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

No annotations are provided, so the description carries the full behavioral burden. It discloses that the tool is slower (seconds-level), uses litellm GwV4F, and produces answers by synthesizing all memories. It doesn't mention error handling or side effects, but for a consultation tool this is adequate. It adds value beyond the schema by noting the performance characteristic.

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, front-loaded with the core purpose and then providing usage guidelines with bold for emphasis. It is concise, well-structured, and every sentence earns its place. The key constraints (when to use, speed) are highlighted effectively.

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 2-parameter tool with no output schema, the description provides all necessary information: what it does, when to use it, how it differs from siblings, and its performance characteristics. An agent can confidently decide and call this tool correctly based on this description alone.

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%, meaning both parameters (query and agent) are already described in the schema. The description adds no additional semantic detail about parameters, so the baseline score of 3 applies. It doesn't compensate for any gaps because there are none.

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: an AI-to-AI consultation that answers questions based on all memories, giving complete, accurate, and colloquial answers. It explicitly distinguishes from siblings by naming mh_tool for standard answers and mh_search for raw memory fragments, making the unique role 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?

It gives explicit when-to-use guidance: only when mh_tool lacks a ready-made answer and the question requires understanding and synthesis. It also states when not to use it, directing to mh_tool for standard answers and mh_search for raw memory, with speed comparisons. This leaves no ambiguity for the agent.

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 burden. '取' and '完整内容' make clear this is a read operation returning the full memory, but it does not mention behavior for missing ids, output format, permissions, or potential side effects. For a simple get operation this is acceptable but not rich.

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 entire description is one short, front-loaded sentence with no filler. It conveys the key action, object, and selection criterion efficiently.

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 single-id fetch operation, the description covers the essential action. However, with no output schema and no annotations, it leaves open the exact return structure and edge-case behavior (e.g., non-existent id). The description is minimally viable but not fully self-sufficient for an agent operating without additional 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?

The schema documents 'id' as a required UUID but provides no description (0% coverage). The description adds that the id selects a single memory, which is useful, but beyond that it does not elaborate on the parameter's meaning or constraints. The parameter is simple enough that the schema plus minimal reference is adequate.

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 clear verb ('取' / get) and a specific resource ('单条记忆完整内容' / full content of a single memory), and explicitly ties retrieval to an id. This distinguishes it from sibling tools like mh_search (query-based) and mh_write/mh_update (mutating 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 implies when to use the tool: when you have a known id and need the complete memory record rather than search results. However, it does not explicitly contrast with mh_search or state when not to use it, leaving the routing partially implicit.

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 burden and does it well: it discloses hybrid retrieval (vector semantics + metadata filtering) and a non-obvious default exclusion (nssm service ledger not returned unless source is specified). It stops short of explicitly declaring read-only behavior, but 'search/retrieve' plus the absence of side-effect language makes that 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?

Three dense sentences with no filler: the core purpose is front-loaded, and each subsequent sentence delivers either routing guidance or an important retrieval caveat. The warning about standard questions is placed immediately after the purpose, which is exactly where an agent needs it.

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 an 8-parameter search tool with no output schema, the description covers the essential context: what is returned, when to use it, how the retrieval works, which metadata filters apply, and an important default-exclusion gotcha. An agent has enough to decide whether to call this tool and how to route alternative requests.

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 only 38%, so the description must compensate. It does so by identifying layer, category, source, tags, and time as metadata filters and giving a concrete example for source. It does not explain layer's k/m enum or top_k semantics, but the added context goes meaningfully beyond the sparse 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?

States a clear verb and resource: retrieve the boss's unified memory and return raw entries, specifically suited for historical details, concrete events, and full accounts of past pitfalls. It further distinguishes itself from curated answer tools like mh_tool and mh_tools_list, so an agent can tell them apart.

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

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

Explicitly describes when to use the tool (historical details, specific events, full stories of pitfalls) and when not to (standard questions about the boss, preferences, environment, services, ports, project changes), routing those to mh_tools_list → mh_tool. It also gives a concrete alternative path for nssm service ledger queries by specifying source='agent-matrix'.

