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

No annotations are provided, so the description bears the full burden of behavioral disclosure. It mentions that the tool 'resolves prefixed URIs to full forms' and returns a dictionary, but it does not explicitly state whether the operation is non-destructive or describe any side effects. The debugging context is helpful but the safety profile is not fully disclosed.

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

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

The description is concise, with the core purpose in the first sentence and only two longer sentences plus Args/Returns. It is front-loaded with the main action and contains no filler or redundant content, earning every word.

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 (one flexible parameter) and the presence of an output schema (as indicated in the context signals), the description provides the key elements: purpose, parameter semantics, and return type. It is sufficient for an agent to decide when to invoke the tool, though it lacks explicit information about prerequisites or edge cases, which are minor for a debugging utility.

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

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

The schema has 0% description coverage for the entity_data parameter, which is simply defined as an object with additionalProperties true. The description fills this gap by defining entity_data as 'Any DanNet JSON-LD entity data,' clarifying the expected input type, but it remains broad and does not detail any required fields or structure beyond being JSON-LD.

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 main action ('Analyze namespace usage') and its specific deliverable ('resolves prefixed URIs to full forms'). It uses a specific verb and resource, and the focus on namespace analysis distinguishes it from sibling tools like get_entity_info or sparql_query, which are retrieval-oriented.

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 identifies the tool as a 'debugging tool' and says it helps understand namespace usage, which gives an implied usage context. However, it does not explicitly state when this tool is preferred over alternatives or when not to use it, lacking explicit exclusions or alternative recommendations.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It states that returns are lemma forms (dictionary forms), comma-separated, alphabetically ordered, and that at least 3 characters are required. This gives the agent a solid understanding of behavior. It does not cover edge cases like empty results or error handling, but the disclosed traits are sufficient for typical use.

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

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

The description is well-structured with clear sections: purpose, use cases, args, returns, note, and an example. It is concise yet informative, with no superfluous words. Every sentence contributes value, making it easy for an agent to parse quickly.

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

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

For a simple two-parameter tool, the description is complete. It covers purpose, parameters, return format, ordering, minimum input length, and includes a practical example. The tool has an output schema but the description still explains return semantics, which is helpful. No significant gaps remain.

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

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

Schema description coverage is 0%, so the description must compensate. It explains both parameters: prefix ('minimum 3 characters required') and max_results ('default: 10'). This adds meaning beyond the schema, which only defines types and a default. The example further clarifies expected parameter usage and output format.

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 opens with a specific verb and resource: 'Get autocomplete suggestions for Danish word prefixes.' This clearly differentiates the tool from sibling tools like fetch_ddo_definition or get_word_info, which serve different lookup purposes. The additional note about discovering vocabulary and spelling reinforces the purpose without 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?

The description explicitly states when the tool is useful: 'discovering Danish vocabulary or finding the correct spelling of words.' This provides clear context for use. However, it does not explicitly name alternatives or list conditions when not to use the tool, so it stops short of a 5. The sibling tools are not referenced in the description, which would have strengthened the 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?

With no annotations, the description carries the full burden. It discloses that data is normalized and structures are handled consistently, which adds behavioral context. However, it does not mention error handling, validation, or what 'normalized' means in concrete terms, leaving notable gaps.

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

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

The description is well-structured with Args/Returns sections and is reasonably concise. There is minor redundancy between the opening sentence and the 'unified way' sentence, but overall it is efficient and 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?

For a simple one-parameter tool with an output schema, the description is moderately complete: it gives purpose, input, and return type. However, it lacks guidance on when to use this versus the specific getter siblings and provides no detail about normalization behavior or edge cases, leaving some ambiguity.

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

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

Schema description coverage is 0%, so the description must compensate. It describes the only parameter as 'Any DanNet entity JSON-LD data,' adding basic meaning beyond the raw schema. Yet it lacks structural details or examples, especially since the schema allows arbitrary additional properties.

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 normalize' and the resource 'semantic data from any DanNet JSON-LD entity.' It explicitly mentions handling synsets, words, or senses, which distinguishes it from sibling tools that target specific entity types (e.g., get_synset_info, get_word_info).

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 cases by calling it a 'unified way' and mentioning handling different JSON-LD structures consistently, but it does not explicitly state when to prefer this over dedicated sibling getters or provide exclusions. Guidance is implied rather than direct.

