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

With no annotations provided, the description fully carries the behavioral disclosure burden. It clearly warns that deletion is permanent and cannot be undone, explains that dry_run defaults to True and must be set to False to actually delete, and describes the return value as a list of deleted or would-be-deleted IDs. This is exemplary transparency for a destructive 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 front-loaded with the purpose and permanence warning, followed by a concise bullet-style list of optional parameters, clear examples, and a return-value note. The redundancy of the permanence warning reinforces safety rather than cluttering the message. Every section earns its place.

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

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

For a destructive bulk operation with no annotations and a 0% schema-description coverage, the description is largely complete: it covers filters, dry-run behavior, irreversibility, examples, and return values. The main gaps are the undocumented 'verbose' parameter and the lack of explicit routing guidance against sibling tools like delete_all_memories.

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 0%, so the description must explain parameters. It does explain user_id, agent_id, run_id, metadata_type, older_than_days, and dry_run with helpful examples. However, the 'verbose' parameter in the schema is not mentioned at all, leaving one parameter unexplained.

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 a specific verb and resource: 'Delete multiple memories matching filter criteria.' It also highlights 'PERMANENT deletion,' which distinguishes it from read-only or reversible operations. The plural scope and filter-based behavior differentiate it clearly from singular delete_memory and define its role relative to bulk operations.

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 clear usage context via optional filter criteria, dry-run preview, and practical examples such as previewing test cleanup and deleting old memories. However, it does not explicitly state when to prefer this tool over delete_memory or delete_all_memories, leaving some sibling differentiation to inference.

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 behavioral burden and succeeds: it flags 'PERMANENT deletion' in bold, warns with '⚠️ Cannot be undone', discloses the scope-matching behavior, the auto-detection of the current user, and the return shape (confirmation with count). For a destructive tool, this is exemplary disclosure.

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 permanence warning leads, followed by scannable OPTIONAL bullets, two concrete examples, and a one-line return note. Every sentence earns its place, and the length is justified given how destructive the operation is.

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 4-parameter destructive tool with no annotations and an output schema, the description covers the essential invocation semantics well (scope, fallback, permanence, return). But it leaves real gaps: the verbose parameter is undocumented, it doesn't state what happens if both user_id and agent_id are provided, and it doesn't explain how it differs from bulk_delete_memories or delete_memory, which an agent could plausibly confuse it with.

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 0%, so the description must compensate, and it does for 3 of 4 parameters: it explains user_id and agent_id as alternative scopes ('OR'), run_id as a session/run scope, and the fallback when neither is provided. However, the 'verbose' parameter is entirely undocumented, which prevents a 5.

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

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

The description states a specific verb and resource: 'Delete all memories for a specific scope. PERMANENT deletion.' The scope qualifier (user/agent/run) makes the operation well-defined. However, it does not explicitly distinguish itself from the closely named sibling 'bulk_delete_memories' or the singular 'delete_memory', which cost it the top score.

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 clear invocation context via the OPTIONAL scope bullets and concrete examples ({"user_id": "john"}, {"run_id": "session_123"}), explaining how to select a scope and the auto-detection fallback. But it never says when to prefer this tool over siblings like delete_memory, bulk_delete_memories, or reset_memory, so tool-selection guidance is left to inference.

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 explicitly discloses the critical behavioral trait: 'PERMANENT deletion' and 'cannot be undone.' This clearly warns the agent about irreversibility, which is the most important behavior for a destructive 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 short and front-loaded: action, required parameter, and warning. Every sentence adds information, and the warning is placed prominently.

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 is simple and destructive, and the description covers purpose, required input, and irreversibility. The output schema exists, so return values are not the description's responsibility. Missing guidance about the verbose parameter and sibling differentiation prevents a perfect score.

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 0%, so the description must compensate. It explains memory_id as a required UUID, adding value beyond the schema. However, it completely ignores the second parameter 'verbose', leaving its effect undocumented.

