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

No annotations are provided, so the description must carry behavioral disclosure. It states 'add' but does not explain side effects (e.g., triggers entity extraction, overwrites existing, requires permissions) or return behavior, which is insufficient for a write 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 a single, concise sentence with no wasted words. It is front-loaded 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 no output schema and three parameters, the description should hint at what the tool returns (e.g., document ID or success message) and address edge cases like duplicate handling or file size limits. It lacks these details, making it incomplete for a file-adding 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 100%, so the baseline is 3. The description adds marginal value by confirming file types (PDF/text) matching the schema's 'Full path to PDF or text file', but it does not elaborate on how title and tags are used or provide additional semantic context 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 action 'Add' and the resource 'PDF or text file to the knowledge base', distinguishing it from siblings like add_documents_bulk (bulk) and scrape_url (from URL). However, it does not explicitly indicate it handles single documents only, which could be inferred but is not stated.

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 such as add_documents_bulk or scrape_url. No mention of prerequisites like file existence or permissions, leaving the agent without context for appropriate invocation.

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 responsibility for behavioral disclosure. It only states the action (adding documents) and a feature (glob patterns). There is no mention of whether the operation is reversible, permission requirements, or potential side effects. Given the lack of annotations, 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.

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

The description consists of two concise sentences that convey the core functionality without extraneous details. Every word earns its place, 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?

Given that the tool has 5 parameters, no output schema, and no annotations, the description is moderately complete. It covers the primary operation and a key feature but lacks details on return values, error scenarios, or performance considerations for bulk operations.

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% description coverage, so the baseline is 3. The description adds minimal value beyond the schema, merely restating the glob pattern feature. It does not provide additional context for parameters like 'tags' or 'skip_duplicates' that isn't already 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 the verb 'add' and resource 'multiple documents from a directory'. It also highlights a key feature 'Supports glob patterns for file matching'. This effectively distinguishes it from the sibling 'add_document' tool, which likely handles single documents.

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 bulk adding from a directory but does not explicitly state when to use this tool versus alternatives like 'add_document'. There is no mention of when not to use it or any prerequisites. Guidance is minimal.

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 provided, so description should disclose behavioral traits. Only mentions it calculates scores, nothing about side effects (read-only), permissions, or performance implications. For a graph analysis tool, 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.

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

Two sentences, front-loaded with action, no filler. All words add value.

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?

No output schema, but description does not specify return format (list of entities with scores?). Lacks details on result structure, which is critical for a computation 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 has 100% coverage with descriptions for all 4 parameters. Description adds conceptual value (explaining PageRank importance) but no additional parameter-level details beyond 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?

Clear verb-resource pair ('Calculate PageRank scores for entities') and states purpose (identify important entities). However, does not distinguish from sibling tool 'calculate_graph_centrality' which may overlap.

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 like calculate_graph_centrality or other analytics tools. Missing context about prerequisites or typical scenarios.

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 discloses key behaviors: RAG-based synthesis from multiple sources, and returns answer text with source references and confidence score. It sufficiently explains what the tool does, though it could mention potential limitations like accuracy.

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 sentences with no fluff; first sentence states purpose and method, second covers output. Front-loaded and 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?

The description covers purpose, method, and output details (answer text, source references, confidence score). No output schema is present, but the description compensates. Adequate for a QA tool with 3 well-documented parameters.

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 baseline is 3. The description adds context about using RAG, synthesizing from multiple sources, and returning citations/confidence, which enriches understanding beyond the parameter 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 'Answer questions about C64 documentation using RAG', specifying a specific verb and resource. It distinguishes itself from sibling search tools by mentioning RAG and synthesis from multiple sources with citations.

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 the tool is for answering questions with citations, but does not explicitly state when to use it over alternatives like search_docs or semantic_search, nor does it provide when-not-to-use 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?

Discloses AI analysis and LLM requirement, but does not explicitly confirm that tags are written to documents (though implied by 'auto-tag'). No mention of performance impacts, rate limits, or nondestructive nature. With no annotations, description carries full burden, leaving some ambiguity.

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?

Description is four sentences, each carrying essential information. No filler or redundancy. Front-loaded with the core purpose.

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 no output schema and no annotations, the description adequately explains what the tool does and its options. However, it does not describe what the tool returns (e.g., count of tagged documents) and assumes prior knowledge of LLM configuration. Minor gap for full completeness.

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 already provides 100% coverage for all 5 parameters with detailed descriptions. The description adds no additional parameter-specific meaning beyond the schema, so baseline 3 applies.

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 it performs bulk auto-tagging using AI, analyzing content and suggesting tags for multiple documents. Distinguishes from siblings like auto_tag_document by specifying 'Bulk' and 'multiple documents'.

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 clear usage context: initial organization or re-tagging collections, and notes ability to skip already-tagged and limit count. Lacks explicit when-not-to-use or alternative tool references, but overall guides appropriate use.

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 carries the burden. It discloses the use of AI analysis and the need for LLM config, but does not explain whether tags are saved, return format, or side effects like modifying the document.

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 only two sentences, with the first sentence stating the core purpose and the second adding relevant details. No 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?

Given no output schema, the description should clarify what the tool returns (e.g., list of tags, success status). It only says 'suggests relevant tags' without specifying output format or if tags are persisted.

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 covers all 4 parameters with descriptions. The tool description adds extra context about the categories of tags, which the schema lacks, thus providing additional meaning.

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 function: automatically generate tags for a document using AI analysis. It specifies the types of tags (hardware, programming, etc.) and distinguishes from siblings like auto_tag_all by focusing on a single 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?

The description mentions a prerequisite (LLM configuration) but does not explicitly state when to use this tool versus alternatives like suggest_tags. There is no guidance on when not to use it.

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

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

No annotations exist, so the description carries full burden. It does not disclose whether the graph is built on-the-fly, cached, or modifies underlying data. It also omits potential side effects, permissions needed, or performance implications. This is a significant gap for a tool that likely requires computation.

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, front-loaded with the core action, and free of extraneous information. Every word contributes to clarity.

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 is moderately complex (3 parameters, no output schema) but the description does not explain what the output looks like (e.g., format, structure of nodes and edges). Agents need to know the return value to use results. This is a serious omission.