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 and does well by disclosing the implementation (httpx, regex parsing), the specific CSS classes used, the caveat that source URLs may not work, and the complete return structure. This is substantial transparency, though it doesn't discuss rate limits or error handling in 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 well-structured with sections (WORKFLOW, IMPORTANT NOTES, Args, Returns, Example) and front-loads the purpose. It is somewhat lengthy but each section adds value, though the workflow detail could be condensed for agent consumption.

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 (web scraping, error potential), the description is exceptionally complete: it explains the problem, the workflow, limitations, return values, and provides an example. The presence of an output schema and detailed return description ensures the agent knows 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 no description for synset_id (0% coverage), but the tool description adds meaningful semantics with examples: 'Synset identifier (e.g., "synset-1876" or just "1876")'. This fully compensates for the schema gap.

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: 'Fetch the full, untruncated definition from DDO (Den Danske Ordbog) for a synset.' It uses a specific verb ('Fetch') and resource ('DDO definition'), and explicitly contrasts with DanNet's capped definitions, distinguishing it from sibling tools like get_synset_info.

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

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

The description provides clear context for when to use the tool: when synset definitions may be truncated. It also outlines the workflow and possible failures (DDO/DanNet divergence). However, it does not explicitly name alternative tools or 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?

With no annotations, the description carries the full burden for behavioral disclosure. It explicitly states that the cache is 'session-scoped' and gives an example that implies read-only observation (checking growth of cache_size). It does not explicitly state 'no side effects,' but the wording 'Return statistics' and the verification pattern make the non-mutating nature clear, adding useful context beyond the name.

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

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

The description is well-structured: a one-sentence purpose, a usage hint, and a clean Returns block. It is slightly longer than the minimal needed (the Returns block duplicates what an output schema likely covers), but the structure keeps it scannable and the extra detail serves the user. For that, it earns a 4.

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 simple (zero parameters, no annotations, output schema present). The description provides the session-scoping context, a measurable verification pattern with steps, and the return structure. It is complete for the tool's complexity and leaves no critical 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?

The tool has zero parameters, so the schema provides no semantic burden. The rubric gives a baseline of 4 for zero-parameter tools. The description adds no parameter-specific information because none exists, but it anchors the tool's behavior with a usage example, which is sufficient.

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 opens with a specific verb+resource: 'Return statistics about the session-scoped resource cache.' This clearly distinguishes it from the sibling retrieval and query tools by focusing on cache instrumentation. The follow-up example ('call get_synset_info twice...') reinforces the specific diagnostic purpose.

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 a concrete use case: verifying caching works by comparing cache_size across repeated calls. While it does not name an alternative tool or explicitly say when not to use it, it gives clear context for when this tool is appropriate, which matches a 4 ('clear context, no 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 discloses the return structure (server_url, server_type, status), the possible server_type values, and gives an example output. It does not discuss error cases or side effects, but as a read-only getter this is sufficient and no mutation is implied.

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

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

The description is front-loaded with the purpose, followed by a compact return contract and a concrete example. Every line adds value; there is no repetition of schema data or irrelevant detail.

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

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

For a parameterless getter, the description is complete: it identifies the resource, specifies every return key with valid values, and provides a realistic example. No output schema is given, but the inline return description fully covers what the agent needs to know.

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 zero parameters and the schema is empty (100% coverage). The description correctly implies there are no inputs to configure, and the example shows a no-argument call. Per rubric, zero parameters earns a baseline of 4.

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

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

The description explicitly states 'Get information about the currently active DanNet server' with a specific verb and resource. It distinguishes itself from sibling tools like switch_dannet_server and get_cache_stats by focusing on the current server state and enumerating return fields.

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 intended use is clear: call this tool when you need the current DanNet server URL, type, or connection status. It does not explicitly name alternatives or exclusions, but the getter semantics are unambiguous and no competing tool serves the same purpose.

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 takes on full responsibility for behavioral disclosure. It thoroughly explains the return format (JSON-LD with @context, @id, @type, RDF properties), namespace-based endpoint behavior, and navigation tips, adding substantial value beyond the plain schema.

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

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

The description is well-structured with clear headers, Args, Returns, and Examples, making it easy to scan. However, it includes some general RDF data-model commentary that could be shortened without losing critical guidance, making it slightly 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 the tool's broad scope (any entity, multiple namespaces) and the existence of specialized sibling tools, the description is complete. It covers all parameters, return structure, entity types, namespace handling, and practical examples, leaving no major gaps for agent invocation.

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 zero descriptions, so the description fully compensates by detailing both parameters: identifier examples (e.g., 'synset-3047') and namespace values with their endpoint mappings and common external namespaces. This provides complete semantic clarity.

