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

Since no annotations are provided, the description carries the full burden. It discloses that AST parsing is used, diagrams are generated by default, and AI analysis requires an API key. However, it does not detail potential side effects or output format, though the tool is likely read-only.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is two sentences with no wasted words. It front-loads the core purpose and then expands on key features. Every sentence earns its place.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given 6 parameters, 100% schema coverage, no output schema, and no annotations, the description covers the tool's capabilities well, including the two modes (AST-only vs AI) and diagram generation. Minor missing detail on return format but otherwise complete.

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

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

Schema description coverage is 100%, so baseline is 3. The description adds context like 'auto-detected' for language and 'AI-powered analysis for quality, security, and suggestions' for useAI, enriching the schema but not critically.

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

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

The description uses specific verbs and nouns: 'performs deep code analysis using AST parsing', 'extracts functions, classes, imports', 'generates Mermaid diagrams', and 'supports AI-powered analysis'. It clearly distinguishes itself from sibling tools like muse_git and muse_template, which have different purposes.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage for code analysis but does not explicitly state when to use this tool versus alternatives, nor does it provide when-not or exclude conditions. It lacks explicit guidance on prerequisites or contexts.

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

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

No annotations exist, so the description must disclose behavioral traits. It mentions 'apply' which implies session modification, but lacks details on persistence, idempotency, rate limits, or side effects. The description adds minimal behavioral context beyond the action names.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is two sentences: first states the overarching purpose, second enumerates actions. It is front-loaded, concise, and every word adds value. No redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The tool has 13 parameters and 4 actions, but the description does not explain how actions map to parameter sets, expected workflows, or return values. Since there is no output schema, the agent lacks information about what the tool returns (e.g., suggested tags list). The description is incomplete for effective use.

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

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

Schema description coverage is 100%, providing detailed param explanations. The description adds value by mapping actions to parameters (e.g., suggest/apply need sessionId, content), but the schema already specifies this via param descriptions. The description does not add significant new semantics beyond the schema.

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

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

The description clearly states the tool auto-tags sessions and lists four actions (suggest, apply, train, config). It specifies the verb and resource, making the main purpose obvious, though it does not explicitly differentiate from sibling tools like muse_analyze_code.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance on when to use this tool vs alternatives. There's no mention of prerequisites, when not to use it, or comparison with sibling tools. The action enumeration is information but not usage guidance.

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

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

The description does not disclose behavioral traits beyond what is implied by the actions. With no annotations, it fails to mention potential destructive nature, authorization requirements, rate limits, or error handling nuances beyond stopOnError. For a tool that executes operations, more transparency is needed.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is three sentences and clearly communicates the tool's purpose and key features. It is well-structured and front-loaded with the action list. Minor improvement could be trimming redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (10 parameters, no output schema), the description lacks information about return values, response format, or error details. It covers usage but misses critical context for an agent to anticipate behavior fully.

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

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

The input schema has 100% coverage with descriptions for all parameters. The description does not add extra semantic value beyond summarizing the actions; thus 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.

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

The description clearly identifies the tool as a batch executor for multiple tool operations, listing specific actions (execute, preview, status, cancel, history) and highlighting its ability to handle sequential/parallel execution with dependency management. It distinguishes itself from sibling tools which are individual operation tools.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides clear context on when to use this tool (for batching multiple operations) and explains actions and modes. However, it lacks explicit 'when not to use' guidance or mention of alternatives (e.g., calling tools individually).

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

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

With no annotations, the description carries full burden. It lists key features: automatic language detection, duplicate removal, and statistics. However, it does not mention side effects (e.g., does it modify state?), behavior when both codeBlocks and rawText are provided, or whether the tool is idempotent. The two sentences add some context but are not comprehensive.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is two sentences and 22 words, very concise. It front-loads the primary purpose and then lists key features. Every word earns its place; no redundant or vague phrasing.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Despite 7 parameters (1 required) and no output schema, the description does not explain what the output looks like (e.g., structured JSON), the relationship between codeBlocks and rawText (mutual exclusivity?), or the behavior when parameters conflict. A tool of this complexity needs more context to be fully understood.

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

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

Schema coverage is 100%, so each parameter is described in the schema. The description only summarizes features (auto-detect, duplicate removal, stats) that directly correspond to parameters. It adds no new meaning beyond what the schema provides, so the baseline of 3 is appropriate.