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% – all three parameters are described in the input schema. The description does not add any meaning beyond the schema's descriptions (e.g., 'entity_types' is already explained). It meets the baseline expectation but does not enhance understanding.

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 explicitly states the tool constructs a knowledge graph from entities and relationships, and explains the graph representation (nodes and weighted edges). This clearly distinguishes it from sibling tools that analyze or compute metrics on graphs.

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 suggests using the tool to understand knowledge structure and find connections, but does not specify when to prefer this over alternatives like analyze_graph_pagerank or detect_graph_communities. No exclusions or when-not-to-use guidance is provided.

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 provided, so description must disclose behavior. It states the output (returns three centrality measures), but does not mention if the tool is read-only, any preconditions (e.g., graph must be built), or performance implications. Partially adequate.

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?

Extremely concise: two sentences, no filler. Front-loaded with purpose, then details. Every sentence adds value.

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 3 optional parameters and no output schema, the description covers the essential behavior (what it computes and returns). Minor gap: does not mention prerequisite that a graph must exist, 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?

All three parameters have schema descriptions (100% coverage), so the description adds little beyond restating the output. The baseline of 3 is appropriate as schema does the heavy lifting.

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 verb 'calculate' and resource 'centrality measures for entities in the knowledge graph'. Specifically names the three measures (betweenness, closeness, degree), distinguishing it from sibling tools like 'analyze_graph_pagerank' or 'compute_graph_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?

Description explains what the measures identify ('entities that bridge different parts...'), giving implicit usage context, but lacks explicit when-to-use or when-not-to-use guidance compared to 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?

The description discloses the tool detects modified files and can automatically re-index them. However, it lacks detail on whether the check operation is purely read-only or has side effects, what 'detects' means in terms of scanning, and any permission requirements. Since no annotations are provided, the description falls short of fully disclosing behavioral traits.

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 at two sentences with no extraneous information. It front-loads the core purpose and follows with the optional behavior, making it 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?

For a simple tool with one parameter and no output schema, the description adequately covers the main functionality and the key option. It lacks some detail on the detection process, but overall it provides sufficient context for an agent to understand what the tool does.

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 single parameter 'auto_update' is fully described in the schema with a default and description. The tool description adds little beyond the schema, merely restating the optional re-indexing behavior. Baseline 3 is appropriate given 100% 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 states the tool checks all indexed documents for updates and optionally re-indexes them. It uses specific verbs ('check', 'detects', 're-indexes') and identifies the resource ('indexed documents'). It distinguishes itself from sibling tools like 'check_url_updates' by specifying 'all indexed documents'.

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 explicit guidance on when to use this tool vs alternatives. With many sibling tools like 'add_document', 'remove_document', and 'rescrape_document', the description does not indicate when checking for updates is appropriate or when other tools should be used instead.

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?

Description explains the detection mechanism (comparing Last-Modified headers) but does not detail what happens after detection (e.g., whether it triggers rescraping automatically, status reporting, or side effects like updating internal state). With no annotations, the burden is higher; partial 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?

Two sentences packed with essential information: purpose, method, and detection capability. No redundant words; well-structured for quick parsing.

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?

No output schema and no mention of return value (e.g., what data is returned, whether it's a list of updated documents or a boolean). Missing details on behavior when auto_rescrape is enabled, leaving gaps for the 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 covers 100% of parameters with a clear description of 'auto_rescrape'. The tool description adds no additional meaning beyond what the schema already provides, 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?

Clearly states it checks all URL-sourced documents for updates using Last-Modified headers. The verb 'check' and resource 'URL-sourced documents' are specific and distinguish from sibling tools like 'check_updates' (likely different resource) and 'scrape_url' (action).

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 such as 'check_updates' or 'rescrape_document'. Does not specify prerequisites or when not to use it. Implied usage from purpose but lacks explicit 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. The description discloses that the tool discovers clusters and identifies outliers, and that it's density-based. It does not mention any side effects, performance, or prerequisites for using the 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?

Two sentences, no fluff. Every word adds value. Very concise 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?

No output schema is provided, and the description does not explain what the tool returns (e.g., cluster assignments, outlier flags). Given the complexity of clustering, more detail on output would be helpful. However, it covers the algorithmic behavior adequately.

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?

Input schema has 100% coverage with descriptions for eps and min_samples. The description adds algorithmic context but no additional meaning beyond schema. 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 clusters documents using DBSCAN, mentions density-based, automatic cluster discovery, and outlier identification. It distinguishes from sibling like cluster_documents_kmeans by noting it doesn't require specifying number of clusters.

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?

It provides a key differentiator (no need to specify number of clusters) which implies when to use over kmeans. However, it doesn't explicitly state when not to use or mention alternatives like HDBSCAN, though sibling list includes multiple clustering methods.

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 must carry the burden. It notes 'automatically discovers clusters and outliers' but omits details like computational cost, parameter sensitivity, or pre-processing requirements (e.g., need for document embeddings).

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 effective sentences: first defines the tool, second lists advantages. It is concise without being under-specified.

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 no annotations and no output schema, the description is adequate but lacks critical context such as required input format (e.g., embeddings), output structure, or prerequisites. It covers the core purpose but is not fully 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 the baseline is 3. The description does not add any additional meaning beyond what the schema already provides for min_cluster_size and min_samples.

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 'Cluster documents using HDBSCAN' and highlights key features (handles varying densities, discovers clusters and outliers), but does not explicitly differentiate from sibling tools like dbscan or kmeans.

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 HDBSCAN versus its alternatives (e.g., DBSCAN, k-means). The description mentions 'advanced clustering' and 'handles varying densities' but does not give concrete scenarios 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 must carry the full burden. It discloses the return of assignments and silhouette score, but it does not state whether the operation mutates data, requires pre-existing embeddings, or how the input documents are selected. This is insufficient for an agent assessing side effects.

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 sentences are concise and front-loaded with the core purpose. However, it could incorporate additional details without becoming verbose.

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 no output schema, the description should explain the return format and prerequisites. It fails to mention that embeddings must exist or what 'documents' refers to. The agent lacks key context to 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%, so the baseline is 3. The description does not add any additional meaning beyond the schema, which already describes the two optional parameters with defaults.