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 its function: 'Get comprehensive RDF data for any entity in the DanNet database.' This distinguishes it from sibling tools that target specific entity types (word, sense, synset) by covering both DanNet entities and external vocabulary entities, demonstrating a clear scope.

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 extensive context on when to use the tool, including the kinds of entities supported and example use cases. However, it does not explicitly mention alternatives or contrast itself with specialized sibling tools like get_word_info, so it lacks explicit exclusionary 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?

With no annotations, the description carries the full burden and does so well: it discloses the return format (dict with RDF properties and resource_id), caveats about DDO URLs potentially not resolving, and how to interpret the data. It does not mention error behavior, but it is thorough about output structure and limitations.

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 exceptionally long, with extensive domain information about DanNet and DDO before reaching the actual usage section (Args/Returns). While structured with headers, it is not concise; key operational details appear late, making it less efficient for an agent to parse quickly.

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

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

The description thoroughly explains the RDF data model, relationships, source traceability, navigation tips, and includes a full example. Given the complexity of the domain and the lack of annotations, this provides a comprehensive picture of what the tool returns and how to use it. It lacks explicit error handling but is 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?

The input schema only defines sense_id as a string. The description adds meaning by providing examples ('sense-21033604' or just '21033604'), clarifying that the prefix is optional, and connecting the ID to sense labels like 'hund_1§1'. This goes beyond the schema and effectively compensates for the 0% schema description 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 explicitly states 'Get comprehensive RDF data for a DanNet sense' with a specific resource type (lexical sense). It also explains the sense-to-word/synset relationships, distinguishing it from sibling tools like get_word_info or get_synset_info.

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 extensive context about what can be done with the results (e.g., navigating to parent word or synset) and includes navigation tips, but it does not explicitly compare against alternatives or state when to prefer this tool over similar tools. The context implies usage but lacks exclusions or direct alternative references.

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 fully carries the burden. It discloses important behaviors: skos:definition may be truncated, dns:ontologicalType is an @set array, dns:sentiment has specific structure, and return format is JSON-LD with namespace prefixes. It also notes that parse_resource_id() can be used on URI references. This is comprehensive behavioral context.

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 quite long, but it is excellently structured with clear sections (DATA MODEL, KEY RELATIONSHIPS, DDO CONNECTION, NAVIGATION TIPS, Returns, Example). Each section adds value for a complex tool. However, it could be slightly trimmed; the relationship list is detailed enough to be a separate reference document, making it less concise than an ideal tool description.

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

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

Given the complexity of the tool (RDF data model, multiple relationship types, cross-linguistic links), the description is remarkably complete. It covers the data model, parameter format, return structure, example usage, and pointers to related tools. No gaps are apparent.

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 only shows 'synset_id' as a string. The description adds crucial semantics: examples ('synset-1876' or '1876') and explains it's a synset identifier. Since schema coverage is 0%, this compensation is essential and well-executed.

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 opens with a clear, specific verb+resource statement: 'Get comprehensive RDF data for a DanNet synset (lexical concept).' This distinguishes it from sibling tools like get_sense_info, get_word_info, and get_entity_info by focusing on synset-level RDF data. The purpose is 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 explicitly guides when to use this tool versus alternatives. It advises to use fetch_ddo_definition() and get_sense_info() for fuller definitions, and provides navigation tips for following hypernym chains and checking inherited properties. This clearly differentiates usage from related tools.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It thoroughly explains the output structure (all RDF properties, resource_id), provides navigation tips, and gives an example, making the tool's behavior clear. However, it does not discuss error handling, invalid word IDs, or any limitations, so it is not fully exhaustive.

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

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

The description is front-loaded with the primary purpose and then uses clear sections to detail the data model, relationships, navigation tips, arguments, and returns. While somewhat lengthy, the structure and educational content serve a purpose for handling complex RDF data, and every section adds value for correct usage.

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 tool returning complex RDF data with a single parameter and no annotations, the description is exceptionally complete. It covers the data model, key relationships, how to interpret the output, and even provides an example with expected keys. The presence of a return type and example further enhances completeness, making it sufficient for an agent 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?

The input schema only defines word_id as a string with no additional description. The description compensates fully by explaining the format with examples ('word-11021628' or just '11021628'), and the example usage demonstrates the accepted value. This adds significant meaning beyond the schema, making the parameter unambiguous.

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 opens with a specific verb+resource: 'Get comprehensive RDF data for a DanNet word (lexical entry).' It clearly differentiates from sibling tools like get_word_overview and get_sense_info by emphasizing comprehensive RDF data and detailing the data model, making the tool's unique purpose evident.

