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 effectively describes key behaviors: processing multiple images in batch, skipping non-image files silently, handling duplicates with warnings by default, and returning a summary table. It doesn't mention potential rate limits, authentication requirements, or error handling for invalid paths.

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 well-structured and appropriately sized. It begins with the core purpose, then explains the processing behavior, provides clear parameter documentation, and ends with the return value. Every sentence adds essential information with zero wasted content.

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 (batch processing with file type filtering and duplicate handling), no annotations, and the presence of an output schema, the description is remarkably complete. It explains what the tool does, how it behaves, what parameters mean, and what it returns, making the output schema's details about the 'summary table' sufficient.

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 significant value beyond the input schema, which has 0% description coverage. It clearly explains both parameters: 'folder_path' as 'Absolute path to the folder containing journal photos' and 'force' with detailed behavior about overwriting existing captures vs. skipping duplicates with warnings. This fully compensates for the schema's lack of descriptions.

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 with specific verbs ('Process all journal page photos', 'Finds every image file', 'runs the full upload pipeline') and identifies the resource ('in a folder at once'). It distinguishes this bulk operation from the sibling 'upload_capture' tool which likely handles single uploads.

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 about when to use this tool ('Process all journal page photos in a folder at once') and what it does with non-image files. However, it doesn't explicitly state when NOT to use it or mention alternatives like the 'upload_capture' sibling tool for single uploads.

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 the full burden. It discloses that the tool aggregates data across all entries and that output quality depends on input volume, which are useful behavioral traits. However, it doesn't mention performance characteristics, error conditions, or specific output format details, leaving some gaps 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.

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

The description is front-loaded with the core purpose in the first sentence, followed by elaboration in two concise sentences. Every sentence adds value by explaining scope, aggregation details, and data dependency, with zero wasted words or repetition.

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 has 0 parameters, no annotations, and an output schema exists, the description provides sufficient context for understanding its purpose and usage. It covers what the tool does and how input volume affects results, though it could briefly hint at output structure to fully leverage the output schema.

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 0 parameters with 100% coverage, so no parameter documentation is needed. The description appropriately focuses on the tool's function without redundant parameter info, earning a high baseline score for not introducing unnecessary complexity.

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 with specific verbs ('Analyze recurring patterns', 'Aggregates symbols, emotions, and themes') and resources ('all Dream Capture entries', 'DC pages'). It distinguishes itself from siblings by focusing on pattern analysis rather than upload, export, search, or other operations.

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 context by stating 'The more DC pages you upload, the more meaningful the patterns become,' suggesting it's best used with substantial data. However, it doesn't explicitly state when to use this tool versus alternatives like 'get_stats' or 'find_connections,' nor does it provide exclusions or prerequisites.

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 the full burden and does well by disclosing key behaviors: it exports data without writing a file (clarifying output as a string), supports filtering by tag, and specifies default values. However, it misses details like rate limits, authentication needs, or error handling, which would enhance transparency.

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 appropriately sized and front-loaded, starting with the core purpose, followed by parameter explanations and return details in a structured format. Every sentence adds value without redundancy, making it efficient and easy to understand.

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 moderate complexity, no annotations, and the presence of an output schema (which handles return values), the description is complete enough. It covers purpose, parameters, and behavioral aspects like no file writing, leaving no critical gaps for agent understanding.

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 significant meaning beyond the input schema, which has 0% description coverage. It explains the purpose of 'format' (Markdown or JSON with default) and 'tag_filter' (optional filtering with an example), compensating fully for the schema's lack of details and providing clear parameter semantics.

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 specific action ('Export all captures or a tag-filtered subset') and the output formats ('as Markdown or JSON'), distinguishing it from siblings like 'search_captures' or 'list_by_tag' which likely have different functions. It precisely defines what the tool does without being vague or tautological.

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 for usage by mentioning the tag-filtering option and default format, but it does not explicitly state when to use this tool versus alternatives like 'export_study_deck' or 'search_captures'. It implies usage for exporting data but lacks explicit comparisons or exclusions.

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 the full burden. It discloses key behavioral traits: it transforms questions into flashcards with specific formatting (front, back, tags), outputs as tab-separated CSV, and explicitly states 'No file is written to disk.' This covers output format and side effects, though it doesn't mention rate limits or authentication needs.

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 well-structured and appropriately sized. It front-loads the core purpose, then details the transformation process, output compatibility, and parameter usage. Every sentence adds value with no redundancy, making it efficient and easy to parse.

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 moderate complexity, no annotations, and an output schema present, the description is complete. It explains what the tool does, how it processes data, output format, parameter usage, and behavioral constraints. With an output schema handling return values, no additional explanation of outputs is needed.

