Kokkai Minutes MCP Agent
Server Quality Checklist
Latest release: v1.0.0
- Disambiguation5/5
Each tool has a clearly distinct purpose with no ambiguity: searchMeetingList retrieves meeting metadata without speech text, searchMeetingsWithSpeechText returns meetings with full speech text, and searchSpeeches focuses on individual speeches with meeting context. The descriptions explicitly differentiate their scopes and output formats, making misselection unlikely.
Naming Consistency5/5All tool names follow a consistent verb_noun pattern using camelCase (searchMeetingList, searchMeetingsWithSpeechText, searchSpeeches), with 'search' as the uniform verb prefix. This predictable naming scheme enhances readability and agent usability without deviations.
Tool Count3/5With only 3 tools, the count feels borderline thin for a parliamentary minutes domain, potentially limiting advanced operations like filtering by date or speaker. However, it covers core search functionalities adequately, avoiding bloat but leaving room for expansion to improve workflow completeness.
Completeness4/5The tool set provides comprehensive search coverage for Kokkai minutes, including metadata, full meeting texts, and individual speeches, with no dead ends. Minor gaps exist, such as lack of update/delete operations (irrelevant for read-only data) or advanced filtering options, but agents can work around these with the available tools.
Average 3.9/5 across 3 of 3 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 0 commits in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI status not available
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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
- 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. It discloses key behavioral traits: the tool is a search operation (implied read-only), returns basic meeting info, excludes speech text, and has a max record limit of 100. However, it lacks details on error handling, authentication needs, rate limits, or pagination behavior (beyond startRecord parameter), leaving gaps for a tool with 22 parameters.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with essential information in three concise sentences: purpose, output format, and key constraint. Every sentence adds value without redundancy, making it efficient and well-structured for quick understanding.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity (22 parameters, no annotations, no output schema), the description is adequate but incomplete. It covers purpose, output scope, and a key limit, but lacks details on return format structure, error cases, or usage prerequisites. For a search tool with many parameters and no output schema, more context on result handling would be beneficial.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema fully documents all 22 parameters. The description adds no parameter-specific information beyond implying search functionality. It mentions 'Max 100 records' which relates to the maximumRecords parameter, but this is already covered in the schema. Baseline 3 is appropriate as the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Searches for meeting summaries'), specifies the data source ('from the Kokkai API'), and distinguishes it from siblings by noting 'Does not include speech text' (unlike searchMeetingsWithSpeechText). It also specifies the output format ('basic info like meeting name, date, ID, URL') and a key constraint ('Max 100 records').
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context by stating what this tool does not include ('Does not include speech text'), which implicitly guides users toward searchMeetingsWithSpeechText for speech content. However, it does not explicitly name alternatives or specify when not to use this tool beyond the speech text exclusion, missing full explicit guidance.
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 behavioral disclosure. It adds some context: it specifies the source (Kokkai API), the output includes meeting info and all speech texts, and a max record limit of 10. However, it lacks details on permissions, rate limits, pagination (beyond the 'startRecord' parameter), error handling, or the exact format of returned data. For a tool with 22 parameters and no annotations, this leaves significant gaps in understanding its behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise and front-loaded, consisting of only two sentences that efficiently convey the tool's purpose, scope, and key constraint (max 10 records). Every word earns its place, with no redundant or vague language, making it easy for an AI agent to quickly understand the tool's core functionality.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (22 parameters, no annotations, no output schema), the description is incomplete. It adequately states the purpose and output scope but lacks details on behavioral aspects like error handling, data format, or usage constraints beyond the record limit. While it meets the minimum for clarity and conciseness, it does not provide enough context for an agent to fully anticipate how to handle results or potential issues, especially with such a rich parameter set.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 100%, meaning all parameters are documented in the schema itself. The description does not add any specific parameter semantics beyond what the schema provides, such as explaining interactions between parameters or providing examples. Since the schema does the heavy lifting, the baseline score of 3 is appropriate, as the description does not compensate with additional insights but also does not detract.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('Searches for meetings'), the resource ('meetings and includes the full text of ALL speeches'), the source ('from the Kokkai API'), and the scope ('Max 10 records'). It distinguishes from sibling tools by specifying it returns speech texts, unlike 'searchMeetingList' (likely just meeting metadata) and 'searchSpeeches' (likely focused on speeches rather than meetings with speeches).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use this tool: when you need meeting information along with all speech texts, with a limit of 10 records. It implies usage by specifying the output includes speech texts, which differentiates it from siblings. However, it does not explicitly state when not to use it or name alternatives, such as using 'searchMeetingList' if only meeting metadata is needed or 'searchSpeeches' for speech-focused searches.
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 full burden for behavioral disclosure. It mentions the 'Max 100 records' limit, which is valuable operational context. However, it doesn't describe authentication needs, rate limits, error conditions, pagination behavior (beyond the startRecord parameter), or what happens when no results are found. For a search tool with 22 parameters and no annotations, this leaves significant behavioral gaps.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is perfectly concise with just two sentences that pack essential information: what it searches, what it returns, and the record limit. Every word earns its place, and it's front-loaded with the core purpose. No wasted words or redundant information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (22 parameters, no output schema, no annotations), the description is adequate but incomplete. It covers the basic purpose and record limit but lacks information about authentication, error handling, response format, pagination strategy beyond the startRecord parameter, and explicit differentiation from sibling tools. For a search tool of this complexity, more contextual guidance would be helpful.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents all 22 parameters thoroughly with descriptions, enums, patterns, and defaults. The description doesn't add any parameter-specific information beyond what's in the schema. According to scoring rules, when schema coverage is high (>80%), the baseline is 3 even with no param info in the description.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb ('Searches'), resource ('specific speeches from the Kokkai API'), and scope ('発言単位出力'). It distinguishes from siblings by specifying it returns 'speech text and info about the meeting it belongs to' rather than just meeting lists or combined results. The mention of 'Max 100 records' further clarifies its operational limits.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context about what the tool returns (speech text + meeting info), which helps differentiate it from sibling tools like 'searchMeetingList' (likely returns only meeting metadata) and 'searchMeetingsWithSpeechText' (might return combined meeting-speech data). However, it doesn't explicitly state when to use this tool versus those alternatives or mention any prerequisites or exclusions.
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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