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 scenarios through phrases like 'comprehensive RDF data' and navigation tips, but it does not explicitly state when to use this tool over alternatives such as get_word_overview or get_word_synsets. There is no mention of exclusions or when-not-to-use, leaving the agent to infer the appropriate 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 carries the full burden. It discloses key behavioral traits: the tool returns only synsets where the word is a primary lexical member, excludes multi-word expressions, may truncate definitions, may return null for lexfile, and lists the exact structure of each returned dict. This is comprehensive for a read-only lookup.

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 organized with a clear summary first, then behavioral details, then return structure, then a concrete example. Every sentence contributes meaning; the example output and follow-up hint to get_synset_info() are valuable. It is long but perfectly scoped for the tool's complexity.

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 is contextually complete: it explains the tool's purpose, relationship to siblings, exact return format, example values, and how to chain into full synset data. The output schema (if present) is not shown, but the detailed 'Returns' section makes the tool usable without additional documentation.

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

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

The schema has one parameter (word) with no enum or richer description, and the structured schema coverage is 0%. The description adds 'The Danish word to look up' and a concrete usage example ('hund'), which clarifies the parameter's meaning. However, it does not specify behavior for empty input, case sensitivity, or words not found in the lexicon, leaving a minor gap.

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 opens with a specific action: 'Get a complete overview of all senses for a Danish word in a single call.' It clearly names the resource (Danish word senses) and distinguishes itself from the sibling tools get_word_synsets, get_synset_info, and get_word_synonyms by explicitly explaining it replaces the multi-call pattern.

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 explicitly describes when to use this tool: 'Replaces the common pattern of calling get_word_synsets → get_synset_info per result → get_word_synonyms.' It also explains the exclusion of multi-word expressions and directs users to get_synset_info() for full JSON-LD data, effectively differentiating from alternatives.

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

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

With no annotations, the description carries the full burden. It discloses that results are aggregated across word senses, returns a comma-separated string, and provides a concrete example. It also warns about polysemy and advises checking synset definitions, which adds useful behavioral context beyond the basic function.

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

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

The description is well-structured with an initial summary, bulleted synonym types, and labeled Args/Returns sections. It earns its length by providing example and usage nuance, though a few sentences (e.g., the third paragraph) slightly restate the purpose. Minor redundancy but not excessive.

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 one-parameter tool, the description is thorough: it explains the mechanism, limitations, return format, and includes an example and a polysemy note. It mentions a return type (comma-separated string) and offers practical guidance, making it complete despite the absence of explicit 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 0%, but the description explicitly documents the 'word' parameter as 'The Danish word to find synonyms for' and gives an example ('hund'). This fully compensates for the missing schema description, making the parameter's meaning unambiguous.

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 begins with 'Find synonyms for a Danish word through shared synsets,' which clearly states the specific action, resource, and mechanism. It further distinguishes itself from sibling tools like get_word_synsets by explaining that it returns the synonym words themselves, not the synset structures.

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 clearly implies usage for finding lexical alternatives in Danish, and provides an example. However, it does not explicitly mention when not to use this tool or name alternative tools, missing a clear exclusion. The note about near-synonyms not being included is a limitation but not a direct comparison 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?

With no annotations, the description fully discloses behavior: it explains two return modes (multiple results vs single-result redirect), the DDO-derived label composition, language parameter limitations, truncated definitions, and the exact structure of returned data. It even provides examples of both return cases. This goes well beyond a minimal description.

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 long but well-structured with headings (DDO CONNECTION, RETURN BEHAVIOR, Args, Returns, Examples). The front-loaded purpose makes it easy to grasp. While some sections (e.g., DDO label composition) are very detailed, they are relevant to interpreting results. It earns its length due to the tool's complexity, though a slightly tighter edit would improve conciseness.

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

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

Given the tool handles two distinct return modes and an ontology model, the description covers all aspects: return types, semantic relationships, language constraints, and even how to interpret labels. It includes practical examples. No output schema was provided, so the detailed Returns section is essential and thorough.

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 0%, so the description carries the full burden. It explains 'query' as 'The Danish word or phrase to search for' and 'language' as controlling labels/definitions with a default of 'da' and availability of 'en'. It explicitly states only Danish words can be searched. This fully compensates for the empty schema 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 starts with a specific verb and resource: 'Get synsets (word meanings) for a Danish word, returning a sorted list of lexical concepts.' It clearly distinguishes from sibling tools like get_word_synonyms by focusing on synsets (meanings) rather than synonyms. It also differentiates from get_synset_info by explaining the two return modes.