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: 'Delete memory by its unique ID.' It clearly identifies the operation scope (single memory by ID) and distinguishes it from sibling tools like bulk_delete_memories or delete_all_memories, which operate on multiple 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?

The description clearly implies when to use the tool: when you have a specific memory_id and want to delete that one memory. It also states the required precondition (memory_id must be provided) but does not explicitly name alternatives or when-not-to-use conditions relative to sibling delete 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?

With no annotations available, the description carries the full behavioral burden. It discloses that by default a file is written to a specific path, that return_content changes behavior to return content, that user_id/agent_id auto-detect when omitted, and what the return value is. This is strong disclosure, though it doesn't mention side effects like overwriting or directory creation.

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

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

The description is well-structured and efficient: a one-sentence purpose statement, followed by compact OPTIONAL parameter lines and a short return/use summary. Every line adds value and the key behavior 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 tool with 7 optional parameters and no annotations, the description covers nearly everything an agent needs: the main behavior, defaults, return values, and common use cases. It is incomplete only in omitting the verbose parameter and not detailing potential file overwrite behavior, but overall it is robust enough for correct invocation.

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

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

Schema description coverage is 0%, so the description must compensate. It explains format values, auto-detection for user_id/agent_id, the default output path, include_metadata default, and return_content behavior. However, the 'verbose' parameter is never mentioned, which is a gap given that the schema provides no description for any parameter.

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

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

The description states a specific verb and resource: 'Export memories to JSON or Markdown format for backup or migration.' It clearly distinguishes export from the many read/search/mutation siblings by immediately naming the output formats and the use cases of backup and migration.

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 'Use for' line gives explicit contexts: backup, migration between backends, documentation, sharing. It does not explicitly say when to avoid this tool or mention alternatives like import_memories, but the intended usage is clear enough that an agent can decide when it is appropriate.

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

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

With no annotations, the Returns line provides the main behavioral disclosure: the operation yields complete memory details including content, metadata, and timestamps. The verb 'Get' implies a non-mutating read, but failure behavior, authentication requirements, and read-only guarantees are not stated.

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

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

Three short sections cover purpose, required parameter, and return value. The core action is front-loaded, the required argument is clearly emphasized, 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?

For a simple get-by-ID operation, the description covers the required input, summarizes the output, and the output schema fills in return structure. The main gaps are the unexplained verbose parameter and lack of explicit sibling routing, but neither prevents a correct 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 0%, but the description compensates for memory_id by specifying it must be the UUID of the memory to retrieve. The optional 'verbose' parameter is never mentioned, leaving its purpose to inference from the parameter name. This is partial compensation: strong on the required field, weak on the optional one.

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?

Opens with a specific verb and object—'Get specific memory'—and scopes it by 'unique ID', immediately distinguishing it from search_memories, list_memories, and get_recent_memories. The purpose is 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?

Makes clear this is the tool to use when the agent has a memory UUID and needs that exact record, explicitly marking memory_id as REQUIRED. It doesn't name exclusions or alternatives, but the ID-based scoping supplies clear 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?

Annotations are absent, so the description carries the burden of behavioral disclosure. It explicitly says the result is a 'chronological list of all changes', which tells the agent what to expect. It does not describe verbose effects or failure behavior, but the read-only nature is clear from 'Get' and 'Returns'.

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 with the required parameter. The 'REQUIRED' and 'Returns' labels make it scannable, though the memory_id line slightly repeats the earlier phrasing.

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

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

For a simple read tool with an output schema, the required ID and return style are sufficient for basic invocation. The only notable gap is the undocumented verbose parameter and lack of explicit error semantics, but neither blocks correct use.

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 0%, so the description must add meaning. It adds UUID semantics for memory_id and marks it REQUIRED, but it entirely omits the verbose parameter, leaving its effect unexplained.

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: 'Get change history for a specific memory by ID.' The phrase 'change history' cleanly distinguishes this from get_memory, which would return the current state.

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?

