kimi-code-mcp

by howardpen9

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Server Quality Checklist

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Latest release: v0.5.0
Disambiguation5/5
Each tool has a distinct purpose: listing sessions, analyzing codebase, querying without context, independent verification, cache management, resuming sessions, and status checking. No overlap or ambiguity.
Naming Consistency5/5
All tools follow a consistent 'kimi_verb' pattern using snake_case (e.g., kimi_analyze, kimi_cache_status). The naming is predictable and uniform.
Tool Count5/5
With 8 tools covering session management, analysis, verification, and cache control, the count is well-scoped for a code analysis assistant—neither too sparse nor overwhelming.
Completeness4/5
The surface covers core workflows (analysis, resume, verification, cache, status). A minor gap is the lack of an explicit session deletion tool, but the provided tools enable effective operation.
Average 4/5 across 8 of 8 tools scored. Lowest: 3.2/5.
See the Tool Scores section below for per-tool breakdowns.
- 1 of 1 community issues answered or closed in the last 6 months
- 8 commits in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is passing
This repository is licensed under MIT License.
This repository includes a README.md file.
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How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

Behavior2/5
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.
Conciseness4/5
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.
Completeness3/5
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.
Parameters3/5
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.
Purpose4/5
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.
Usage Guidelines3/5
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.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden. It mentions status checks and live probes but omits safety (e.g., non-destructive read), error behaviors, or rate limits. The probe implies a network request, but consequences (cost, side effects) are undisclosed.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences, front-loading the core purpose, then adding the optional probe. Every word is necessary and no fluff.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Despite no output schema, the description fails to explain return structure (e.g., status fields, auth state, model). For a complex status tool with 8 siblings, it should suggest when to use this vs. kimi_cache_status or set expectations for output.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so baseline is 3. The description adds minimal value beyond repeating the schema's 'probe' behavior and default model; no new semantics for parameters like format constraints or edge cases.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
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/API status, default model detection, and auth. It distinguishes from sibling tools like kimi_query or kimi_cache_status by focusing on overall health.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description gives guidance on using probe_api for a live check but does not explicitly state when to use this tool over alternatives or any exclusions. The context of sibling tools helps but the description itself is limited.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
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.
Conciseness5/5
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.
Completeness3/5
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.
Parameters4/5
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.
Purpose5/5
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.
Usage Guidelines4/5
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.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden. It discloses session context retention up to 256K tokens, but omits details about authorization, rate limits, error handling, or side effects. The behavioral information is adequate but incomplete.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is three sentences, front-loaded with the primary action and resource. Every sentence adds essential context: action+retention, prerequisite, and use case. No fluff or redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (8 params, no output schema), the description covers purpose, context retention, and a use case. However, it lacks details on return values, error scenarios, or behavior for missing/invalid sessions. Moderately complete but with gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so baseline is 3. The description does not add meaning beyond the schema; it reinforces the role of session_id and prompt, but does not elaborate on parameter syntax or constraints. Meets minimum expectation.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'Resume' with the resource 'existing Kimi Code session' and specifies the action 'with a new prompt'. It distinguishes from siblings by mentioning prerequisite use of 'kimi_list_sessions' and positioning as follow-up after 'kimi_analyze'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit prerequisite ('Use kimi_list_sessions to find session IDs first') and a use case ('Ideal for drilling deeper after an initial kimi_analyze scan'). It implies when to use this tool over kimi_analyze, but lacks explicit when-not scenarios or alternative tools like kimi_query.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
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.
Conciseness5/5
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.
Completeness4/5
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.
Parameters3/5
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.
Purpose5/5
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.
Usage Guidelines4/5
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.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of disclosing behavior. It mentions the default model and the lack of codebase context, but does not discuss idempotency, authentication, rate limits, or error handling. For a read-only query tool, this is minimally adequate but leaves gaps.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences long, front-loading the core purpose and then providing usage guidance. Every sentence is meaningful and there is no redundancy or unnecessary detail.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the 5 parameters are well-documented in the schema and the tool is a straightforward query, the description is largely complete. It could be enhanced by mentioning the response format, but this is not critical for a simple question-answering tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so baseline is 3. The description does not add parameter-specific details beyond what the schema already provides (e.g., default model, examples). Therefore, no extra value is contributed.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb ('Ask'), the resource ('Kimi Code'), and the key differentiator ('without codebase context'). It also lists specific use cases (general programming questions, algorithm explanations, second opinions), which distinguishes it from sibling tools that may involve codebase context.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
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 for general programming questions, algorithm explanations, or getting a second opinion.' It also implies when not to use it by specifying 'without codebase context,' indicating that for codebase-specific queries, other tools should be preferred.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. Details caching, time expectations (60-120s first, ~10s subsequent), auto-expiry, budget-controlled output (5-15K tokens), and configurable detail_level. Thoroughly transparent.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Well-structured with clear sections (purpose, cache behavior, output). Front-loaded with main purpose. Somewhat lengthy but each part earns its place. Minor redundancy could be trimmed.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Covers caching, timings, output budgets, relationships to sibling tools, and configuration options. No output schema needed; mentions structured report but not exact format. Fairly complete for a complex tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, baseline 3. Description adds significant value: explains caching mechanism in relation to session_id, detail_level token ranges, max_output_tokens usage, and distinction between thinking and include_thinking. Compensates well.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
Clearly states it sends a prompt to Kimi Code for codebase analysis, mentions reading codebase with 256K context and returning a compressed structured report. Differentiates from siblings by referencing kimi_resume and kimi_cache_status.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides usage context: first call may be slow, subsequent calls faster via caching; suggests kimi_resume for deeper analysis. Lacks explicit when-not-to-use but covers key scenarios.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description fully discloses behavior: it calls the Kimi Code API directly, requires an API key, and critically states 'Kimi sees ONLY the 'context' string. It has NO access to this session, the repository, prior messages, or any tools.' This properly sets expectations for the stateless, isolated verification.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured: starts with core purpose, then usage guidelines, then critical behavioral warnings. Every sentence adds value without redundancy. It is appropriately sized for the complexity and difficulty.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema, the description mentions the response footer detection but doesn't detail the return structure. However, it covers input requirements, behavioral constraints, and detection markers. For a text-returning tool with 6 parameters (100% schema coverage), it is largely complete, though a brief note on expected response format would strengthen it.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% (6 params described in input schema). The description adds value beyond schema by explaining the purpose of each parameter (e.g., 'context' must be self-contained, 'question' defaults to general correctness review, 'role' and 'model' are overrides). It provides practical guidance, but the schema already covers the basics.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Get an INDEPENDENT second opinion from Kimi Code ... acting as an external third-party verifier.' It uses specific verbs and resources, and distinguishes it from sibling tools (like kimi_analyze, kimi_query) by emphasizing its verification role.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides explicit when-to-use scenarios: 'cross-check your own work', 'confirm a fix', 'find bugs / edge cases / security issues', 'sanity-check a claim or plan', 'get a dissenting view before you commit'. It also emphasizes the importance of passing full context. While it doesn't explicitly state when not to use or name alternatives among siblings, the usage context is clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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