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

Since no annotations are provided, the description fully covers behavioral traits: cache behavior (auto-cache, expiration, performance), output budget control (detail_level, max_output_tokens), and estimated execution times (60-120s for first call, ~10s subsequent, 1-5 minutes for large codebases).

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 a clear purpose statement followed by structured items on cache behavior and output details. It is relatively long but front-loaded with the main purpose, earning a 4 for good organization.

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 8 parameters, no output schema, and no annotations, the description is highly complete. It covers tool purpose, caching mechanics, output configuration, performance expectations, and related tools, ensuring the agent can use it effectively.

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 input schema already provides 100% coverage with descriptions for all 8 parameters. The description adds context about caching and output budget but does not significantly enhance parameter meaning beyond the schema.

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

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

The description explicitly states the tool sends a prompt to Kimi Code for codebase analysis and returns a compressed, structured report. It distinguishes itself from siblings like kimi_resume and kimi_query by mentioning their use cases.

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 guidance on when to use this tool (first vs. subsequent calls) and suggests using kimi_resume for deeper analysis. However, it does not explicitly state when not to use this tool or provide comprehensive 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?

No annotations are provided, so the description must carry the full behavioral burden. It only states the basic purpose and one use case, but does not disclose side effects, permissions, or what 'invalidate' actually means (e.g., deletion vs marking).

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 with no unnecessary words. The action and usage context are front-loaded, making it efficient for an AI 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?

The tool is simple (one optional parameter, no output schema), and the description covers the basic purpose and parameter behavior. However, it lacks information about the return value or what 'invalidate' entails operationally, which could be improved.

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%; the description adds value by explaining the default behavior when 'work_dir' is not provided (ALL caches cleared). This goes beyond the schema's own description.

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?

Clearly specifies the verb 'invalidate' and the resource 'session cache entries', and provides a reason for use. Distinguishes from sibling tools like kimi_list_sessions and kimi_analyze because it focuses on cache invalidation.

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: 'force fresh analysis' or 'suspect stale sessions'. Does not mention when not to use or explicitly reference sibling alternatives, but the context 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?

No annotations are provided, so the description carries full responsibility. It implies a read-only operation ('View'), but does not disclose potential side effects, permission requirements, or rate limits. Minimal behavioral insight beyond the basic purpose.

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 concise sentences: first states purpose, second adds usage guidance and output summary. No wasted words, though slightly more detail on output could be added without harming conciseness.

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 single parameter, no output schema, and no annotations, the description adequately covers purpose and usage. However, it lacks details on return format, pagination, or performance overhead, leaving some gaps for an AI agent.

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

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

Schema coverage is 100% with one boolean parameter 'detail' already described in the schema. The description adds no extra meaning beyond the schema, so baseline score applies.

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

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

Description explicitly states 'View session cache statistics and status,' specifying the verb and resource. It distinguishes itself from sibling tools like kimi_cache_invalidate (mutation) implicitly, but does not strongly differentiate from kimi_status, which might also show status.

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 advises using the tool 'to monitor cache effectiveness and troubleshoot issues,' providing context. However, it does not explicitly state when not to use it or mention alternatives, leaving some guidance 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?

No annotations are provided, so the description carries full burden. It correctly implies a read-only operation and mentions a default limit (implied by the 'max sessions' parameter description). However, it does not disclose pagination behavior, cost, or whether results are sorted or cached.

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 short sentences: first explains what the tool does and what it returns, second explains its primary use case. No filler 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?

For a list tool with no output schema, the description covers the essential output fields and ties to a sibling. Missing details like sorting or page size limits, but given the simplicity, it is largely 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 coverage is 100%; both parameters ('limit' and 'work_dir') have descriptions. The tool description adds no new parameter meaning beyond listing output fields, so it meets the baseline but does not exceed it.

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

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

The description clearly states the verb ('List'), the resource ('Kimi Code sessions'), and the output fields ('titles, working directories, and timestamps'). It also immediately connects to a sibling tool use-case ('find session IDs for kimi_resume'), differentiating it from other analysis 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?

The description explicitly tells when to use this tool ('find session IDs for kimi_resume'), which implies the context. It does not enumerate alternatives or exclusion cases, but siblings are distinct (analyze, query, verify, etc.), making the guidance clear 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?

With no annotations provided, the description correctly signals that this is a non-codebase query tool (read-only, no side effects). It doesn't detail auth, rate limits, or response format, but the behavioral trait 'without codebase context' is a key disclosure. Adequate for a simple query 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?

Two sentences with zero wasted words. Critical constraint ('without codebase context') is front-loaded. Every sentence earns its place; 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 query tool with no output schema, the description tells the agent what to expect: a natural language answer. It could mention that responses are text, but the context is sufficiently complete given the tool's simplicity and the presence of sibling tools for other tasks.

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 parameters. The description adds overall context but doesn't elaborate on parameter behavior beyond the schema. Baseline 3 is appropriate; no additional value for individual parameters.

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 specifies a clear verb ('Ask') and resource ('Kimi Code'), and crucially distinguishes this tool from siblings by stating 'without codebase context'. It lists concrete use cases (general programming questions, algorithm explanations, second opinion), leaving no ambiguity about 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?

Explicitly says when to use: for general programming questions without codebase context. While it doesn't list negative examples, the positive guidance is strong and implicitly excludes codebase-specific queries (handled by siblings like kimi_analyze). Clear context for selection.

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

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

Discloses session retains up to 256K tokens, but lacks information on destructive behavior or side effects. Since no annotations are provided, description carries the burden but only partially meets it.

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 fluff. The key action and usage hint are front-loaded, making it easy for an agent to quickly grasp the tool's purpose.

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 core behavior and use case. Lacks details about return values or error conditions, but schema and sibling context fill some gaps. Adequate for a resume 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 parameters are already documented. Description adds context about session retention but does not significantly enhance parameter understanding beyond 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 resumes a session with a new prompt, mentions context retention, and distinguishes itself from siblings like kimi_analyze and kimi_list_sessions.

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 says to use kimi_list_sessions first and recommends for deeper drilling after kimi_analyze, providing clear context for when to 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 provided, the description carries full burden for behavioral disclosure. It states what the tool checks (status, version, auth) but does not mention whether it is read-only, if it requires network access, or any side effects. The description is adequate but could be more transparent.

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: first states purpose, second gives usage guidance. Every sentence adds value, and it is front-loaded with the essential action.

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 is complete for a simple status check with no output schema. It covers what the tool does and when to use it. However, adding a note about the output format or that it is a fast, safe operation would enhance completeness slightly.

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 baseline is 4. The description does not need to add meaning beyond the empty schema, and it doesn't. It correctly implies no input is required.

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 checks 'Kimi CLI installation status, version, and authentication.' This provides a specific verb ('check') and resource, and it distinguishes from sibling tools like kimi_analyze or kimi_cache_status, which have different purposes.

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 tells when to use the tool: 'Use this to diagnose issues before running analysis.' This provides context and a clear use case, though it does not explicitly mention when not to use it or alternative tools.

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