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

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

The description clearly states the tool collects code blocks and conversation summaries into structured context for documentation, with verbs 'collects' and 'supports'. It distinguishes from sibling tools like muse_analyze_code (analysis) and muse_auto_tag (tagging). However, 'structured context' is somewhat vague and could be more specific about the output format.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description does not provide any guidance on when to use this tool vs alternatives. It neither states when it is appropriate nor mentions any prerequisites or complements. This leaves the agent without information to decide among siblings.

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

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

No annotations are provided, so the description carries full burden. It mentions 'creates' implying a write operation, but does not disclose side effects (e.g., file creation, database write), permission requirements, or overwrite/append behavior. Lacks transparency for a creation tool.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Single sentence, no wasted words. Concise and directly states the purpose. Could be slightly more structured but remains efficient.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With 7 parameters and no output schema, the description is too brief. It does not explain where logs are saved, their format details beyond what's in schema, or how the tool interacts with the system. Incomplete for a tool with nested objects and multiple options.

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

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

Schema description coverage is 86%, so the input schema already documents most parameters. The description adds 'daily or session-based', which aligns with the options.logType enum but does not provide additional meaning beyond the schema. Baseline score of 3 is appropriate.

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

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

Description uses specific verb 'creates' and identifies the resource 'vibe coding session logs' with type differentiation ('daily or session-based'). Clearly states what the tool does, but does not distinguish from sibling tools like muse_export_session or muse_session_history.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance on when to use this tool versus alternatives (e.g., muse_export_session). No context on prerequisites or exclusions. The usage is only implied through the description, which is insufficient given the number of sibling tools.

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

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

No annotations are provided, and the description does not disclose behavioral traits beyond the basic purpose. It does not state that the tool is non-destructive (read-only), nor does it mention authorization needs, rate limits, or side effects like file writing. The description is too minimal for a mutation-free guarantee.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Two efficient sentences with no wasted words. Front-loads the verb and resource, lists formats, and gives use cases. Ideal conciseness for a focused description.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For an export tool with no output schema and well-documented parameters, the description covers the core functionality and use cases. It could mention that the tool does not modify sessions or what happens when outputPath is provided, but overall it is adequate.

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

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

Schema description coverage is 100%, so parameters are well documented in the schema. The description adds no additional meaning beyond what the schema already provides. Baseline score of 3 is appropriate as the schema handles the burden.

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

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

Clearly states the tool exports vibe coding sessions to Markdown, JSON, HTML, and mentions use cases (documentation, backups, reports). However, it does not explicitly differentiate from siblings like muse_create_session_log or muse_generate_dev_document, which also produce documentation.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Provides explicit use cases: 'creating shareable documentation, backups, or reports from session history.' Does not mention when not to use it or alternatives, but the context is clear enough for an agent.

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

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

Annotations are absent, and the description does not disclose behavioral traits like whether the tool saves files to disk, overwrites existing files, requires authentication, or has rate limits. It only says 'Generates' without explaining what happens to the output.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, concise sentence that efficiently communicates the tool's purpose and key capabilities. It is not verbose, but could be improved by structuring the list of features for readability.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With 19 parameters (many nested) and no output schema, the description is too brief. It fails to explain the return value or behavior, leaving agents without enough context to understand the tool's full functionality.

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

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

Schema description coverage is 100%, so baseline is 3. The description adds some context by listing supported features (badges, API reference, FAQ) that correspond to parameters, but does not explain parameter formats or relationships beyond the schema.

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

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

The description clearly states the verb 'Generates' and the resource 'dev documents' (README, DESIGN, TUTORIAL, CHANGELOG) in Markdown format. It lists supported features (multiple languages, badges, API reference, FAQ) and is distinct from sibling tools like 'muse_analyze_code' or 'muse_publish_document'.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance is provided on when to use this tool versus alternatives, such as when to generate vs publish documents. No prerequisites, context, or exclusions are mentioned.

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

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

No annotations are provided, so the description bears full responsibility for behavioral disclosure. It does not mention whether actions are read-only or destructive, any side effects (e.g., linking to a session may modify data), authentication requirements, or rate limits. This is a significant gap for a multi-action tool.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise (two sentences) and front-loaded with the tool's purpose. It lists key capabilities without unnecessary detail, making it easy to scan.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Despite a high parameter count (25) and multiple actions, the description is too brief to provide complete guidance. It does not explain how actions relate to parameters, what each action returns, or how to combine parameters effectively. With no output schema or annotations, the description should offer more context to compensate for the tool's complexity.