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 clusters documents using K-Means on embeddings, partitions into K clusters, and returns assignments and silhouette score. However, it does not differentiate from sibling clustering tools like DBSCAN or HDBSCAN, which are also present on the server.

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 K-Means versus other clustering algorithms, nor any prerequisites or exclusions. The description is purely functional without 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 bears full responsibility for behavioral disclosure. It states that the tool returns a comparison with a cosine similarity score, indicating a read-only operation. However, it does not explicitly confirm non-destructive behavior, mention authentication needs, or describe any side effects. The lack of explicit read-only assertion is a minor 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 extremely concise with three sentences. The first sentence states the core action, the second provides use cases, and the third mentions the output format. Every sentence adds value without redundancy. It is well-structured and front-loaded with the most critical 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?

Given the tool's complexity (multi-faceted comparison) and the absence of annotations and output schema, the description only partially covers what the agent needs. It explains the comparison types and output metric but does not detail the full return structure (e.g., format of diffs, entity lists). The description provides sufficient context for basic use but lacks completeness for advanced 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 input schema already provides descriptions for all 3 parameters (100% coverage). The tool description does not add substantial new meaning beyond summarizing the comparison types. For instance, the schema already explains the 'comparison_type' enum values. With full schema coverage, 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 explicitly states the tool compares two documents side-by-side with specific features (similarity scoring, metadata diff, content diff, entity comparison). It clearly identifies the action (compare documents) and the output (comprehensive comparison with cosine similarity). This distinguishes it from siblings like 'find_similar' which only finds similar documents without detailed comparison.

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 use cases: 'finding differences between document versions, comparing related documents, or analyzing document similarity.' However, it does not explicitly state when to avoid this tool or mention alternatives among siblings (e.g., 'find_similar' or 'get_summary'), leaving some ambiguity for the 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?

The description discloses that metrics are stored in the database if store_results is true, but does not explicitly state that this is a write operation or mention other behavioral traits like performance impact, rate limits, or whether the tool modifies existing data. With no annotations, more transparency would be beneficial.

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 long, each serving a purpose: stating what metrics are computed, describing the return value, and noting storage behavior. It is concise without unnecessary words.

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 lack of output schema, the description states 'Returns detailed metrics for all entities and graph-level statistics' which is adequate but vague. It does not specify the format or structure of the returned metrics. A more detailed explanation of the output would improve completeness.

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?

All four parameters are fully described in the input schema (100% coverage). The description does not add any additional meaning or context beyond what the schema already provides, so 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 computes comprehensive graph metrics including PageRank, betweenness centrality, degree centrality, and community detection. It specifies return of detailed metrics for all entities and graph-level statistics, and distinguishes from siblings like analyze_graph_pagerank or calculate_graph_centrality which likely compute single 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?

No guidance is provided on when to use this tool versus alternatives like analyze_graph_pagerank or calculate_graph_centrality. The description does not mention prerequisites, trade-offs, or scenarios where a simpler tool would be preferred.

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 behavior. It mentions that a backup is created and compressed to a zip file, but does not specify if it overwrites existing backups, required permissions, or any side effects. Basic behavior is clear but lacks depth.

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-loaded with purpose, and each sentence adds value. No wasted words; highly concise and structured effectively.

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 no output schema, the description covers purpose, usage advice, and basic behavior. It could include what happens if the backup file already exists, but overall it is sufficiently complete for a simple backup 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 has 100% coverage with descriptions for both parameters. The description adds minimal value beyond the schema, only tying 'zip file' to the compress parameter. Baseline 3 is appropriate as the schema already documents parameters well.

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 that the tool creates a backup of the knowledge base, including database and embeddings to a zip file. However, it does not differentiate from the sibling tool 'restore_backup' or other backup-related tools, leaving room for ambiguity.

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 usage context: 'Use this regularly for data safety and before making major changes.' This guides the agent on when to invoke the tool, but lacks guidance on when not to use or mention of 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 provided, so description carries full burden. States it analyzes patterns and returns severity scores based on learned baselines, but does not disclose if it modifies data, requires specific permissions, or has side effects.

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 sentences, front-loaded with action and result. No extraneous information; 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?

No output schema, but description explains return values (severity scores). Lacks mention of read-only nature or prerequisites, but overall adequate for a simple detection 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?

Input schema has 100% description coverage, providing clear parameter details. Description adds context about severity scores and analysis but does not significantly augment 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?

Clearly specifies detecting anomalies in URL monitoring history, analyzing patterns for unusual frequencies, performance degradation, or content changes. Distinguished from siblings as no other tool mentions anomaly detection.

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 explicit guidance on when to use versus alternatives. Implies use for URL monitoring analysis but lacks contextual cues 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, the description should disclose behavioral traits. It only defines communities but does not mention whether the tool is read-only, performance implications, or side effects.

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 sentences, no wasted words, 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.

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

Lacks output schema and annotations; description does not specify return value or behavior details. Minimal contextual completeness for a tool with these inputs.

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 already covers all three parameters with descriptions (100% coverage). The description adds no extra meaning beyond high-level 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 it detects communities/clusters in the knowledge graph and explains what communities are. However, it does not differentiate from sibling tools like 'detect_anomalies' or 'calculate_graph_centrality'.

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?

Implies usage for identifying topic clusters but provides no explicit guidance on when to use this tool vs. alternatives, nor any 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 does not disclose behavioral traits such as read-only nature, computational cost, or potential side effects. The description only states what it shows, not how it behaves.

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 sentences with no wasted words. The primary function is stated first, followed by a brief use case. 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 low complexity (one optional parameter, no output schema), the description is adequate but does not explain the return format or structure. For a stats tool, it could specify what the output looks like (e.g., JSON with counts).

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% with a well-described optional enum parameter. The description adds no additional meaning beyond the schema, simply restating that it shows breakdown by type. Baseline score 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 retrieves statistics about extracted entities, including breakdown by type, top entities, and documents with most entities. The verb 'Get' and resource 'statistics about extracted entities' are specific, and it distinguishes from sibling tools like 'get_top_entities' and 'kb_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 only says 'Useful for understanding the knowledge base content,' which is vague. No explicit guidance on when to use this tool versus alternatives like 'get_entity_analytics' or 'kb_stats', nor any mention of when not to use it.