No guidance is given on when to use this tool versus siblings like get_memory or search_memories. There is no mention of alternatives, exclusions, or conditions that should trigger use.

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 must carry behavioral disclosure. It discloses auto-detection of the current user and what the response summarizes, which is useful. However, it never explicitly states that this is a read-only operation, nor does it mention any permissions or side effects, leaving some behavioral burden unaddressed.

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, scannable, and front-loaded with the core purpose. The optional parameter note, return summary, and use cases each occupy one short line with 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?

For a stats tool with an output schema, the description conveys scope, output categories, and use cases without needing to enumerate return fields. The only notable gap is the undocumented 'verbose' parameter and the implied but not enforced mutual exclusivity of user_id and agent_id.

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 0%, so the description must compensate. It explains user_id and agent_id as optional filters with auto-detection fallback, adding real meaning. However, the 'verbose' parameter is not mentioned at all, leaving one of three parameters semantically undocumented.

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 opens with 'Get statistics about stored memories,' a specific verb and resource that clearly differentiates it from sibling list/get/search tools. It also states the return categories (counts by metadata type, date ranges, storage summary), reinforcing the purpose. It does not explicitly name a sibling, so it stops short of 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 provides explicit use cases: 'Understanding memory usage, identifying cleanup opportunities, monitoring growth.' It also gives scoping guidance for user_id/agent_id and the auto-detection fallback. It does not state when not to use it or explicitly compare with alternatives such as search_memories or list_memories.

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 behavioral disclosure burden. It communicates that the operation is a read ('Get'), that results are sorted newest first, and that user/agent detection happens automatically when neither ID is supplied. This is meaningful behavioral context beyond the schema.

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: a clear purpose sentence, a compact optional-parameter list, and a return statement. Each line adds value, though the repeated 'OPTIONAL:' prefix is slightly redundant and could be trimmed without losing clarity.

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 the tool's main behavior, key parameters, and return ordering. An output schema exists, so detailed return fields are not required here. The main gap is the undocumented 'verbose' parameter and the lack of explicit guidance on choosing among sibling memory tools.

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 0%, so the description must compensate. It explains days, limit, run_id, user_id, and agent_id with defaults and mutual exclusivity, adding real semantics. However, it omits the 'verbose' parameter, so parameter documentation is incomplete.

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: 'Get recently added or updated memories.' It also states the output ordering ('sorted by newest first'), which clearly separates this tool from siblings like get_memory, list_memories, or search_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?

The phrase 'for session continuity' gives some usage context, but the description does not explicitly say when to use this tool instead of list_memories, search_memories, or get_memory. The intended selection criteria are mostly implied by the tool name and the word 'recently.'

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 behavioral disclosure burden. It communicates the action and what will be returned ('Backend type, connection status, and basic diagnostics'), but it does not explicitly say that the tool is read-only or describe side effects. The check/connectivity wording implies safety but leaves the guarantee 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 three short, front-loaded sections: action, return value, and use cases. Every sentence contributes useful information and there is no redundancy 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 parameterless, simple diagnostic tool, the description is complete: it states what is checked, what is returned, and when to use it. The presence of an output schema covers detailed return-field definitions, so nothing necessary 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 the schema is complete and empty. Per the baseline for no-parameter tools, the description adds sufficient context by outlining the returned health-check details.

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-resource pairing: 'Check the health and connectivity of the memory backend.' It clearly defines a diagnostic operation and is immediately distinguishable from all sibling tools, which handle memory/storage operations.

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 'Use for:' section explicitly lists two intended scenarios: 'Troubleshooting connection issues, verifying configuration.' None of the sibling tools cover this diagnostic purpose, so the tool is uniquely positioned for these cases.

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 burden and covers meaningful details: skip_existing semantics, infer defaulting to false for speed, user/agent override, and a return summary. It does not discuss potential side effects when skip_existing is false, but the additive bulk-import nature is reasonably clear.