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

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

The description provides clear context for when to use the tool: 'This function returns all synsets associated with a word, effectively giving you all the different meanings/senses that word can have.' It also notes the single-result redirect case is equivalent to calling get_synset_info(). However, it does not explicitly list alternatives or exclusions, leaving some ambiguity among the many 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 provided, the description carries the full burden, and it excels. It discloses automatic prefix declaration, query validation, auto-retry with inference when results are empty, the fact that both DanNet and English WordNet are scanned if queries are unanchored, and the behavior of the custom dnf: similarity functions. It also explains performance implications, making the tool's operational characteristics clear.

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 long but exceptionally well-structured: purpose, critical rules, fast templates, known prefixes, args, returns. It is front-loaded with the most important 'CRITICAL PERFORMANCE RULES' that prevent costly mistakes. The 11 query templates are directly reusable. Every section earns its place, and the formatting makes it scannable despite the length.

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, the description is remarkably complete. It covers performance constraints, naming conventions, namespace prefixes, inference modes, return format, and validation. The presence of an output schema reduces the need to detail return values, yet the description still includes a helpful summary. For an agent invoking a SPARQL endpoint, this provides all necessary context to operate effectively.

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

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

The input schema has 0% description coverage, so the description must compensate. The 'Args:' section thoroughly explains all five parameters: query, timeout (with default and max), max_results (with default and max), distinct (with use case for disabling), and inference (explaining None/True/False and when to use each). This is far beyond mere schema field names.

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 opens with a specific action and resource: 'Execute a SPARQL SELECT query against the DanNet triplestore.' It clearly identifies this as the generic, direct-access query tool, distinguishing it from the specialized sibling lookup tools like get_word_synsets and get_synset_info. Its scope is further narrowed by stating 'Only SELECT queries are supported.'

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 'CRITICAL PERFORMANCE RULES' section provides explicit when-to-use guidance, including direct alternatives: 'Use get_word_synsets("hund") first, then query specific synset URIs.' It also tells when to use certain query patterns (e.g., use VALUES over FILTER, use property paths) and what to avoid (whole-graph scans, cartesian products). This is concrete, actionable guidance that an agent can follow to select this tool appropriately.

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 and provides good disclosure: it changes runtime endpoint, accepts local/remote/custom URL with validation (must start with http:// or https://), and returns a status dict with previous/current URLs. It doesn't mention side effects on ongoing requests or persistence, but for a config switch it's 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?

Well-organized with Args, Returns, and Example sections; slightly redundant first two sentences but still focused and 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?

For a one-parameter tool with a simple output, the description is comprehensive: explains the parameter options, return structure, and usage scenario. The included examples further clarify expected 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?

The schema has no description for the 'server' parameter, and the description fully compensates by detailing the allowed values ('local' maps to localhost:3456, 'remote' to wordnet.dk) and custom URL format with examples.

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 opens with 'Switch between local and remote DanNet servers on the fly,' a specific verb+resource+scope statement. It clearly distinguishes from sibling tools like get_current_dannet_server by indicating this tool changes the endpoint rather than reads it.

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 states it's 'useful for switching between development (local) and production (remote) servers' and emphasizes 'without restarting the MCP server,' giving clear context. However, it does not explicitly name alternative tools or 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?

Annotations are absent, so description carries full burden. It mentions the tool validates and analyzes data and returns a dict with results, implying read-only behavior. However, it does not disclose error handling, side effects, or permissions, 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?

The description is concise and front-loaded with the purpose. The Args and Returns sections are clear and structured, but the second sentence 'This enhanced tool helps debug...' is somewhat redundant with the first, adding mild fluff.

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 one-parameter tool, the description covers the input source and the return type. It provides enough context for an agent to decide when to invoke it. It lacks explicit side-effect disclosure, but given the tool's validation nature, this is a minor gap.

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 0%, but the description compensates by explaining that synset_data is the return value from get_synset_info(), providing essential context for the single parameter. It adds meaning beyond the schema's bare object type, though not detailed structure.

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 validates and analyzes synset JSON-LD data, with a specific verb and resource. It distinguishes itself from siblings like get_synset_info (which fetches data) by focusing on structural validation and analysis.

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 Args section explicitly states that synset_data is the output from get_synset_info(), giving clear context on when to use this tool (after fetching synset data). However, it does not explicitly state when not to use it or mention alternative tools for validation.

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