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

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

No annotations are provided, placing the disclosure burden on the description. The description implies a read operation ('export metadata') but does not explicitly state safety, side effects, or permissions required.

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 concise sentences, front-loaded with the main action, no unnecessary 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?

No output schema is provided, and the description does not describe the output structure, return type (file, string, etc.), or any pagination/limitations, leaving the agent uncertain about what to expect.

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% description coverage, so the tool description adds minimal new parameter meaning. It mentions available formats (matching the schema enum) but does not clarify what 'metadata' includes.

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 exports metadata for multiple documents in three formats (JSON, CSV, Markdown), distinguishing it from sibling export tools that handle entities, relationships, or other resources.

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 gives use cases ('reports, backups, sharing document lists') but does not explicitly differentiate this tool from alternatives or provide when-not-to-use 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?

No annotations; description lacks details on output behavior (e.g., returns string vs saves file), potential data size limits, or side effects. Only states what it exports, not how.

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?

Three sentences: action/format, content, usage. No filler, 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?

Adequately covers purpose and parameters but omits important context like output structure, export limits, or behavior when output_path is omitted. Missing detail for a potentially large dataset export.

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 covers all 4 parameters with descriptions (format, entity_types, min_confidence, output_path). Description adds no extra parameter insight beyond listing exported fields.

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 exports entities to CSV or JSON, lists included fields (text, type, confidence, etc.), and distinguishes from sibling extract/export tools by specifying entity 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?

Provides use cases (data analysis, reporting, importing) but fails to explicitly differentiate from sibling export tools like export_relationships or export_results, or advise when not to use.

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 an export operation (likely read-only) but does not explicitly state that it is non-destructive, nor does it discuss side effects, limits (e.g., large data volume), or performance characteristics. The description provides basic behavior but lacks depth.

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 long, with no wasted words. The first sentence states the core action and output, the second provides included data fields, and the third suggests use cases. It is front-loaded and efficiently communicates 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 moderate complexity (4 parameters, no output schema, no annotations), the description covers the main purpose, output formats, filtering options, and file saving. It is largely complete for a straightforward export tool. However, it could mention potential size limitations or that it exports 'all' relationships, which might be implicit but important for large datasets. The absence of output schema is acceptable as the description implies the return format.

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 baseline is 3. The description does not add meaning beyond the existing schema descriptions; it merely restates that format can be 'csv' or 'json', min_strength ranges from 0.0 to 1.0, entity_types filters, and output_path is optional. No additional context is provided for nuanced use, so the score remains at the baseline.

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 specifies the action (export), resource (entity relationships), and output formats (CSV or JSON). It lists included data (entity pairs, types, strength scores, document counts) and provides a use case (network analysis, visualization, data export), effectively distinguishing it from sibling export tools like export_entities or export_documents_bulk.

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 states it is 'perfect for network analysis, visualization, or data export,' giving some usage context. However, it does not mention when not to use it, nor does it guide the agent to compare with alternatives like get_entity_relationships or export_entities. This lack of exclusions and alternative selection guidance leaves room for ambiguity.

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 carry the behavioral disclosure burden. It correctly implies a read-only export operation, but does not explicitly state that no data is modified, or detail any permissions or constraints, which is adequate but not exceptional.

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 long, front-loaded with the core purpose, and contains no redundant or irrelevant information. Every sentence contributes value.

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 a simple tool with 3 parameters and no output schema, the description covers the basics of purpose and use cases. However, it lacks information about the return format or potential errors, which would be beneficial given the absence of an 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?

Input schema has 100% coverage with clear parameter descriptions. The tool description adds no additional semantic value beyond what the schema provides, so a score of 3 is appropriate as the schema already explains the parameters sufficiently.

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 exports search results to markdown, json, or html formats. It specifies the verb 'export' and the resource 'search results', which is distinct from sibling export tools that handle documents or entities directly, though it does not explicitly differentiate.

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 use cases ('save for offline use, sharing, or creating custom reference guides') but does not compare to alternative export tools like export_documents_bulk or export_entities, which limits guidance on when to prefer this tool over 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, the description bears full burden for behavioral traits. It discloses the tool stores to database (a write operation), but lacks details on whether it is destructive, permissions needed, rate limits, or error handling. Adequate but 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 (24 words), front-loads the main action, and includes illustrative examples. Every phrase is informative with no 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?

Despite a clear purpose, the description omits critical context: no output schema, so the return type (e.g., job ID or status) is unspecified. No mention of side effects, prerequisites, or performance impact. Incomplete for a mutation tool with no annotations.

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% with both parameters well-described. The description does not add extra meaning beyond the schema (e.g., does not elaborate on doc_id or min_confidence). 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 the tool's purpose: extracting temporal events (with concrete examples like product releases, milestones) from a document. This distinguishes it from siblings like 'extract_entities' (which extracts named entities) and 'search_events_by_date' (which queries already extracted events).

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 versus alternatives, such as 'search_events_by_date' for querying events or other extraction tools. No explicit when-to-use, when-not-to-use, or references to siblings are included.

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 provided. Description hints at caching via 'force_regenerate' parameter and mentions AI usage, but does not disclose read/write behavior, side effects, or rate limits. Behavioral traits are partially covered.

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 sentences, front-loaded with main action, efficient and no unnecessary words.

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?

No output schema exists. Description mentions return fields (type, confidence, context) but omits structure details (e.g., nested objects, arrays). For a complex AI extraction tool, more output specification 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?

Schema coverage is 100%, so baseline is 3. Description adds general context about entity types and output structure but does not add meaningful parameter-specific semantics beyond what the schema already 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?

Description clearly states it extracts named entities from C64 documents, lists specific entity categories (hardware, memory addresses, etc.), and mentions output fields (type, confidence, context). It distinguishes from siblings like extract_entities_bulk and extract_entity_relationships.

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?

Notes 'Requires LLM configuration' but does not explicitly state when to use this tool vs. alternatives (e.g., bulk extraction, queueing). Lacks explicit 'when not to use' or comparison with 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?

