Obsidian MCP Server

by libra850

Overview Schema Related Servers Score Discussions

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Server Quality Checklist

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Latest release: v1.0.0
Disambiguation5/5
Each tool has a clearly distinct purpose with no overlap: analyze_backlinks focuses on graph analysis, create_moc generates navigation structures, create_note_from_template handles templated creation, find_broken_links detects issues, link_notes creates connections, list_tags and list_templates enumerate metadata, read_note and update_note handle content operations, rename_tag manages tags, and search_files finds files. The descriptions clearly differentiate these functions.
Naming Consistency5/5
All tool names follow a consistent verb_noun pattern in snake_case, such as analyze_backlinks, create_moc, find_broken_links, and update_note. This uniformity makes the tool set predictable and easy to understand, with no deviations in naming conventions.
Tool Count5/5
With 11 tools, this server is well-scoped for managing an Obsidian vault, covering key operations like note creation, reading, updating, linking, tagging, searching, and analysis. Each tool serves a specific purpose without redundancy, making the count appropriate for the domain.
Completeness4/5
The tool set provides comprehensive coverage for core Obsidian workflows, including note lifecycle (create, read, update), linking, tagging, searching, and analysis. A minor gap is the lack of a delete_note tool for full CRUD operations, but agents can work around this by using file system operations or other methods.
Average 2.9/5 across 11 of 11 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 is failing
This repository is licensed under MIT License.
This repository includes a README.md file.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
Tip: use the "Try in Browser" feature on the server page to seed initial usage.
Add a glama.json file to provide metadata about your server.
If you are the author, simply .
If the server belongs to an organization, first add glama.json to the root of your repository:
```
{
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}
```
Then . Browse examples.
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How to sync the server with GitHub?

Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.

To manually sync the server, click the "Sync Server" button in the MCP server admin interface.

