameters, no output schema, no annotations), the description covers purpose, usage guidance, behavior, return format, and ordering. It is complete enough for an agent to understand how and when to use 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?

Schema coverage is 100%, so the description need not add much. It reinforces the ordering ('Newest memories are returned first regardless of limit') and lists return fields, which is helpful but not essential. The description does not significantly deepen understanding of the parameters beyond what the schema already provides.

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 'list', the resource 'memories', and the key distinction from sibling 'search_memory' by noting it does not require a query. It specifies the ordering (newest first) and return fields, making the 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?

The description explicitly states when to use: 'to browse what has been stored, audit memory contents, or check memory counts per category or project'. It also contrasts with 'search_memory' (requires a query). However, it does not explicitly state when not to use or provide alternative tools beyond the mention of search_memory.

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 details three modes with token cost implications ('summary' cheap, 'structured' moderate, 'raw' expensive) and advises using raw only when lighter modes are insufficient. This goes beyond a simple read, giving the agent actionable behavioral guidance without contradicting any structured metadata.

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 about five sentences and front-loaded with the core purpose, then provides mode details and a workflow. It is efficient and avoids redundancy, though it lacks bullet-point formatting that could improve scannability. No wasted 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?

The tool has a moderate complexity (2 params, 3 modes) and no output schema. The description gives enough context for the agent to understand what to expect from each mode and how to use the tool effectively, covering purpose, mode selection, cost, and workflow. It does not describe error cases or exact return structures, but these are not critical for basic 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%, so baseline is 3. The description adds meaningful extra semantics, especially for the mode parameter: it expands on the schema by explaining 'summary' as one paragraph, 'structured' as decisions/loops/warnings, and 'raw' as full transcript excerpt, plus cost guidance. This enriches the schema definitions.

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 'Read' and the resource 'PreCompact capture by ID', and specifically ties it to recovering context lost during mid-session compaction. This distinguishes it from sibling tools like list_captures or capture_now by focusing on reading a specific capture.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description says when to use the tool ('recover context lost during mid-session compaction') and provides a workflow: 'read recovered captures before relying on the latest checkpoint; start with the lightest mode that answers your question.' This gives clear context and usage strategy, though it does not explicitly mention alternatives to other sibling tools.

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?

Discloses key behaviors: uses FTS5, results ranked by relevance and importance, increments retrieval_count on matched memories, returns specific fields, and returns empty array on no match. This is comprehensive given no 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?

Concise and well-structured (~90 words), each sentence serves a purpose: purpose, ranking info, side effect, usage, return structure, and empty case. No 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?

Covers all essential aspects: search mechanism, ranking criteria, side effect (increment retrieval_count), return fields, and empty result handling. No output schema but description adequately describes return structure.

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 has descriptions for all parameters (100% coverage). The description adds value by explaining query semantics ('Broader queries return more results') and limit behavior ('Results are ranked by relevance and importance before truncation').

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 performs a full-text search on stored memories using SQLite FTS5, distinguishing it from sibling tools like list_memories (which likely lists all memories) and other CRUD tools.

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 usage scenarios: 'Use at session start to load relevant context, or mid-session to recall specific information.' While it does not explicitly exclude alternatives, the guidance is clear 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?

No annotations are provided, so the description carries the burden and does well: it discloses persistence across sessions, the auto-generated ID returned on success, and the non-deduplicating write semantics that would otherwise surprise a caller. It stops short of failure behavior or what happens to the memory lifecycle after storage.

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?

Front-loaded with the action and what is created, then use cases, then the return value, then the dedup caveat. Dense and mostly waste-free, though the use-case list and the sibling enumeration add a little 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?

For a 5-parameter write tool with a nested provenance object, no annotations and no output schema, the description covers the return value (generated ID), persistence, and the non-dedup gotcha. The conditional provenance requirement is left entirely to the schema, which is a minor residual 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?

Schema description coverage is 100%, so parameters are already fully documented, including the provenance object and the importance/category semantics. The description only references 'the provided content' and adds no additional guidance on project scoping or category selection beyond what the schema says, which is the correct baseline 3.

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+resource ('Store a new memory in the local SQLite database') and enumerates exactly what is created (UUID, timestamp, content). It also situates the tool relative to siblings by naming boot_report, search_memory and list_memories as the surfacing paths, so an agent can place it in the workflow without opening other schemas.

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 usage contexts ('preferences, lessons learned, project context, relationship notes, or general information worth remembering across sessions'). The dedup caveat implicitly signals that updating an existing memory is a different operation, but it never names update_memory or states when not to use this tool.

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 behavioral load and largely delivers: partial-update semantics, automatic updated_at, FTS index rebuild on content change, and both success and not-found return shapes. It omits the conditional write-provenance requirement and any authorization/permission expectations, which the schema alone covers.

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 patch semantics and the use-case sentence are front-loaded, and each sentence carries information (state model, auto-timestamp, index rebuild, return contract). It is on the long side — five sentences for a mutation tool — but nothing is 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?

A mutation tool with no annotations and no output schema, so the description must cover behavior and returns — and it does, including the not-found error object. The one gap is the nested provenance object's conditional requirement, which is pushed entirely to the schema and unmentioned in prose.

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 only the semantic that omitted fields are preserved and that changing content triggers a reindex; it does not clarify the importance scale, category enum choices, or provenance nesting beyond what the schema already documents.

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 and resource (update an existing memory) plus the patch semantics that define it, and explicitly contrasts with delete-and-recreate. An agent can distinguish it from store_memory and delete_memory without reading either 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?

Gives concrete use cases — correct inaccurate memories, adjust importance, reclassify category — and frames it as the alternative to deleting and re-creating. It does not explicitly say when to prefer store_memory instead (i.e., for new memories), so the routing guidance is clear but not exhaustive.

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