With no annotations, the description carries the burden. It states batch processing and skipping behavior, which are useful, but does not disclose whether entities are persisted, any destructive actions, or rate limits. The description is adequate but leaves some behavioral aspects implicit.

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, front-loaded with purpose, and contains no redundant information. 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 the tool has 4 parameters (all described in schema) and no output schema, the description covers the overall behavior (batch processing, skipping logic) and return type (statistics). It is complete enough for an agent to understand the tool's function, though specific statistics format is not detailed.

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 description adds limited value beyond the schema. It reinforces the skip existing and force regenerate logic but does not provide additional parameter context beyond what is already 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 the verb 'extract' and resource 'entities from multiple documents', distinguishing it from single-document extraction tools like 'extract_entities' by emphasizing 'bulk' and 'batch'.

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 explains that it processes documents in batch and skips documents with existing entities unless force_regenerate is set, providing good context. However, it does not explicitly compare with siblings like 'extract_entities' or 'queue_entity_extraction' for when to choose this tool over 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, so the description must carry the full burden. It describes the operation (extraction) and return types, which is adequate. However, it does not mention any potential side effects, resource usage, or behavioral constraints beyond the basic functionality.

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

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

The description is concise: two sentences front-loaded with purpose, followed by an example and output summary. No extraneous words.

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 no output schema, the description adequately covers the return type (entity pairs with strength and context). Parameters are clear. The tool is not overly complex (2 simple params), so the description is sufficiently 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 coverage is 100%, so both parameters (doc_id, min_confidence) are already described in the schema. The description adds context about the output but does not add new meaning to the input 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 the action ('extract co-occurrence relationships'), the resource ('entities in a document'), and the output ('entity pairs with relationship strength and context snippets'). It distinguishes from sibling tools like 'extract_entities' or 'search_entity_pair' by focusing on co-occurrence relationships.

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 explains when to use this tool (to analyze how entities co-appear in a document). It does not explicitly state when not to use or provide alternatives, but the context of sibling tools and the clear purpose implies appropriate usage.

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 the full burden of behavioral disclosure. It only describes filtering capability without mentioning read-only nature, side effects, result format, or pagination. The description is too minimal for safe invocation.

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 brief (three sentences) and to the point, with no redundant information. It could be slightly more structured (e.g., listing parameters explicitly), but overall it is 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?

The description omits return value information (no output schema), does not mention max_results and tags parameters, and lacks details on behavior like default limit or sorting. Given the tool has 4 parameters and nested objects, this is inadequate.

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?

Despite 100% schema coverage (baseline 3), the description adds value by giving concrete examples of facet types and values (e.g., SID, LDA, $D000) that go beyond the schema's generic example. However, it does not cover other parameters like max_results or tags.

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 for searching with faceted filtering and provides concrete examples of filter types (hardware, assembly instructions, memory registers). However, it does not explicitly differentiate from sibling search tools like fuzzy_search or semantic_search, so clarity is strong but not fully distinguished.

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 narrowing down to specific technical domains, but lacks explicit guidance on when to use this tool versus alternatives. No exclusions or when-not-to-use scenarios are mentioned, leaving the agent to infer context from the examples.

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. It only states the core function without mentioning read-only nature, side effects, or any constraints. For a search tool, the lack of explicit read-only indication or response format 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?

Two sentences with no wasted words. The first sentence states the core function, the second adds examples and context. Information is front-loaded and scannable.

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 no output schema, the description should hint at return format or result details. It does not mention what the output looks like (e.g., list of documents, highlighted matches). While the tool is simple, missing output behavior reduces completeness.

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 baseline is 3. The description adds value by providing concrete examples for ref_value ('$D020', 'VIC+0', '156'), which clarifies the expected format beyond the schema's descriptions. This helps an AI agent correctly format inputs.

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: 'Find documents by cross-reference.' It provides specific examples (memory addresses, register offsets, page references), which distinguishes it from general search tools like search_docs or fuzzy_search, making the purpose concrete and 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 contextualizes usage with 'Great for tracking how specific registers or memory locations are documented,' indicating when this tool is appropriate. However, it does not explicitly compare to sibling tools or state when not to use it, but the focus on technical cross-references is clear.

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 states the tool finds the shortest path, indicating a read operation, but it does not disclose behavioral traits like what happens if no path exists, performance constraints, or the return format.

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, each adding value: purpose, explanation, and use case. It is front-loaded with the core action and has no redundant 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 explains purpose and typical use, but it lacks details on output format, error handling, or prerequisites (e.g., entity existence). Given the tool's complexity and lack of output schema, more context would be helpful.

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%, and the description mentions entity1 and entity2 in context but adds no additional meaning beyond the schema (e.g., source/target entity names). With high coverage, the baseline is 3, and the description does not elevate it further.

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 finds the shortest path between two entities in a knowledge graph, showing connections through intermediate relationships. This is specific and distinct from sibling tools like 'find_related_entities' or 'get_entity_relationships'.

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 says it's 'useful for understanding conceptual connections and knowledge pathways,' which implies when to use it, but it does not explicitly specify when not to use it or how it differs from alternatives such as 'find_related_entities', 'get_entity_relationships', or 'search_entity_pair'.

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 burden. It states 'Returns top related entities for quick exploration,' which indicates a read-like operation but does not disclose if any data is modified, required permissions, or how the results are derived. Adequate but not rich.

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 sentences, tightly written with no extraneous words. The most critical information (purpose and comparison) is front-loaded, achieving high efficiency.

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 a simple discovery tool with two parameters and no output schema, the description sufficiently covers the what, how (simplified), and return (top related entities). It lacks details on edge cases or response format, but these are minor given the tool's simplicity.

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% with clear descriptions for entity_text and max_results. The description adds context about being a simplified version focused on exploration, but does not significantly extend beyond the schema. 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 the verb 'Discover' and resource 'entities related to a given entity', and explicitly distinguishes itself as a simplified version of get_entity_relationships, making its purpose very clear.

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 notes it is a simplified version of get_entity_relationships, implying a quick exploration use case, but does not explicitly state when to use it vs alternatives like find_similar or search_entities, nor does it provide when-not-to-use conditions.

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?