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 clearly front-loaded with REQUIRED/OPTIONAL sections followed by examples and return info. Minor redundancy appears because 'Import memories from exported JSON file' is restated later as 'Imports memories from a JSON file created by export_memories tool,' so it is not maximally tight.

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 file-based bulk import with an output schema, the description covers purpose, source format, parameter semantics, use cases, and examples. The undocumented verbose parameter is the main omission, but it is a minor behavioral flag and likely self-explanatory.

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 0%, and the description compensates well by explaining file_path, user_id/agent_id, skip_existing, and infer with defaults and examples. The only schema parameter not mentioned is verbose, which leaves a small but real gap.

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 opening line uses a specific verb ('Import') and resource ('memories') and identifies the source format ('exported JSON file'). It clearly differentiates from siblings like export_memories or store_memory by framing this as a bulk file-based restore operation.

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 names the companion tool (export_memories) and lists concrete scenarios: backup restoration, migration between backends, and sharing memory sets. It does not explicitly state when to avoid using it, such as preferring store_memory for single additions, so it stops short of full when/when-not routing.

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 disclosure burden and does substantial work: it states automatic chunking and storage, auto-detection of the current user when user_id/agent_id are absent, default values, the min/max constraints on chunking, and the performance rationale for infer=False. It omits idempotency/overwrite behavior and failure modes, but for a no-annotation tool this is strong coverage.

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 section earns its place: purpose, supported formats, parameter spec, examples, and return value. It is front-loaded with the core purpose and uses a clear label-per-line structure. Some redundancy exists between the parameter list and the examples, but given 10 parameters at 0% schema coverage, the length is justified.

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 10-parameter tool at 0% schema coverage, the description covers formats, all key parameter semantics, defaults, examples, and a brief return summary. An output schema exists, so deep return-value documentation isn't required. Remaining gaps — the undocumented verbose flag, no error conditions, no note on whether re-ingesting the same path overwrites or duplicates — are minor rather than blocking.

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 0%, so the description must fully compensate, and it does: it documents 9 of 10 parameters with defaults, ranges, and conditionals — including min/max constraints on chunk_size and chunk_overlap that the schema itself lacks, and the user_id OR agent_id fallback logic. Only 'verbose' is left undocumented, a minor miss against otherwise exceptional compensation.

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: 'Ingest documents into memory as a knowledge base using LlamaIndex.' This clearly distinguishes it from sibling tools like search_memories, get_memory, and delete_memory, and its document-chunking role is distinct from store_memory. The supported format list reinforces the scope.

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?

Four concrete examples (single file, directory, metadata, custom chunking) plus REQUIRED/OPTIONAL parameter guidance give an agent clear invocation context. However, it never explicitly contrasts with alternatives like store_memory or import_memories, so an agent must infer when document ingestion is the right sibling choice.

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 must convey behavioral traits itself. It usefully discloses read-only listing, auto-detection, pagination default, run_id filtering, and creation-date sorting. However, it omits sort direction, behavior when both user_id and agent_id are provided, and the meaning of the 'verbose' flag.

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 organized: a plain-language first sentence, scannable OPTIONAL bullets for parameters, and a final returns line with ordering information. Every line adds value and nothing is redundant.

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 an output schema exists, the description does not need to explain return values in prose. It covers scope, filters, pagination defaults, and ordering. Minor gaps remain around 'verbose' semantics and user_id/agent_id exclusivity, but the tool is still actionable.

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 0%, so the description must compensate for the bare schema. It adds meaningful semantics for user_id/agent_id auto-detection, run_id filtering, and page_size default. The only parameter left completely unexplained is 'verbose', which keeps this from a perfect score.

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 ('List'), resource ('memories'), and scope ('for a user or agent with pagination'), and closes with a concrete result statement. It does not explicitly differentiate itself from sibling tools like search_memories or get_recent_memories, so it stops short of 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 clear usage context: list all memories for a user/agent, with optional filters and auto-detection of the current user when no scope is provided. It does not mention when to prefer an alternative tool, but the intended scenario is well 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?