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

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

Schema description coverage is 100%, so the schema already documents all 25 parameters adequately. The description adds overall context (e.g., listing actions) but does not enhance understanding of individual parameters beyond what the schema provides.

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

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

The description clearly states it is a 'Git integration for vibe coding sessions' and lists specific capabilities: getting repository status, commit history, diffs, branch info, capturing snapshots, and extracting design decisions. This distinguishes it from sibling tools that focus on analysis, tagging, or session management.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage for Git-related tasks within coding sessions but does not explicitly guide when to use this tool versus alternatives like muse_summarize_design_decisions. There is no mention of prerequisites, when not to use it, or comparison with siblings.

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

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

With no annotations provided, the description carries full responsibility for transparency. It discloses that the tool handles various markdown elements, but it does not mention side effects (e.g., whether the original document is modified), return type (likely a string), or error conditions. The description is moderately transparent but could be improved.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, well-structured sentence that front-loads the main action and key details. Every word adds value; there is no redundancy or extraneous information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description covers the tool's purpose and supported elements but lacks information about the output format (e.g., returns a string) and does not discuss error handling or limits. For a tool with nested options and no output schema, the description should provide more context about the result.

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

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

The schema describes all parameters and sub-properties, so the description adds limited new meaning. However, it reinforces that the platform parameter targets specific platforms and hints at the scope of options. Since schema coverage is high, a baseline score of 3 is appropriate.

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

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

The description clearly states the tool's purpose: converting Markdown documents for specific platforms (Notion, GitHub Wiki, Obsidian) and lists the elements handled. It distinguishes itself from sibling tools like muse_analyze_code or muse_publish_document by focusing on normalization for target platforms.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description lacks guidance on when to use this tool versus alternatives. For example, it does not mention that muse_publish_document might be more suitable for full publication workflows, or provide any exclusion criteria. The usage context is only implied by the platform enumeration.

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

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

The description uses the vague verb 'manages' and emphasizes 'save', which underrepresents the full range of actions (create, get, update, delete, list, setActive, getActive, clone). With no annotations, the description carries the burden of behavioral disclosure but remains incomplete and somewhat misleading.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise: two sentences that avoid fluff and front-load the purpose. However, it is so brief that it omits important aspects of the tool's functionality. For a tool with 19 parameters, this brevity borders on under-specification.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (19 parameters, nested objects, multiple actions, no output schema), the description is too minimal. It does not explain the profile lifecycle, the significance of actions like setActive/getActive, or how profiles integrate with other tools. The description leaves significant gaps for an agent.

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

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

Schema description coverage is 100%, so baseline is 3. The description mentions 'documentation, code analysis, and publishing' which aligns with schema parameters, but adds no extra meaning beyond what the schema already provides. It does not enhance understanding of parameter relationships or constraints.

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

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

The description clearly states the tool manages project profiles for vibe coding sessions, specifying the domains (documentation, code analysis, publishing). This distinguishes it from sibling tools like muse_analyze_code or muse_publish_document, which operate on profiles rather than managing them.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no explicit guidance on when to use this tool versus alternatives. It does not mention that profiles are prerequisites for other tools, nor does it explain when to create, get, update, or list. Context is implied but missing direct usage instructions.

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

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

With no annotations, the description carries full burden for behavioral disclosure. It only states the action (publish) but omits details like whether it creates or updates, authentication requirements, rate limits, or handling of failures. This lack of transparency is a significant gap for a tool interacting with external APIs.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, clear sentence that front-loads the core purpose. It is efficient, though slightly oversimplified, and could benefit from a brief note on when to use or a reference to the schema for details.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (multiple platforms, nested options, no output schema), the description lacks crucial context such as return values, error handling, prerequisites, or authentication setup. It is inadequate for an agent to fully understand the tool's behavior and expected outcomes.

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

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

The description adds minimal value beyond the input schema. The schema already provides descriptions for all parameters, including nested options, and the enum list for platform. The description merely echoes the platforms without clarifying parameter usage or providing additional context.

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

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

The description clearly states the verb 'Publishes' and the resource 'generated documents', listing all six target platforms. It effectively differentiates from sibling tools, which are mostly analysis and generation tools, making the tool's purpose unambiguous.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies use when you need to publish a document to an external platform, and the sibling tools are distinct enough not to cause confusion. However, it does not explicitly mention when not to use it or provide alternatives.