Discloses algorithmic behavior: uses semantic embeddings with TF-IDF fallback. With no annotations, this provides meaningful context. Does not cover potential side effects or limitations, but sufficient for a read-only search 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?

Two sentences, no redundant words. Purpose is front-loaded, algorithm explanation is concise, and use case is stated efficiently.

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?

No output schema; description does not mention return format or data structure. However, the tool is straightforward and the algorithm details provide adequate context for an agent to understand its behavior.

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?

All parameters are fully described in the input schema (100% coverage). The description adds no extra meaning beyond the schema, so 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?

Clearly states 'Find documents similar to a given document' with verb and resource. Contrasts with siblings like semantic_search by mentioning the algorithmic approach (embeddings/TF-IDF).

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?

Implies usage for 'discovering related content' but lacks explicit guidance on when to use this tool over alternatives like semantic_search or fuzzy_search. No exclusions or when-not-to-use 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?

With no annotations, the description must disclose behavior fully. It notes that exact matches come before fuzzy matches, which is helpful, but omits details on performance, authorization needs, or behavior on no matches. Adequate but not thorough.

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, using two sentences to convey purpose, examples, and ordering of results. No wasted words, and key information is front-loaded.

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?

Missing output schema, so description should compensate by specifying return format. It mentions 'returns exact matches first, then fuzzy matches' but does not describe what those matches look like (e.g., document IDs, scores, content). Leaves 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 coverage is 100%, so baseline is 3. The description adds examples for the query and explains similarity_threshold, but does not enhance understanding of max_results or tags beyond the schema. Meets minimal expectations.

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 fuzzy string matching with typo tolerance and gives concrete examples, making the purpose clear. However, it does not explicitly differentiate from sibling search tools like semantic_search or hybrid_search, which reduces clarity in distinguishing when to use this tool.

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 typos are expected but provides no explicit guidance on when to use versus alternatives, nor does it mention when not to use. No prerequisite or context for selection is given.

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 provided. The description does not disclose side effects, permissions, or whether it modifies data. It only mentions the visualization output, lacking behavioral context needed for safe invocation.

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 sentences, clear and concise. No redundant information; front-loaded with purpose.

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 main output (heatmap image) but does not specify return value (e.g., success message or path). Lacks detail on what happens after generation, but adequate for a simple visualization 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?

All parameters are described in the schema (100% coverage). The description adds no additional meaning beyond what the schema provides, so 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 it generates a heatmap of document-topic probabilities, differing from sibling tools like generate_topic_wordcloud which produces a word cloud.

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?

Implied that it's for visualizing topic distributions across documents, but no explicit guidance on when to use versus alternatives like other visualization or clustering 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 provided, and description does not disclose behavioral traits such as file handling (overwrite? permissions?) or performance implications. Only states it creates an image file.

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 sentences, direct and efficient. Front-loads purpose in first sentence, adds detail in second.

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?

Missing details about return value (e.g., file path or status), and no explanation of output format. Sibling tools exist but no comparison.

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?

Parameter descriptions in schema cover all 5 parameters with defaults. Description does not add additional semantics beyond 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?

Description clearly states it generates a word cloud visualization for a topic, specifying it creates an image file with word size proportional to weight. Differentiates from sibling tool generate_topic_heatmap which generates a heatmap.

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 explicit guidance on when to use this tool over alternatives like generate_topic_heatmap. Lacks usage context 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?

Describes a read operation ('Get the full content'), implying no side effects, but does not explicitly state idempotency or safety traits. Lacks annotations, so description carries full burden.

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 sentences, front-loaded with purpose, no wasted words. Highly concise 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?

For a simple retrieval tool with two parameters and no output schema, the description covers purpose and usage hint. Could mention return format or error handling, but 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?

Schema coverage is 100% with clear descriptions for both parameters. The description does not add extra meaning beyond the schema, meeting baseline.

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 action 'Get the full content' and the resource 'specific document chunk'. Distinguishes from siblings by referencing 'search_docs' for context.

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?

Explicitly says 'Use after search_docs', providing clear context. Does not include when not to use or alternative tools, but the connection to a sibling is strong.

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 provided, so description carries full burden. Mentions output content (documents and distances) but fails to disclose side effects, idempotency, error handling, or performance behavior. For a read operation, basic traits are missing.

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 sentences, no wasted words, front-loaded with core action. Highly concise and 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 no output schema, description adequately explains return content (documents and distances). Could mention default for max_results or algorithm optionality, but schema covers that. Reasonably complete for a simple retrieval 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 coverage is 100% with each parameter described. Description adds context about output (distances from centroid) but does not clarify parameter usage beyond schema. 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?

Description clearly states 'Get all documents in a specific cluster' with specific verb and resource. Adds detail about including distances from centroid. Distinguishes from sibling cluster creation tools (e.g., cluster_documents_kmeans) and visualization 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?

Implies usage after clustering to examine results, but no explicit when-to-use or when-not-to-use compared to alternatives. Lacks guidance on prerequisites or when this tool is appropriate.

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 carry the full burden. It only states a read operation but omits behavior on missing IDs, error handling, rate limits, or permission requirements. This leaves the agent uninformed about side effects or constraints.

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, front-loaded with the verb and resource. Every word earns its place. No unnecessary 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?

For a simple retrieval with one parameter, the description is minimally adequate. However, it does not describe the return format (e.g., plain text, structured JSON) or differentiate from similar tools, which is important given the large number of siblings.

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 the baseline is 3. The description adds no additional meaning beyond 'full content' and the schema's 'Document ID'. No extra context about doc_id format or usage.

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 action (get), resource (full content of a document), and method (by ID). It is specific enough to distinguish from siblings like list_docs or get_summary, but could be more precise about what 'full content' entails.

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 such as get_summary, list_docs, or search_docs. The agent receives no context for decision-making.

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?

Description mentions it returns probabilities and top words, adding behavioral context beyond the schema. However, with no annotations, more details (e.g., that it is read-only) would improve 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?

Two efficient sentences, front-loaded with key information, no unnecessary 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?

Adequately explains what is returned, but lacks mention of dependencies (e.g., requirement for a trained topic model). For a tool with no output schema, more detail on the response structure would help.

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% and parameter descriptions are clear. The description adds context about what the tool returns but does not add meaning beyond the schema for the 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?