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 0% description coverage, so the description must compensate. It adds detailed meaning for the single parameter 'tag_filter': explains it's optional, provides an example ('machine-learning'), and clarifies that leaving it blank exports all questions. This fully documents the parameter beyond the bare 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's purpose: 'Export your open questions as a portable study deck (tab-separated CSV).' It specifies the verb 'export' and the resource 'open questions,' and distinguishes it from siblings like 'export_captures' by focusing on study decks with flashcards from journal questions.

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 for when to use this tool: for exporting open questions as study decks, compatible with tools like Anki and Quizlet. It doesn't explicitly mention when not to use it or name alternatives, but the focus on flashcards from journal questions implies it's for study purposes rather than general data export.

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 the full burden of behavioral disclosure. It states the tool 'shows' connections, implying a read-only operation, but doesn't clarify if this requires specific permissions, what the output format is (though an output schema exists), or any rate limits or side effects. 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.

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

The description is highly concise and well-structured: it starts with the core purpose in the first sentence, then provides parameter details in a separate 'Args' section. Every sentence adds value without redundancy, making it easy to parse and understand quickly.

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 low complexity (one parameter), the presence of an output schema (which handles return values), and no annotations, the description is reasonably complete. It covers the purpose, parameter semantics, and implied usage context. However, it could improve by addressing behavioral aspects like permissions or output structure, but the output schema mitigates some of this need.

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 meaningful context for the single parameter 'capture_id', explaining it's a 'numeric ID returned by upload_capture or search_captures'. This clarifies the parameter's source and type beyond what the schema provides (which has 0% description coverage and only states it's an integer). Since there's only one parameter and the description compensates well for the low schema coverage, a score of 4 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: 'Show all connections for a specific capture' with specific details about what constitutes connections ('tag overlap and @-references'). It distinguishes this from siblings like 'search_captures' or 'list_by_tag' by focusing on connections within a single capture rather than searching or listing captures. However, it doesn't explicitly contrast with all siblings, so it's not a perfect 5.

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 context by specifying that 'capture_id' comes from 'upload_capture or search_captures', suggesting when to use this tool after those operations. However, it doesn't provide explicit guidance on when to choose this over alternatives like 'suggest_synthesis' or 'dream_patterns', nor does it state any exclusions or prerequisites beyond the ID requirement.

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 the full burden of behavioral disclosure. It describes the tool as a read operation ('Return all Synthesis entries') and specifies the output format and ordering, which is helpful. However, it lacks details on potential limitations like pagination, rate limits, or error conditions, which are important for a tool that might return large datasets.

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 appropriately sized and front-loaded, with the first sentence stating the core functionality clearly. Each subsequent sentence adds meaningful context without redundancy, such as detailing the returned fields and the tool's purpose for tracking thinking evolution. There is no wasted text, making it efficient and well-structured.

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 (0 parameters, no annotations, but with an output schema), the description is mostly complete. It explains what the tool does, the data returned, and its use case. Since an output schema exists, it need not detail return values, but it could benefit from mentioning behavioral aspects like performance or data scope. Overall, it covers the essentials well.

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 0 parameters with 100% coverage, so the schema fully documents the absence of inputs. The description adds value by explaining that no parameters are needed and implicitly clarifies the scope ('all Synthesis entries'), which compensates for the lack of parameters. It does not need to detail parameters, so a baseline of 4 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 with specific verbs ('Return all Synthesis entries') and resources ('Synthesis entries'), distinguishing it from siblings like 'search_captures' or 'find_connections' by focusing exclusively on SYN entries. It explicitly mentions what data is returned ('breakthrough field, patterns identified, $ insight tags, and topic tags') and the chronological ordering, making the 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 provides clear context for when to use this tool ('to see how your thinking has evolved and which ideas led to the biggest discoveries'), implying it's for retrospective analysis of breakthroughs. However, it does not explicitly state when not to use it or name alternatives among sibling tools, such as 'search_captures' for filtered results, leaving some guidance gaps.

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 for behavioral disclosure. While 'Return an overview' implies a read-only operation, it doesn't specify whether this requires authentication, has rate limits, returns real-time or cached data, or what format the output takes. The description lists content categories but doesn't describe behavioral traits like performance characteristics or access requirements.

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 extremely concise and well-structured in a single sentence. It front-loads the core purpose ('Return an overview of your knowledge base') followed by specific components in a clear list format. Every word earns its place with no redundancy or unnecessary elaboration.