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

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

No annotations are provided, and the description does not disclose behavioral traits such as persistence behavior, side effects, or permission requirements. The word 'manages' is vague.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single sentence that is clear and not overly verbose. It efficiently states the tool's purpose without unnecessary detail.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given 15 parameters, no output schema, and no annotations, the description is too brief. It does not explain return values or behavior for each action, leaving significant gaps for a tool of this complexity.

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

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

Schema coverage is 100%, so all parameters have descriptions. The description adds no additional meaning beyond the schema, so baseline 3 is appropriate.

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

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

The description clearly states it manages vibe coding session history with save, retrieve, search, and manage actions. However, it does not differentiate from siblings like muse_session_stats, which also deal with sessions.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance on when to use this tool versus alternatives like muse_create_session_log or muse_session_stats. The description is generic and does not specify prerequisites or context.

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

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

No annotations are provided, so the description must fully disclose behavioral traits. It states the tool 'provides analytics' but does not mention whether it is read-only, requires authentication, or has rate limits. For a stats tool, it is likely safe, but the description lacks explicit behavioral context, earning a 2.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is one sentence plus a parenthetical list of actions. Every word is necessary; there is no fluff. It is front-loaded with the core purpose and quickly enumerates the available actions. Excellent conciseness.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The tool has 9 parameters and no output schema. The description explains what each action provides (e.g., 'language breakdown'), but it does not describe the return format or structure of the analytics. Given the complexity, the description is adequate but leaves gaps on output expectations, earning a 3.

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

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

Schema description coverage is 100%, providing a baseline of 3. The description adds value by elaborating on the 'action' parameter's enum values (e.g., 'overview' as summary stats, 'languages' as language breakdown). This clarifies what each action returns beyond the schema's simple 'Type of statistics to retrieve'. The extra context justifies a 4.

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

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

The description clearly states the tool provides analytics and insights about coding sessions and lists specific actions like overview, languages, timeline, etc. This distinguishes it from sibling tools such as muse_session_history (which likely returns raw session data) and muse_analyze_code (code analysis). The verb 'provides' and resource 'analytics and insights' are specific and informative.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description does not explicitly state when to use this tool versus alternatives like muse_session_history or muse_analyze_code. However, the listed actions (overview, languages, timeline, etc.) imply different use cases, so an agent can infer context. No when-not or alternative guidance is provided, which limits the score to 3.

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

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

With no annotations, the description carries full burden. It discloses features (language support, importance scoring, keyword extraction) and prerequisites (ANTHROPIC_API_KEY for AI). However, it does not mention any side effects or rate limits.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is three sentences, front-loading the main purpose, then listing features, then a config hint. No wasted words, efficient and clear.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given 7 parameters, no output schema, and no annotations, the description adequately covers all major features and behaviors. It provides enough context for an AI agent to understand and use the tool correctly.

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

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

Schema coverage is 100%, but the description adds context beyond individual parameter descriptions by summarizing the overall purpose and binding parameters together. It also highlights the useAI parameter's requirement, which is not detailed in the schema.

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

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

The description clearly states it 'Extracts and analyzes key architectural and design decisions from conversation logs,' with specific verb and resource. This distinguishes it from sibling tools like muse_analyze_code and muse_session_stats.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description does not provide guidance on when to use this tool versus alternatives, nor does it mention when not to use it. It only mentions setting useAI for enhanced analysis but lacks explicit usage context.

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

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

No annotations exist, so the description must cover behavioral traits. It only lists actions without mentioning idempotency, side effects, authorization needs, or what happens on errors. The lack of details about actions like 'delete' or 'update' is a gap.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise: one sentence followed by a list of actions. It is front-loaded with the core purpose. However, the list could be more structured (e.g., grouping actions by type), but still minimal waste.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With 13 parameters, nested objects, and no output schema, the description is insufficient. It does not explain the template lifecycle, how actions interact, or what results to expect. The schema covers parameter details, but the overall picture is missing.

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

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

Schema description coverage is 100%, so baseline is 3. The description adds no extra parameter context beyond the schema. The action list is redundant with the schema's enum. No new meaning is added.

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

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

The description states 'Manages custom document templates with variable substitution', which clearly identifies the tool's domain. The list of actions (create, get, etc.) further specifies functionality. However, it does not differentiate from sibling tools beyond the subject matter, which are all distinct anyway.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance is provided on when to use this tool versus alternatives. There are no prerequisites, exclusions, or context for selecting specific actions. The sibling tools are all different, but the description offers no decision-making support.

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