Clearly states the verb 'Get', resource 'topics for a specific document', and what is returned (probabilities and top words). Distinguishes from siblings like list_docs or search_docs.

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 like cluster_documents_* or generate_topic_heatmap. Does not mention prerequisites such as needing a trained topic model.

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 the full burden. It describes the behavioral output (distribution, top entities, relationships, timeline trends), but omits details like data freshness, pagination, or performance implications. Adequate but 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?

Two sentences, no fluff. First sentence establishes purpose, second lists deliverables. Every phrase adds value.

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 lists output categories but lacks details on structure, format, or how results relate to each other. Given no output schema, more context on the returned data would improve completeness.

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%; the description adds no information about the time_range_days parameter beyond what the schema already provides. Baseline score of 3 is appropriate as schema does the work.

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 entity analytics for dashboard visualization, listing specific outputs like entity distribution, top entities, and relationship statistics. It distinguishes its comprehensive nature from siblings like entity_stats or get_timeline, but does not explicitly contrast 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?

No guidance on when to use this tool versus alternatives such as entity_stats or search_analytics. The description does not mention prerequisites, when not to use it, or provide context for selecting among many analytics 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, so the description carries the full burden. It mentions communities are detected through graph analysis but does not disclose side effects, read-only nature, or performance implications. The description does not contradict any annotation.

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, front-loaded with the main purpose. It is efficient and contains no extraneous information, though it could be slightly more 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 no output schema, the description mentions what is returned (community ID, member count, list of members). It covers the basic usage scenario adequately for a simple retrieval 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 100%, so the schema already documents both parameters. The description adds no additional meaning beyond the schema; it only states what the tool returns, not how parameters affect behavior.

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 action ('Get all entities in the same community') and specifies the resource ('the specified entity'). It also explains what communities are and distinguishes from sibling tools like detect_graph_communities.

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 retrieving community members but does not provide explicit guidance on when to use this tool versus alternatives like find_related_entities or entity_stats. No when-not-to-use or prerequisite information is given.

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 explains it retrieves stored metrics (read-only) and lists returned fields, but it omits behavioral details such as what happens if the entity does not exist, potential errors, or if any computation is triggered. The description is adequate but 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, concise and to the point. It front-loads the primary action and lists key return values. No wasted words, though it could be slightly more structured with bullet points or clearer separation.

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 no output schema, the description covers the return values (PageRank, betweenness, etc.). It does not mention error handling or missing entity behavior, but for a simple retrieval tool, the core information is present and sufficient for most use cases.

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, so the schema already documents parameters. The description mentions the metrics list but does not add significant semantic detail beyond what the enum and description in the schema provide. 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 retrieves stored graph metrics for a specific entity and lists the metrics returned. It distinguishes from sibling tools that compute or analyze metrics (e.g., calculate_graph_centrality, detect_graph_communities) by specifying that this tool accesses precomputed values, though it does not explicitly name alternatives.

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 suggests using the tool to understand entity importance and role, but it does not provide explicit guidance on when to use this tool over siblings like analyze_graph_pagerank or get_entity_analytics. No when-not or alternative listing is given.

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 burden. It explains the tool returns co-occurring entities sorted by strength, which is helpful but lacks details on whether the operation is read-only or has side effects.

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 with three effective sentences: first states the action, second adds sorting detail, third gives a use case. No unnecessary words.

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 no output schema, the description should clarify what the response contains (e.g., entity names, strength scores, sorting direction). It omits these details, leaving the agent to infer the output structure.

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% description coverage, so the schema already explains each parameter adequately. The description adds minimal semantic value beyond mentioning 'frequently co-occur', which is not parameter-specific.

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 retrieves entities related to a specific entity, sorted by relationship strength. It provides examples of entity values (e.g., 'VIC-II'). However, it does not differentiate from the sibling tool 'find_related_entities', which may 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 implies the tool is suitable for discovering related concepts, hardware, and techniques, but does not provide explicit when-to-use or when-not-to-use guidance, nor does it mention 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, so the description must carry the burden. It discloses that the tool shows various job states but lacks details on pagination (though limit parameter exists), rate limits, or authentication requirements. Adequate but 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?

Two concise sentences with the action verb first. No redundancy, each sentence adds value: first states the operation, second explains the output and use case.

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?

Since there is no output schema, the description explains the categories of jobs returned, which helps the agent understand what to expect. It could mention the structure of each job object but is adequate for a list monitoring 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 100% as both parameters have descriptions. The description does not add additional meaning beyond what the schema already provides, so 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 retrieves all entity extraction jobs with optional status filtering, and lists the types of jobs shown (queued, running, completed, failed). It differentiates from sibling tools like get_extraction_status by focusing on the full job list.

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 extraction progress but does not explicitly state when not to use it or provide alternatives. Sibling tools like get_extraction_status or queue_entity_extraction are not mentioned for comparison.

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 provided. The description compensates by disclosing the status values (queued/running/completed/failed) and that it includes timestamps and error messages. It does not indicate any side effects or destructive 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?

Two sentences, each serving a distinct purpose: definition of output and usage advice. No redundant words. Highly 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?

Given the simplicity of the tool (one param, no output schema), the description fully covers what it does, what it returns, and when to use it. No gaps.

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% for the single parameter 'doc_id', with a clear description. The tool description does not add further semantic detail about the parameter beyond what the schema provides. 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 verb 'Get' and the resource 'entity extraction status for a document.' It also lists what it shows: existence, job status, timestamps, errors. It distinguishes from siblings by positioning it as a prerequisite check before querying entities.

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?

Explicitly advises when to use: 'Use this to check if extraction is complete before querying entities.' This provides clear context. No explicit 'when not to use' but the positive use case is well-defined.

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 only states the output, but fails to disclose behavioral traits such as whether it is read-only, error handling for empty graphs, or any side effects. It minimally implies a safe read 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 concise and front-loaded, using two sentences to convey the purpose and output without any 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?

Given the simplicity of the tool (two optional params), the description covers the key output aspects. However, it lacks details on return format or edge cases, but no output schema exists to compensate.

