AI-Collab-Memory

by sodam-ai

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Server Quality Checklist

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Latest release: v2.0.0
Disambiguation5/5
Each tool has a clearly distinct purpose with no overlap. For example, get_work_log retrieves logs, log_work records work, save_checkpoint saves state, and search_research searches research—all targeting different resources and actions. The descriptions reinforce these distinctions, making misselection unlikely.
Naming Consistency5/5
All tool names follow a consistent verb_noun pattern in snake_case, such as get_work_log, save_checkpoint, and search_research. There are no deviations in naming style or convention, making the set predictable and easy to understand.
Tool Count5/5
With 7 tools, this server is well-scoped for AI collaboration and memory management. Each tool serves a specific function (e.g., logging, checkpointing, research handling), and none feel redundant or missing, fitting the domain appropriately.
Completeness4/5
The tool set covers core workflows for AI collaboration, including logging, checkpointing, and research management. However, minor gaps exist, such as no explicit update or delete operations for logs or research, which agents might need to work around, but the surface is largely complete for the stated purpose.
Average 3.2/5 across 7 of 7 tools scored.
See the Tool Scores section below for per-tool breakdowns.
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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?
With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions saving state for resumption, but lacks details on permissions, data persistence, whether it overwrites existing checkpoints, error handling, or response format. For a mutation tool with zero annotation coverage, this is a significant gap.
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 highly concise with two sentences that directly explain the tool's purpose and benefit. Every word earns its place, and it's front-loaded with the core functionality.
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?
For a mutation tool with 5 required parameters and no annotations or output schema, the description is insufficient. It doesn't address behavioral aspects like what happens on save, how checkpoints are stored/retrieved, or error conditions. The context signals indicate complexity that isn't adequately covered.
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?
The input schema has 100% description coverage, providing clear documentation for all 5 parameters. The description adds no additional parameter semantics beyond what's in the schema, so it meets the baseline of 3 where the schema does the heavy lifting.
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?
The description clearly states the tool's purpose with a specific verb ('save') and resource ('current work state'), explaining it allows resuming work after interruption. However, it doesn't explicitly differentiate from sibling tools like 'save_research' or 'log_work', which likely have overlapping save/logging functions.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides a general context ('when work is interrupted') but offers no explicit guidance on when to use this tool versus alternatives like 'save_research' or 'log_work'. There's no mention of prerequisites, exclusions, or comparative scenarios with sibling tools.
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 provided, the description carries full burden but offers minimal behavioral context. It mentions search capabilities but doesn't disclose whether this is a read-only operation, how results are returned (format, pagination), error conditions, or performance characteristics. The description is functional but lacks transparency about how the tool behaves beyond basic functionality.
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 perfectly concise - two clear sentences that directly explain the tool's function and search parameters without any wasted words. It's front-loaded with the core purpose and efficiently lists the search dimensions.
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?
For a search tool with 3 parameters and no output schema, the description is inadequate. It doesn't explain what 'research' entails, the format of returned results, whether searches are case-sensitive, how multiple parameters interact, or any limitations. With no annotations and no output schema, the description should provide more complete context about the tool's behavior and results.
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 the schema already documents all three parameters thoroughly. The description adds marginal value by confirming the three search dimensions (keyword, tag, contributor) but doesn't provide additional semantics like search logic (AND/OR), partial matching, or parameter interactions beyond what's in the schema descriptions.
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?
The description clearly states the tool's purpose as 'searching saved research' with specific search criteria (keywords, tags, authors). It distinguishes itself from siblings like 'save_research' and 'log_work' by focusing on retrieval rather than creation or logging. However, it doesn't explicitly differentiate from potential similar search functions that might exist in other contexts.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites, limitations, or compare with sibling tools like 'get_work_log' or 'load_checkpoint' that might also retrieve information. The agent must infer usage from the tool name alone.
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 carries full burden. It mentions filtering capabilities but lacks critical behavioral details: whether this is a read-only operation, if it requires authentication, how results are ordered, pagination behavior beyond the 'limit' parameter, or what format/logs are returned. For a query tool with zero annotation coverage, this leaves significant gaps.