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 the full burden. It mentions analysis and understanding graph structure, but doesn't disclose behavioral traits like whether it's read-only, if it modifies data, what permissions are needed, or how results are returned. This leaves significant gaps for a tool with no annotation coverage.
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 a single, efficient sentence in Japanese that states the purpose without unnecessary words. It's appropriately sized and front-loaded, though it could be more structured for clarity.
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 lacks details on what the analysis entails, how results are formatted, or any behavioral context, making it inadequate for a tool that likely returns complex graph data.
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 the single parameter 'targetNote' documented as '分析対象のノートパス（vault相対パス）' (target note path relative to vault). The description doesn't add meaning beyond this, 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.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description states 'バックリンク分析とグラフ構造の把握を行います' (performs backlink analysis and graph structure understanding), which provides a general purpose but lacks specificity about what resources are involved or how it differs from sibling tools like 'find_broken_links' or 'link_notes'. It's not tautological but remains somewhat vague.
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?
No guidance is provided on when to use this tool versus alternatives such as 'find_broken_links' or 'link_notes', nor any context about prerequisites or exclusions. The description implies analysis but doesn't specify use cases or constraints.
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. It only states the action of reading/loading note content, without addressing permissions (e.g., read-only access), error handling (e.g., for invalid paths), or output format (e.g., text, metadata). This leaves critical behavioral traits unspecified for a tool that interacts with files.
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 a single, efficient sentence that directly states the tool's function without unnecessary words. It is appropriately sized for a simple tool, though it could be more front-loaded with key details if expanded for better clarity.
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 the complexity of file I/O operations and the lack of annotations and output schema, the description is incomplete. It doesn't explain what '読み込みます' entails (e.g., returns content as text, handles errors), leaving gaps in understanding how the tool behaves and what results to expect, which is inadequate for safe and 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?
The schema description coverage is 100%, with the single parameter 'notePath' clearly documented in the schema as 'ノートのパス（vault相対パス）' (note path relative to vault). The description adds no additional meaning beyond this, such as path format examples or constraints, so it meets the baseline for high schema coverage without compensating value.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description states the action ('読み込みます' - reads/loads) and resource ('Obsidianノートの内容' - Obsidian note content), providing a basic purpose. However, it doesn't distinguish this from potential sibling tools like 'update_note' or 'search_files' that might also involve note content access, making it vague about its specific role in the toolset.
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?
No guidance is provided on when to use this tool versus alternatives. The description doesn't mention prerequisites, context (e.g., for viewing vs. editing), or exclusions, leaving the agent to infer usage from the name alone. This lack of explicit or implied guidelines reduces effectiveness in tool selection.
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. It states the tool 'automatically generates' a Map of Contents note, implying a write operation, but doesn't specify permissions needed, whether it overwrites existing files, error handling, or output format. For a tool that likely creates or modifies files, this lack of detail is a significant gap in transparency.
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 a single, clear sentence in Japanese: 'Map of Contents（目次ノート）を自動生成します'. It's front-loaded with the core purpose, has zero wasted words, and is appropriately sized for the tool's complexity. Every part of the sentence earns its place by stating what the tool does.
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 the tool has no annotations and no output schema, the description is incomplete. It doesn't cover behavioral aspects like file creation effects, error cases, or return values. For a tool with 5 parameters that likely modifies the file system, more context is needed to fully understand its operation and implications.
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 description adds no parameter semantics beyond what the input schema provides. Schema description coverage is 100%, with all parameters well-documented in Japanese (e.g., 'MOCのタイトル' for title, 'グループ化方法' for groupBy). The description doesn't explain how parameters interact or provide additional context, so it meets the baseline for high schema coverage.
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: 'Map of Contents（目次ノート）を自動生成します' (automatically generates a Map of Contents/table of contents note). It specifies the verb '自動生成します' (automatically generates) and the resource 'Map of Contents（目次ノート）'. However, it doesn't explicitly differentiate from sibling tools like 'create_note_from_template' or 'update_note', which could also create notes, so it doesn't reach the highest score.
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 any prerequisites, context, or exclusions, nor does it compare to sibling tools such as 'create_note_from_template' or 'link_notes'. This leaves the agent with minimal direction on appropriate usage scenarios.
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 'creates' which implies a write operation, but doesn't disclose critical behavioral traits: what happens if the template doesn't exist, how variable substitution works, whether the operation is atomic, what permissions are needed, or what happens on failure. For a creation tool with zero annotation coverage, this is insufficient.
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 a single, efficient sentence in Japanese that directly states the tool's core function. There's no wasted words or unnecessary elaboration. It's appropriately sized for a tool with good schema documentation.
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 creation tool with 4 parameters, no annotations, and no output schema, the description is inadequate. It doesn't explain what the tool returns (success/failure, created note details), error conditions, or behavioral constraints. The user must rely entirely on the input schema without contextual guidance about the operation's outcomes or limitations.