With no annotations provided, the description carries the full burden of behavioral disclosure. It clearly communicates that the operation is destructive, affects all memories globally, is irreversible, and returns a confirmation of reset. This is comprehensive and unambiguous.

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 well-structured, with the destructive warning front-loaded and critical details in short, scannable sentences. Every sentence adds essential information without filler.

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

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

For a zero-parameter tool with an output schema, the description covers purpose, scope, when to use, irreversibility, and return behavior. No critical information is missing, making it complete for an AI agent to decide whether and how to invoke it.

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 there is no parameter semantics to explain. The baseline of 4 is appropriate because the description cannot add meaning beyond the empty schema, and nothing is missing.

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 'Reset the entire memory store' and labels it a 'DESTRUCTIVE operation,' giving a clear verb and resource. It further specifies that it deletes 'ALL memories in the backend, regardless of user/agent/app,' which distinguishes its global scope from targeted sibling tools like delete_memory.

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: 'Only use this for testing or when you need to completely clear the memory store.' It also warns that the operation cannot be undone. It does not explicitly name alternatives or contrast with delete_all_memories, but the usage boundary is 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?

With no annotations provided, the description carries the full behavioral burden. It discloses useful behaviors: auto-detecting the current user when user_id/agent_id are absent, AND-style matching across filters, pagination behavior, and backend-specific filtering strategy. It does not explicitly state read-only semantics, but the word 'search' strongly implies it and no side effects are suggested.

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-organized with a clear opening sentence, grouped OPTIONAL parameter bullets, a returns line, concrete examples, and a backend note. Every part serves a purpose and the structure makes the information easily scannable for an agent.

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 9 optional parameters and no schema descriptions, the description is nearly complete: it explains filters, pagination defaults, user/agent scoping, and example invocations. The main gap is the omitted 'verbose' parameter, which prevents full self-sufficiency in all contexts.

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 0%, so the description must compensate. It documents nearly every parameter with types, defaults, constraints, and examples, adding substantial meaning beyond the raw schema. However, the 'verbose' parameter is not mentioned at all, leaving one parameter undocumented.

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: 'Search memories by metadata fields' and immediately distinguishes the tool from semantic search. This makes its role clear and separates it from siblings like search_memories without needing to inspect 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?

The phrase 'without semantic search' clearly implies the intended use case: metadata-based lookup rather than semantic similarity. It gives usage examples for common filters, though it does not explicitly name an alternative tool for semantic search or state 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 provided, the description carries the full behavioral burden and does so excellently. It explains FAISS L2 distance scoring, the relevance threshold default of 1000, the MEM0_MAX_RELEVANCE_SCORE configuration, auto-detection of the current user when neither user_id nor agent_id is supplied, and the limit/page_size interaction.

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 well-structured, front-loading required and optional parameters before adding examples and score interpretation. Every section serves a purpose: parameter semantics, score guidance, and usage context. The formatting with bold labels makes it easy for an agent to parse.

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, eight parameters, and existing output schema, the description is thorough. It covers filtering behavior, pagination, user scoping, examples, threshold configuration, and practical use cases. The only minor omission, 'verbose', does not prevent correct invocation since it is an optional boolean with a schema default.

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 0%, so the description must add meaning to the raw parameter schema. It does this well for query, user_id, agent_id, run_id, limit, page, and page_size, including defaults and behavior. However, the 'verbose' parameter is not explained at all, leaving one parameter without semantic context.

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 memories with semantic similarity and relevance filtering." This clearly distinguishes it from sibling tools like list_memories and search_by_metadata by specifying the semantic similarity mechanism.

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 parameter expectations (REQUIRED vs OPTIONAL), tangible examples, and a 'Use for' section naming concrete scenarios like finding code, recalling preferences, and retrieving project context. It does not explicitly compare against sibling alternatives such as search_by_metadata, 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 no annotations provided, the description carries the full behavioral burden. It states the file path created, what the file instructs the AI to do, and the return value. It does not mention behavior if the file already exists or whether it overwrites, but for a simple setup tool the core behavior is disclosed well.