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?
The description is concise with two sentences that directly address purpose and filtering capabilities. It's front-loaded with the main action and avoids unnecessary details. However, it could be slightly more structured by explicitly separating purpose from parameter guidance.
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?
Given no annotations and no output schema, the description is incomplete. It doesn't explain what '작업 로그' contains, the return format, error conditions, or behavioral traits like read-only nature. For a tool with 3 parameters and query functionality, more context is needed to guide effective use.
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 the schema already documents all three parameters with descriptions. The description adds marginal value by mentioning filtering by '특정 AI' (specific AI) and '태그' (tag), which aligns with the 'agent' and 'tag' parameters, but doesn't provide additional syntax, format examples, or constraints beyond what's in the schema.
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?
The description clearly states the verb '조회합니다' (retrieve/query) and resource '작업 로그' (work logs), making the purpose understandable. It distinguishes from siblings like 'log_work' (which creates logs) and 'search_research' (which searches research content). However, it doesn't specify whether this retrieves all logs or has implicit limitations beyond filtering.
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 implies usage context by mentioning filtering capabilities ('특정 AI의 작업이나 태그로 필터링할 수 있습니다'), suggesting when to use these parameters. However, it doesn't explicitly state when to choose this tool over alternatives like 'search_research' or provide exclusion criteria (e.g., when not to use it).
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 provided, the description carries full burden for behavioral disclosure. It mentions the persistence aspect ('저장합니다' - saves) and future searchability, but doesn't address critical behavioral traits like whether this operation is idempotent, what permissions are required, whether it overwrites existing research, or what happens on failure. For a write operation with zero annotation coverage, this is inadequate.
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?
The description is appropriately concise with two sentences that each serve a purpose: the first states the core action, the second explains the value/context. There's no wasted text, though it could be slightly more structured by front-loading the most critical information.
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 this is a write operation with 5 parameters, no annotations, and no output schema, the description should do more to explain behavioral aspects. While it covers the basic purpose and persistence context, it lacks information about what the tool returns, error conditions, or how it differs from similar tools like 'save_checkpoint'. The description is minimally adequate but has clear 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 description coverage is 100%, so the schema already documents all 5 parameters thoroughly. 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 description.
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?
The description clearly states the tool's purpose with a specific verb ('저장합니다' - saves) and resource ('리서치 결과' - research results), and distinguishes it from siblings by mentioning future searchability. However, it doesn't explicitly differentiate from 'save_checkpoint' or 'log_work' which might have overlapping functionality.
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 implies usage context ('다른 AI가 나중에 검색해서 활용할 수 있습니다' - other AIs can search and utilize it later), suggesting this is for persistent storage of research findings. However, it doesn't provide explicit when-to-use guidance or alternatives, nor does it mention when NOT to use it versus siblings like 'save_checkpoint' or 'log_work'.
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 carries the full burden of behavioral disclosure. The description mentions the tool 'records' work, implying a write/mutation operation, but doesn't specify whether this creates new entries, updates existing ones, requires permissions, has side effects, or what happens on failure. For a mutation tool with zero annotation coverage, this leaves significant behavioral gaps unexplained.
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 extremely concise (two sentences) and front-loaded with the core purpose first, followed by use cases. Every sentence adds value: the first states what the tool does, and the second explains why it's useful. There's zero redundancy or wasted words, making it highly 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?
Given the tool's moderate complexity (5 parameters, mutation operation) and lack of both annotations and output schema, the description is minimally adequate. It covers the purpose and use cases but lacks details on behavioral aspects like error handling, persistence model, or return values. The 100% schema coverage helps, but for a write tool with no structured safety hints, more context would be beneficial.
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%, with all 5 parameters well-documented in the schema (agent, action, detail, result, tags). The description doesn't add any parameter-specific information beyond what the schema provides, such as format examples or constraints. With complete schema coverage, the baseline score of 3 is appropriate as the description doesn't enhance parameter understanding but doesn't need to compensate for gaps.
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?