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 has 0 parameters, 100% schema coverage, and an output schema exists, the description's job is simplified. It adequately describes what the tool returns (an overview with specific components). However, for a reporting tool with no annotations, it could benefit from more behavioral context about data freshness, authentication needs, or output format since the output schema isn't visible in the evaluation context.

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 tool has 0 parameters, and the input schema has 100% description coverage (though empty). The description appropriately doesn't discuss parameters since none exist. It focuses on what the tool returns rather than what it accepts, which is correct for a parameterless tool.

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: 'Return an overview of your knowledge base' with specific components listed (capture counts, top tags, open questions, key insights, date range). It uses a specific verb ('Return') and resource ('knowledge base'), but doesn't explicitly differentiate from sibling tools like 'knowledge_progress' or 'journal_health' which might provide related metrics.

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 guidance on when to use this tool versus alternatives. With sibling tools like 'knowledge_progress', 'journal_health', and 'get_breakthroughs' that might provide overlapping or complementary metrics, there's no indication of when this overview tool is preferred or what distinguishes it from other reporting 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?

With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes what the tool returns (health score and actionable recommendations) and outlines the specific metrics tracked, giving good insight into its behavior without contradictions.

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 well-structured and front-loaded with the main purpose, followed by a bulleted list of tracks and a summary of returns. Every sentence adds value, with no wasted words, making it highly efficient and 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?

Given the tool's complexity (health assessment with multiple metrics), no annotations, and an output schema present, the description is complete. It thoroughly explains what the tool does, the metrics it tracks, and what it returns, making additional details unnecessary.

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 0 parameters with 100% coverage, so no parameter documentation is needed. The description adds value by explaining the tool's focus on journal practice health, which compensates for the lack of parameters, earning a baseline 4 for zero parameters.

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 as providing a KPI dashboard and coaching recommendations for journal practice, with specific metrics listed (capture velocity, insight velocity, days since last Review entry, etc.). It distinguishes itself from siblings by focusing on health assessment rather than operations like uploading, exporting, or searching.

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 monitoring and improving journal practice health, but does not explicitly state when to use this tool versus alternatives (e.g., get_stats for general statistics or knowledge_progress for progress tracking). It provides clear context but lacks explicit exclusions or named 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?

With no annotations provided, the description carries full burden for behavioral disclosure. It describes what the tool does (track progression and show status movement) but lacks details on output format, data freshness, rate limits, authentication needs, or whether it's read-only or has side effects. The mention of 'over time' hints at historical data, but this isn't elaborated.

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 well-structured and appropriately sized, with a clear purpose statement followed by parameter details. Every sentence adds value, though the second sentence could be slightly more concise by combining ideas about status progression.

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 has an output schema (which handles return values), one parameter with good description coverage, and no annotations, the description is reasonably complete. It explains the tool's purpose, parameter usage, and context, though it could benefit from more behavioral details like data scope or limitations.

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 significant value beyond the input schema, which has 0% description coverage. It explains the 'topic' parameter's purpose (to filter by topic tag), provides an example ('calculus'), clarifies it's optional, and specifies that leaving it blank shows all topics. This fully compensates for the schema's lack of documentation.

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: 'Track Knowledge Status progression across your Review (REV) entries' and explains it visualizes how topics move through status levels over time. It specifies the resource (REV entries) and verb (track progression), though it doesn't explicitly differentiate from sibling tools like 'get_stats' which might also provide progress-related data.

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 context by mentioning 'REV entries' and 'Knowledge Status field', suggesting it's for tracking learning progress in a review system. However, it doesn't provide explicit guidance on when to use this tool versus alternatives like 'get_stats' or 'journal_health', 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.

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 describes the tool's behavior as browsing captures by tag, with optional prefix filtering, but lacks details on permissions, rate limits, pagination, or error handling. The examples add some context, but key behavioral traits are not disclosed.

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 well-structured and front-loaded, starting with the core purpose, followed by usage guidelines with examples, and ending with parameter details. Every sentence adds value, with no redundant information, making it efficient and 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?

Given the tool's moderate complexity (2 parameters, no annotations, but with output schema), the description is largely complete. It covers purpose, usage, and parameters thoroughly. However, it lacks behavioral details like permissions or rate limits, which are important since no annotations exist. The output schema mitigates this gap somewhat, but not 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?

Schema description coverage is 0%, so the description must compensate. It provides detailed semantics for both parameters: 'tag' is explained as 'Tag value to look up (without the prefix character),' and 'prefix' is described with examples showing allowed values (#, @, !, ?, $, ->) and its effect ('narrow the search'). This fully documents the parameters 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's purpose: 'Browse all captures that carry a specific tag — no text query required.' It specifies the verb ('browse'), resource ('captures'), and key constraint ('no text query required'), which distinguishes it from sibling tools like 'search_captures' that likely require text queries.