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: purpose first, then file details, then the optional parameter, then the call timing, then the return value. Every sentence adds useful 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 low-complexity setup tool with one optional parameter, the description covers the what, where, when, and return value. The only notable gap is the lack of guidance on repeated calls or behavior when the steering file already exists. Overall, it is sufficiently complete for an agent to invoke 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 0%, so the description must compensate. It explains 'workspace_path' as 'path to workspace root (default: current directory)', which adds meaning beyond the raw schema. It could go further by clarifying how workspace_path affects the file location, but the parameter's role is reasonably clear.

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: it creates a steering file at a specific path (.kiro/steering/memory-first.md) with a defined role. This is distinct from the sibling memory CRUD and health-check tools, so an agent can identify that this is a one-time setup operation.

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 trigger condition: 'Call this once after adding the MCP server to enable memory-first workflows.' It does not explicitly discuss when not to use it or name alternatives, but the one-time setup context is clear and distinguishes it from the ongoing memory operations in the sibling list.

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, and it delivers. It discloses auto-detection of user/agent when neither is supplied, LLM-based fact extraction and deduplication behavior, expected latency differences (5-15s vs 1-2s), environment configuration defaults, and return-behavior compact vs verbose. This is rich, non-obvious 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?

Despite covering eight parameters plus examples and configs, the description is organized with clear REQUIRED/OPTIONAL sections, examples, use-case guidance, and notes. Every section earns its place; no filler or repeated schema metadata.

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 a high-parameter tool, zero schema descriptions, and no annotations, the description is remarkably complete. It covers parameter semantics, edge-case behavior, configuration, performance tradeoffs, and return values, leaving an agent with everything needed to invoke the tool correctly.

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

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

Schema description coverage is 0%, so the description must compensate for all 8 parameters, and it does. It explains the conditional user_id/agent_id requirement, content as required, metadata as a JSON object, infer and verbose booleans, run_id for session scoping, and custom_instructions for per-call control, supplemented with concrete input examples.

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 opens with a specific verb and resource: 'Store memory content with metadata support.' It explicitly lists use cases ('storing code patterns, user preferences, project details, technical knowledge'), clearly distinguishing the write-focused intent from the many retrieval/manipulation sibling tools such as search_memories, get_memory, and delete_memory.

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 concrete when-to-use context with examples and a 'Use for' list, and it explains the infer=True vs infer=False tradeoff. It does not explicitly name alternatives or when-not-to-use conditions, but the context is strong enough for an agent to select this tool for storing rather than retrieving or deleting memories.

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 explains that the update is direct, bypasses LLM inference and deduplication, and preserves memory history. It also clarifies that metadata is replaced, not merged. This is meaningful behavioral context beyond the schema, though it does not cover error cases or permissions.

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 with clear REQUIRED/OPTIONAL sections, a contrast to the sibling tool, concrete examples, and a return-value note. Every sentence adds value and the most important information 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 tool with 4 parameters and no annotations, this description covers the main behaviors, parameter semantics, and purpose well. Examples and the store_memory contrast further help an agent. It falls short only by omitting the verbose parameter and not addressing failure behavior when the memory_id does not exist.

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 0%, so the description must compensate. It documents memory_id, content, and metadata well, including the UUID type and the 'replace existing metadata' semantics. However, the 'verbose' boolean parameter is completely undocumented, leaving the agent without guidance on an available input. The phrase 'content and/or metadata' is also slightly misleading since content is required by 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 uses a specific verb ('Update') with a clear resource ('existing memory') and explicitly states what can be modified ('content and/or metadata'). It also names the key alternative, store_memory, and distinguishes this tool from it, so an agent can tell them apart immediately.

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 clearly contrasts this tool with 'store_memory with infer=true', noting that update_memory works directly without LLM processing or deduplication. This gives an agent a clear sense of when to use it. It could be slightly more explicit about when not to use it, but the contrast is strong enough.

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