The description clearly states the tool's purpose: 'AI가 수행한 작업을 기록합니다' (records work performed by AI). It specifies the verb ('기록합니다' - records) and resource ('작업' - work/tasks). While it doesn't explicitly differentiate from all siblings, it does mention use cases like tracking '누가 뭘 했는지' (who did what) and resuming interrupted work, which helps distinguish it from purely retrieval tools like get_work_log.
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 provides implied usage context by mentioning when the recorded information is useful ('나중에 확인하거나 중단된 작업을 이어서 할 때' - for later review or resuming interrupted work). However, it doesn't explicitly state when to use this tool versus alternatives like save_checkpoint or save_research, nor does it provide exclusion criteria. The guidance is helpful but incomplete for sibling differentiation.
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 carries full burden. It states this is a read operation ('확인합니다' - check/verify) which implies non-destructive behavior. However, it doesn't disclose important behavioral traits like whether this requires authentication, rate limits, what format the statistics are returned in, or whether it's real-time vs cached data. For a status tool with zero annotation coverage, this leaves significant gaps.
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?
The description is appropriately concise with two sentences that efficiently convey the tool's purpose and scope. The first sentence states the main function, the second elaborates on what's included. No wasted words or redundant information. Could potentially be improved with more specific behavioral context, but within its current scope it's well-structured.
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 has no parameters (simple complexity) and no output schema, the description provides adequate basic information about what the tool does. However, for a status/overview tool that presumably returns structured data about multiple resource types, the description should ideally specify what format the statistics are returned in or what specific metrics are included. The absence of annotations means the description should do more heavy lifting.
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?
The tool has 0 parameters with 100% schema description coverage, so the schema fully documents the absence of parameters. The description appropriately doesn't discuss parameters since none exist. Baseline for 0 parameters is 4, and the description doesn't incorrectly mention any parameters.
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?
The description clearly states the tool's purpose: '확인합니다' (check/verify) and '보여줍니다' (show/display) with specific resources mentioned (work logs, research, checkpoint statistics). It distinguishes from siblings like get_work_log or search_research by covering multiple resource types in one view. However, it doesn't explicitly contrast with each sibling tool.
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 implies usage context ('전체 현황' - overall status) suggesting this is for getting a comprehensive overview rather than specific queries. However, it doesn't provide explicit guidance on when to use this versus alternatives like get_work_log for individual work logs or search_research for filtered research. No exclusion criteria or prerequisites are mentioned.
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 provided, the description carries the full burden of behavioral disclosure. While it mentions the tool loads saved checkpoints for resuming work, it doesn't describe what 'loading' entails operationally - whether it restores state, overwrites current work, requires specific permissions, or has any side effects. For a tool that presumably modifies system state, this lack of behavioral detail is a significant gap.
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 perfectly concise with just two sentences. The first sentence states the core functionality, and the second provides usage context. Every word earns its place with no redundancy or unnecessary elaboration. The structure is front-loaded with the primary purpose stated first.
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 has 2 parameters with full schema coverage but no annotations and no output schema, the description provides adequate but minimal context. It explains what the tool does and when to use it, but doesn't address behavioral aspects, return values, or error conditions. For a checkpoint loading tool that likely affects system state, more completeness would be beneficial, but it meets minimum viable standards.
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?
The schema has 100% description coverage, with both parameters clearly documented in the schema itself. The description doesn't add any parameter-specific information beyond what's already in the schema descriptions. According to the scoring rules, when schema_description_coverage is high (>80%), the baseline is 3 even with no param info in the description, which applies here.
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?
The description clearly states the action ('불러옵니다' - loads/retrieves) and resource ('저장된 체크포인트' - saved checkpoints), making the purpose understandable. It distinguishes from siblings like 'save_checkpoint' by focusing on retrieval rather than creation. However, it doesn't explicitly differentiate from other retrieval tools like 'get_work_log' or 'search_research' beyond the checkpoint focus.
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 clear context for when to use this tool: '중단된 작업을 이어서 시작할 때' (when resuming interrupted work). This gives practical guidance about the tool's intended scenario. However, it doesn't specify when NOT to use it or mention alternatives among sibling tools, which prevents a perfect score.
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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