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% and the schema already describes both parameters adequately. The description does not add any additional meaning to the 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 the tool's purpose: retrieving a statistical overview of the knowledge graph, listing specific metrics (node count, edge count, etc.) which distinguishes it from sibling tools like compute_graph_metrics or entity_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 provides no guidance on when to use this tool versus alternatives (e.g., compute_graph_metrics), nor does it mention any 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, the description carries full burden. It discloses that the tool returns events and the scope (target year plus surrounding years). This is sufficient for a read-like operation, though it doesn't explicitly state it's 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?

Two sentences, front-loaded with purpose, no wasted words. Efficiently communicates the core functionality.

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?

While the description explains the purpose and scope, it does not specify the return format or structure of events. Without an output schema, the description could be more detailed to fully compensate, though it is adequate for a simple 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 coverage is 100% with parameter descriptions. The description adds context that the function returns events with surrounding years, but does not provide additional meaning beyond what's already 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?

Description clearly states it retrieves historical context for a year, returning events from that year plus surrounding years. It effectively communicates the specific verb and resource, but does not explicitly distinguish from siblings like get_timeline.

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?

Implies usage for obtaining historical context with temporal scope, but provides no explicit guidance on when to use vs alternatives (e.g., get_timeline, search_events_by_date) 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, the description carries full behavioral burden. It indicates a non-destructive, read-only operation by mentioning 'cached' and 'without regenerating,' but does not describe behavior if the cache is missing or other side effects.

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?

A single, well-front-loaded sentence conveys essential information without any waste. Every word serves a purpose.

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 simplicity (cached retrieval), the description is largely complete. However, absence of output schema means agents lack clarity on return format, and no guidance on cache miss behavior. Still, it adequately covers the core function.

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 parameters are documented in schema. The description adds no extra meaning beyond the schema for doc_id and summary_type. Baseline score of 3 applies.

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 action ('Retrieve') and the resource ('cached summary') with a specific qualifier ('without regenerating it'), distinguishing it from sibling tools like summarize_document that generate summaries.

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 retrieving already cached summaries, but does not explicitly compare to alternatives such as summarize_document or get_chunk, nor does it state when not to use this tool.

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 provided, so description carries full burden. It specifies output includes tag usage counts and sample documents, but does not explicitly state read-only nature, though inferred. Adequate but could be more explicit.

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 sentences, front-loaded with action and categories, no extraneous words. Information-dense and 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?

Given no parameters and no output schema, description fully covers what the tool does and what it returns. Complete for a simple browsing 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?

Input schema has zero parameters. Rule: 0 params = baseline 4. Description adds meaning by explaining the organization by category and output details, compensating for no 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?

Description clearly states it browses all tags organized by category, listing specific categories (hardware, programming, etc.) and mentions output (usage count, sample documents). Distinguishes from sibling tools like suggest_tags.

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?

States tool is 'useful for discovering and organizing content,' implying browsing context. Does not explicitly exclude alternatives but provides clear use case.

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 provided. The description implies a read-only operation but does not explicitly state that it is safe or describe any side effects. Basic behavioral traits are missing, but the description does not contradict annotations (none exist).

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 sentences front-load the purpose and output format. No unnecessary information. Every word 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 five optional parameters, no output schema, and no annotations, the description adequately conveys the tool's function and output. Minor omission: it does not specify sort direction (though 'chronological' implies ascending).

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?

Input schema has 100% description coverage, so the schema already documents each parameter's meaning. The tool description adds no additional semantics beyond restating that filtering is optional, which is already clear from 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?

Description clearly states it retrieves a chronological timeline with optional filtering and returns sorted entries with event details. However, it does not differentiate from sibling tool 'search_events_by_date', which may also return timeline-like 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?

No guidance on when to use this tool versus alternatives such as 'search_events_by_date' or 'get_historical_context'. No exclusions or prerequisites are mentioned.

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

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

No annotations are provided, so the description must fully inform the agent. It discloses that the tool returns a ranked list with scores, types, and other metrics, and lists available metrics. However, it does not specify if the results are computed on the fly or retrieved from stored data, whether the graph must be pre-built, or if there are any side effects (though it is likely read-only). The description is adequate but lacks depth on behavioral traits beyond the obvious.

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 long, front-loaded with the core purpose in the first sentence, followed by return details and a use case. No redundant or vague language. Every word contributes meaning.

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 three parameters, no output schema, and no annotations, the description covers the function, return format, and typical use case. It does not mention prerequisites (e.g., graph must exist or metrics already computed) or clarify if the ranking uses precomputed values. However, for a straightforward retrieval tool, the context provided is mostly 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 input schema has 100% description coverage, providing clear definitions for metric, limit, and entity_types. The description adds value by explaining the output nature (ranked list of top entities) and the significance of the results ('most important, central, well-connected'), which goes beyond the schema alone. It also hints at 'other metrics' returned, 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 specifies the action (get), the resource (top-ranked entities), and the context (by a specific metric like PageRank, betweenness, or degree). It also describes the return format (ranked list with scores, types, and other metrics). However, it does not explicitly differentiate from sibling tools such as 'analyze_graph_pagerank' or 'get_entity_metrics', which might compute or return similar information.

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 states it is 'useful for discovering the most important, central, or well-connected entities', implying a use case. However, it provides no guidance on when to use this tool versus alternatives (e.g., when you need a simple ranked list vs. detailed analytics). With many sibling tools that may overlap, explicit usage guidelines are missing.

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 provided, so description carries full burden. It states the tool 'Returns status, metrics, feature availability, and any issues detected' – clear on what the tool does and what output to expect. No side effects mentioned, but health check is inherently 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?

Two concise sentences, zero wasted words. Front-loaded with the core purpose. Every word adds value.

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 a no-parameter, no-output-schema tool, the description fully explains what the tool does and what it returns. Complete and sufficient for an AI 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?

The tool has 0 parameters (schema coverage 100%). Description already explains what the tool returns. Baseline 4 applies as no parameter details needed.

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 'Perform health check on the knowledge base system' – a specific verb+resource. It distinguishes from sibling tools by focusing on system status rather than on documents, entities, or graph 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 for checking system health, but does not explicitly state when to use or when not to use it compared to other analysis tools like kb_stats (which may be more detailed). Usage is implied.

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