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 fully documents all 4 parameters. The description adds no additional parameter information beyond what's in the schema (it doesn't explain template location, variable format, path conventions, or overwrite implications). This meets the baseline for high schema coverage but doesn't enhance understanding.
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: 'creates an Obsidian note using a template' (verb+resource). It specifies the method ('using a template') which distinguishes it from generic note creation tools. However, it doesn't explicitly differentiate from sibling tools like 'create_moc' or 'update_note' that might also involve note creation/modification.
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 when this template-based approach is preferred over direct note creation, nor does it reference any sibling tools (like 'list_templates' for available templates or 'create_moc' for other note types). The user 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 the full burden of behavioral disclosure. It states the tool creates wiki links, implying a write operation, but doesn't cover aspects like whether it modifies existing notes, requires specific permissions, handles errors for non-existent notes, or what happens on success/failure. This leaves significant gaps for a mutation tool.
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 a single, efficient sentence in Japanese that directly states the tool's purpose without any wasted words. It's appropriately sized and front-loaded, making it easy to understand at a glance.
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 the tool's complexity as a mutation operation with 4 parameters, no annotations, and no output schema, the description is insufficient. It doesn't explain the return values, error conditions, or behavioral nuances, leaving the agent with incomplete information for proper invocation.
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 details for all 4 parameters (sourceNote, targetNote, linkText, insertPosition). The description doesn't add any additional meaning beyond what's in the schema, such as explaining parameter interactions or usage examples, so it meets the baseline score when schema coverage is high.
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 'creates wiki links between notes' (ノート間にウィキリンクを作成します), which specifies the action (creates wiki links) and resource (between notes). However, it doesn't differentiate from sibling tools like 'find_broken_links' or 'analyze_backlinks' which also deal with note links, so it doesn't reach the highest score.
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, context, or exclusions, such as when to choose this over 'update_note' for modifying links or how it relates to sibling tools like 'find_broken_links'.
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. It mentions 'batch rename' but lacks details on permissions needed, whether changes are reversible, potential side effects (e.g., affecting multiple notes), or error handling. This is a significant gap for a mutation tool.
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 a single, efficient sentence in Japanese that directly states the tool's purpose without any wasted words. It is appropriately sized and front-loaded with the core action.
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 this is a mutation tool with no annotations and no output schema, the description is incomplete. It should explain more about behavioral traits (e.g., what 'batch' entails, success/failure responses) to compensate for the lack of structured data, but it does not.
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 both parameters ('oldTag' and 'newTag') with details on format ('with or without #'). The description adds no additional meaning beyond what the schema provides, meeting the baseline for high coverage.
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 ('renames') and resource ('tags in Obsidian vault') with a specific scope ('batch'). However, it doesn't explicitly differentiate from sibling tools like 'update_note' or 'link_notes' that might also modify tags indirectly, leaving some ambiguity about uniqueness.
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?
No guidance is provided on when to use this tool versus alternatives. For example, it doesn't specify if this should be used instead of manually editing notes or other tools like 'update_note' for tag changes, nor does it mention prerequisites or exclusions.
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 the tool searches but doesn't disclose behavioral traits like whether it's read-only, how results are returned (e.g., list format, pagination), error handling, or performance implications (e.g., speed on large vaults). This is inadequate for a search tool with zero annotation coverage.
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 a single, efficient sentence in Japanese that directly states the tool's function without unnecessary words. It's appropriately sized and front-loaded, with every part contributing to understanding the purpose.
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 the complexity of a search tool with no annotations and no output schema, the description is incomplete. It doesn't explain what the search returns (e.g., file paths, metadata), how results are structured, or any limitations (e.g., case sensitivity, regex support). This leaves significant gaps for an agent to use the tool effectively.
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 description adds no parameter semantics beyond what the input schema provides. With 100% schema description coverage, the schema fully documents both parameters (searchPath and pattern), including defaults and meanings. The baseline score of 3 reflects that the schema does the heavy lifting, and the description doesn't compensate or add value.
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: 'search files and directories in Obsidian vault' (verb+resource). It's specific about the scope (Obsidian vault) but doesn't differentiate from sibling tools like 'find_broken_links' or 'list_tags' which also involve searching or listing operations in the same context.
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, exclusions, or compare it to siblings like 'list_templates' or 'analyze_backlinks', leaving the agent to infer usage based on tool names 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?
With no annotations provided, the description carries the full burden of behavioral disclosure. It states this is an update operation, implying mutation, but doesn't describe what happens (e.g., overwrites entire content, merges, requires specific permissions, whether changes are reversible, or error conditions). For a mutation tool with zero annotation coverage, 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence in Japanese that directly states the tool's function. It's appropriately sized with zero wasted words and front-loaded the core action, making it easy to parse quickly.