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 and it discloses key behaviors: exact-name matching rather than semantic search, and a list-all fallback when 'name is empty. It also previews return content including startup parameters, port, and path, which goes beyond a generic query description.

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

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

Two sentences front-load the core operation and then provide the key behavioral caveat and fallback instruction. There is no filler or redundant restatement of structured fields.

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 tool with one optional parameter and no output schema, the description is complete: it tells the agent what is returned, how matching works, and what to do when the name is unknown. Nothing necessary for correct invocation is missing.

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

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

Schema coverage is 100%, so the baseline is 3, but the description adds real value by clarifying that the name must be exact and that omitting it triggers a full list, which is more specific than the schema's '关键词' phrasing. This helps the agent use the single parameter correctly without opening 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 states a specific verb ('查') and resource ('本机 nssm 服务台账'), and further scopes behavior to exact-name matching with a non-semantic-search caveat. This makes it clearly distinguishable from semantic-search siblings like mh_search.

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

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

It explicitly instructs the agent to omit 'name' when the exact service name is unknown, resulting in a list-all-services fallback, and states that exact matching is required. It does not explicitly name alternative tools for fuzzy/semantic lookup, but the '不走语义检索' caveat implies that boundary clearly enough.

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

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states what metrics are reported but does not explicitly say the operation is read-only, whether it has side effects, what permissions are needed, or how the data is returned. The read-only nature is only inferred from 'status'.

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 a single compact sentence that front-loads the tool's purpose and immediately lists the six key metrics. There is no redundant filler, and every element contributes to understanding what the tool provides.

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

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

The description covers what metrics are included, but with no output schema and no annotations, it leaves out details about the return format, data types, and whether the operation is strictly read-only. For a simple status tool it is adequate, but it is not fully complete for an agent needing to interpret the response reliably.

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

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

The tool has zero parameters, so the baseline is 4. The description adds meaningful context by listing the categories of data the no-argument call will return, which helps the agent understand the call's output even though there is no parameter schema to enrich.

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 identifies a specific resource ('openmem 健康状态') and enumerates six concrete metrics it reports (总条数/钉住数/归档数/待审冲突/工具数/咨询次数). It goes beyond the bare tool name, though it lacks an explicit verb. It is reasonably distinguishable from sibling tools like mh_write or mh_search because it clearly denotes a status/overview 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 phrase '健康状态' implies this tool is used for health/status checks, but the description provides no explicit when-to-use guidance, exclusions, or comparison with sibling tools. There is an implied usage context, but no direct routing or alternatives are mentioned.

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 that it uses pre-generated answers, does not invoke the LLM, and returns near-instantly, which is key behavioral context. With no annotations, this is the primary source of behavioral information, though it does not cover failure modes or side effects of force_refresh.

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 typical but front-loads the core purpose and includes structured examples and bolded guidance. Every sentence serves a purpose, though it could be tightened.

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 two-parameter retrieval tool, the description covers purpose, usage, and discovery guidance. It lacks output format details, but that is acceptable given no output schema and the simplicity of the tool.

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

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

The schema already describes both parameters with 100% coverage, so the description adds little beyond the example name. It does not clarify semantics further than the schema does.

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

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

The description clearly identifies the tool as a caller for pre-generated answers, with a specific resource (standard questions) and distinguishes it from mh_search. It lists concrete categories, making the 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?

Explicitly instructs to use this tool for standard questions first, forbids using mh_search instead, and directs to mh_tools_list for discovery. Provides both positive and negative usage 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?

No annotations are provided, so the description must carry the burden. The description only states what it lists but doesn't disclose any behaviors like whether it requires authentication, whether it returns stale data, or whether it makes external calls. It doesn't contradict anything, but it's thin on 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?

The description is a single sentence in Chinese, which is concise and to the point. It front-loads the purpose ('列出全部预生成答案工具') and adds the extra detail (freshness). It could be slightly more structured but it's 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?