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 explicit guidance on when to use this tool: 'Use this to find every note related to a topic, source, question, or insight.' It also distinguishes it from alternatives by noting 'no text query required,' implying that 'search_captures' is for text-based searches, and includes examples with different prefixes to illustrate various use cases.

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 burden and does well by explaining search matches terms in journal text, works across all template types, and handles natural language queries. It could improve by mentioning pagination or result format, but covers core behavior adequately.

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?

Well-structured with purpose statement, usage guidelines, examples, parameter details, and a note - all in compact sentences. Every section adds value without redundancy. The information is front-loaded with the core purpose first.

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 4 parameters with 0% schema coverage and no annotations, the description provides complete context: purpose, usage, behavioral details, parameter semantics, and sibling differentiation. The output schema exists, so return values don't need explanation. This is comprehensive for a search tool.

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 0%, so the description must compensate fully. It provides detailed parameter explanations beyond schema titles: query is for concept/keyword/phrase, tag_filter narrows results with examples, date_from/date_to are ISO date bounds with examples. This adds substantial semantic value.

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 searches journal entries by concept, keyword, or phrase across all template types. It specifies the verb 'search' and resource 'journal entries', distinguishing it from sibling tools like list_by_tag which is mentioned for tag-only browsing.

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?

Explicit guidance is provided: use for finding notes by idea rather than tag, when users ask to find notes or explore topics. It distinguishes from list_by_tag for tag-only browsing and provides concrete examples of when to use it.

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 the full burden of behavioral disclosure. It mentions scanning and identifying clusters but doesn't describe how the tool behaves: e.g., whether it returns a list of topics with metadata, how it handles large datasets, or if it requires specific permissions. For a tool with no annotation coverage, this leaves significant gaps in understanding its operation.

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 appropriately sized and front-loaded: the first sentence states the core purpose, and the second provides parameter details. There is no wasted text, and the structure is clear and efficient, making it easy for an agent to parse quickly.

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 has an output schema (which likely describes return values), the description doesn't need to explain outputs. However, with no annotations and only one parameter, the description covers the purpose and parameter semantics adequately but lacks behavioral details (e.g., how clusters are identified, performance considerations). It's minimally viable but could be more complete for a clustering tool.

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 meaningful context for the single parameter 'min_captures', explaining it as 'Minimum number of RC entries on a topic to flag it' and noting the default value. Since schema description coverage is 0% (the schema only provides title and type), the description compensates well by clarifying the parameter's purpose and default, though it could add more about valid ranges or effects.

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: 'Scan your Rapid Capture entries and identify topic clusters ready to be synthesized into a SYN page.' This specifies the verb ('scan', 'identify'), resource ('Rapid Capture entries'), and outcome ('topic clusters ready to be synthesized'). However, it doesn't explicitly differentiate from sibling tools like 'search_captures' or 'find_connections', 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.

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., having Rapid Capture entries), exclusions, or comparisons to sibling tools like 'search_captures' or 'find_connections'. The agent must infer usage context solely from the purpose statement.

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 burden and does well. It discloses multiple behavioral traits: the multi-step processing pipeline (OCR, parsing, extraction, storage, copying, connection detection), the overwrite behavior controlled by the force parameter, and the return format (summary with strongest connection). It doesn't mention error handling, rate limits, or authentication needs.

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 perfectly structured and concise. First sentence states the comprehensive purpose, followed by clearly labeled Args and Returns sections. Every sentence earns its place by providing essential information without 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 (multi-step processing pipeline), no annotations, and the presence of an output schema (which handles return values), the description is complete enough. It covers purpose, parameters, behavior, and return summary, providing all necessary context for an agent to use this tool effectively.

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 0%, so the description must compensate fully. It provides excellent parameter semantics: image_path is explained as 'Absolute path to the image file (JPG, PNG, TIFF, etc.)' and force as 'Set to True to overwrite an existing capture with the same template ID (default False — warns instead).' This adds crucial meaning beyond the bare 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's purpose with specific verbs (process, run OCR, parse, extract, store, copy, detect) and resources (journal page photo, capture, knowledge base). It distinguishes from siblings like bulk_upload (batch vs single) and find_connections (detection only vs full processing).

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 for when to use this tool (processing a single journal page photo for OCR and storage). It doesn't explicitly mention when not to use it or name alternatives, though bulk_upload is an obvious sibling for batch processing. The force parameter guidance implies usage for overwriting existing captures.

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