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 this is a mutation tool (update operation) with no annotations, no output schema, and incomplete behavioral disclosure, the description is insufficient. It doesn't cover what the tool returns, error handling, or important behavioral traits like whether it overwrites or appends content. For a tool that modifies data, more context is needed.
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 both parameters ('notePath' and 'content') with descriptions. The description adds no additional parameter semantics beyond what's in the schema, such as format examples or constraints. Baseline 3 is appropriate when 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 verb ('更新します' - updates) and resource ('Obsidianノートの内容' - Obsidian note content), making the purpose understandable. It distinguishes from siblings like 'read_note' (read operation) and 'create_note_from_template' (creation operation), but doesn't explicitly differentiate from potential similar update operations 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 (e.g., note must exist), when not to use it, or how it differs from other tools like 'rename_tag' or 'link_notes' that might also modify notes. Usage context is implied but not explicitly stated.
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. While it mentions detection and repair support, it lacks details on how the tool behaves: e.g., whether it's read-only or modifies data, what permissions are needed, if it has side effects like creating logs, or how repairs are implemented. This leaves significant gaps for a tool that implies potential mutations.
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 a single, efficient sentence in Japanese that directly states the tool's function without any fluff. It's front-loaded with the core purpose and uses minimal words, making it highly concise and well-structured for quick understanding.
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 the complexity implied by 'detection and repair support' and the lack of annotations and output schema, the description is incomplete. It doesn't explain what 'repair support' entails (e.g., automatic fixes, suggestions, or logs), what the output looks like, or any behavioral constraints. This leaves the agent with insufficient context for safe and effective use.
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 input schema has 0 parameters with 100% coverage, so no parameter documentation is needed. The description doesn't add parameter details, which is appropriate here. A baseline of 4 is applied as it compensates for the lack of parameters by not introducing unnecessary complexity.
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: '壊れたリンクの検出と修復支援を行います' translates to 'Detects broken links and provides repair support.' This specifies both detection and repair assistance, though it doesn't explicitly differentiate from sibling tools like 'analyze_backlinks' or 'link_notes' in terms of scope or method.
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, context, or exclusions, leaving the agent to infer usage based on the purpose alone. For example, it doesn't clarify if this is for proactive maintenance or reactive fixes, or how it differs from 'analyze_backlinks'.
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 the action ('list all tags') but doesn't disclose behavioral traits like whether it returns a flat list or structured data, pagination, performance characteristics, or error conditions. The description is minimal and lacks operational context.
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 a single, efficient sentence in Japanese that directly states the tool's purpose with no wasted words. It's appropriately sized for a simple, parameter-less tool and is front-loaded with the core action.
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 the tool's simplicity (0 parameters, no annotations, no output schema), the description is minimal but adequate for basic understanding. However, it lacks context about the return format (e.g., list structure, data types) and doesn't address potential limitations or usage scenarios, leaving gaps for an AI agent to infer behavior.
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, and schema description coverage is 100% (since there are no parameters to describe). The description doesn't need to add parameter semantics, so a baseline of 4 is appropriate as it doesn't introduce confusion or redundancy.
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 ('list') and resource ('all tags in Obsidian vault') in Japanese. It's specific about what the tool does, though it doesn't explicitly differentiate from sibling tools like 'rename_tag' or 'search_files' which might also involve tags.
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, context, or compare with sibling tools like 'search_files' which might also retrieve tag-related information.
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 states it retrieves a list, implying a read-only operation, but doesn't specify whether this requires authentication, what format the list returns (e.g., names, metadata), if there are rate limits, or if it's paginated. For a tool with zero annotation coverage, 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence in Japanese that directly states the tool's purpose with zero wasted words. It's appropriately sized for a simple list-retrieval tool and front-loads the core functionality.
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 simplicity (0 parameters, no output schema, no annotations), the description is minimally adequate. It states what the tool does, but lacks details on return format, authentication, or error handling. For a basic list tool, it meets the minimum viable threshold but could be more complete by addressing behavioral aspects.
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, and schema description coverage is 100% (though empty). The description doesn't need to compensate for any parameter documentation gaps. It appropriately doesn't mention parameters since none exist, earning a baseline score of 4 for this dimension.
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 ('取得します' - retrieves) and resource ('TEMPLATEフォルダ内の利用可能なテンプレート一覧' - list of available templates in TEMPLATE folder). It's specific about what it retrieves (templates from a specific folder), but doesn't explicitly distinguish it from sibling tools like 'create_note_from_template' or 'search_files' which might also involve templates.
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 when this tool is appropriate versus using 'search_files' for templates or 'create_note_from_template' which might involve template selection. There's no context about 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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