For a listing tool with no parameters, the description provides the essential what (list) and the extra (freshness). It doesn't describe the output format, but since there's no output schema, the description might need to hint at what 'freshness' means, but it's not critical. It's minimally 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?

With zero parameters, the description doesn't need to explain parameter semantics. The schema is empty, and the description correctly avoids mentioning parameters. A baseline of 4 is not necessary because there's nothing to compensate for.

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 purpose: listing all pre-generated answer tools and providing freshness information. It is clear about the resource (pre-generated answer tools) and the action (list). It doesn't explicitly distinguish from siblings, but the siblings have different names suggesting different functions.

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 implies when to use it (when you need an overview of available pre-generated tools and their freshness), but it doesn't explicitly state when not to use it or mention alternatives. For a tool with no parameters, context is limited, so a 3 is reasonable.

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 states partial-update semantics (only provided fields change), automatic vector and hash recomputation when content changes, and id preservation that doesn't break superseded_by/conflict reference chains. It doesn't mention error cases or permission requirements, but the core behavioral traits are well disclosed.

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 the action and use cases, the second explains the partial-update semantics and side effects, and the third gives the id-preservation rationale and a practical recommendation. Every sentence earns its place 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 an 8-parameter mutation tool with no annotations and no output schema, the description covers the essential behavioral contract: partial updates, side effects, and id preservation. It doesn't describe return values or error conditions, but the core semantics an agent needs to invoke it correctly are present. The recommendation to use mh_get first adds practical completeness.

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 50%, and the description adds meaningful semantics beyond the schema: it explains the in-place partial-update model ('只改传了的字段，未传的保持不动'), the vector/hash recomputation side effect for content, and the id-preservation guarantee. This compensates well for the parameters that lack schema descriptions (layer, source, category, confidence).

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 ('原地更新' / in-place update) and resource ('一条记忆'), and enumerates concrete use cases (fixing typos, supplementing content, toggling pinned, changing category). It also explicitly distinguishes itself from the alternative '删了重写' (delete-and-recreate), which differentiates it from sibling tools like mh_write.

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

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

The description gives explicit when-to-use guidance: use this for in-place updates to preserve id and reference chains, and it explicitly recommends reading the original with mh_get before modifying content. It also names the alternative approach ('删了重写') and explains why this tool is superior, giving clear context for when to choose this over other operations.

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 behavioral disclosure burden. It confirms the write nature and the mandatory completion rule, but does not mention whether the operation is new-entry-only, whether it can overwrite or merge, authentication/permissions, or any failure/error behavior. For a mutation tool with zero annotation coverage, this is a meaningful transparency gap.

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

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

The description is concise and front-loads the core action, then lists the essential parameter semantics in compact clauses. It avoids excessive wording and every sentence adds information. The only minor loss is a slightly disorganized ordering of requirements, but overall it is well-suited for an efficient read.

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 has 7 parameters and no output schema, the description leaves several operational gaps: it doesn't state what the tool returns, how errors are signaled, or exactly what happens when a new entry conflicts with existing data. It also does not differentiate between writing a new memory and updating/replacing one via siblings. These are non-trivial details an agent needs to reliably invoke and verify the operation.

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?

With 86% schema coverage, the schema already documents most parameters. The description reinforces layer/category semantics and adds the 'important conclusions must be persisted' rule, but it mostly duplicates the schema's own parameter descriptions. It does not add per-parameter nuance that goes beyond the schema, so the baseline of 3 is appropriate.

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

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

The description starts with a clear verb and object (写一条记忆/知识 to the memory hub), explicitly names the target resource (openmem), and constrains it with required source, layer, and category values. It does not explicitly contrast itself with the sibling mh_update, but the verb 'write' versus 'update' strongly suggests a create-style 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 a high-level condition (重要结论不写入=任务不算完成), which implies that important conclusions should be written here, but it does not state when not to use it or mention alternatives like mh_update or mh_service. There is no inclusion/exclusion guidance or reference to other tools, leaving an agent to infer when to switch to a sibling tool.

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