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

With no annotations provided, the description fully carries the burden. It discloses the data source, caching behavior (24-hour cache), local filtering, and status markers. It also describes the return structure with JSON fields. No destructive or auth-related information is needed as it is a read-only query tool.

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

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

The description is well-structured with clear sections (FUNCTION NAME, DESCRIPTION, DATA SOURCE, STATUS MARKERS, PARAMETERS, RETURNS, EXAMPLES, USE CASES). It is somewhat verbose but every section adds value. The key purpose is front-loaded in the first line.

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

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

Given the tool's low complexity (2 optional parameters, no required params, output schema present), the description is complete. It explains all possible behaviors, return fields, status meanings, and provides multiple usage examples. An agent can use this tool without 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 coverage is 100% (both query and limit are defined). The description adds significant value: it clarifies query expects English keywords, provides concrete examples (inline declaration, CORRESPONDING, CDS), shows how to use limit with an example, and demonstrates empty query behavior. This greatly aids correct parameter 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 explicitly states it searches the ABAP Feature Matrix to check feature availability across SAP releases. It clearly distinguishes itself from sibling tools that search SAP community or discovery center, making the purpose unambiguous.

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

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

The 'USE CASES' section lists four specific scenarios for when to use this tool (check feature availability, find introduction, compare releases, get full matrix). While it doesn't explicitly state when not to use or mention alternatives, the scenarios are clear and comprehensive.

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, so description carries full burden. It states returns full document text with metadata and lists return fields. Implies safe read operation, but could disclose potential costs or limits. Still, adequate 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?

Well-structured with headers (usage, parameters, returns). Front-loaded with purpose. A bit verbose but every sentence adds value. Minor redundancy could be trimmed.

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?

Simple 1-param tool with output schema. Description covers workflow, parameter usage, and return fields. Nothing missing.

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

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

Schema already has 100% coverage with description and examples. Description adds crucial context: ID must come from search results and be exact. Adds meaning 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?

Clearly states 'Retrieves the full content of a document from search results.' The verb 'fetch' and resource 'full content of a document' are specific. Distinguishes from sibling 'search' by outlining the workflow: use search first, then fetch.

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

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

Explicit usage instructions provided: '1. First use search() to find relevant documents, 2. Use the 'id' from search results to fetch full content.' Also emphasizes using exact IDs. Clearly guides when to use and implies prerequisite.

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, but the description fully compensates by disclosing the underlying API (Khoros LiQL), automatic full content retrieval for top 3 posts, and detailed parameter behavior (minKudos defaults and effects). It covers all relevant behavioral aspects for a 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?

The description is well-structured with clear headings (IMPORTANT, PARAMETERS, RETURNS, WORKFLOW) and front-loaded with the core purpose. Every sentence adds value, and there is no redundancy. It is concise yet comprehensive.

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 very complete, including return format details and workflow. However, there is slight ambiguity: the description says it returns results from top 3 posts but also has a k parameter for number of results. Clarifying the relationship between k and the top 3 limit would improve completeness. Still, it is fully functional.

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 adds significant value by explaining the semantic meaning of minKudos values, recommending specific query types like error messages, and clarifying defaults. This goes well 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 it searches SAP Community blogs, Q&A, and discussions, returning full content of top matching posts. It distinguishes from the sibling tool 'search' which already includes community results, making the purpose specific and differentiated.

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

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

The description provides explicit when-to-use guidance: use the main search first, then this tool if results are insufficient or for specific scenarios like error messages. It also gives a clear workflow with steps, and explains 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, description carries the burden. It clearly explains the search behavior, filtering options, and output. However, it does not disclose any rate limits or potential side effects, but as a search tool, it is sufficiently transparent.

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

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

Well-structured with bold summary, function call example, use case list, exclusions, parameter details, and output explanation. Every sentence is valuable 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 search tool with 4 parameters and no output schema, the description covers purpose, usage, parameters, and return format comprehensively. No 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 coverage is 100%, baseline 3. Description adds value by providing examples for query, explaining default/max for top, listing categories, and enum for license_model. It also describes the output format, which is not 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 'Search the SAP Discovery Center service catalog to find SAP BTP services', specifies verbs like 'find', 'discover', 'browse', and lists concrete use cases. It distinguishes itself from siblings like sap_discovery_center_service and search.

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

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

Explicit when-to-use (e.g., 'Find which SAP BTP services are available for a specific use case') and when-not-to-use ('Do NOT use this for ABAP documentation... use the 'search' tool for those'). Also provides follow-up action: 'After finding a service, use sap_discovery_center_service to get full details.'

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 carries full burden. It discloses auto-resolution of serviceId, default parameter values, and detailed return structure. Does not mention error handling or rate limits, but overall transparent for a 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?

Description is front-loaded with a concise function signature, then organized into use-case bullets and detailed sections. Slightly verbose in the return structure but well-structured and informative.

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 no output schema, the description provides a comprehensive return structure (JSON fields). Covers use cases, parameter details, auto-resolution, and defaults. Complete for a moderately complex 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%, and description adds valuable semantics: auto-resolution for serviceId, default currency 'USD', and defaults for boolean flags. All parameters are explained 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 explicitly states 'GET SAP BTP SERVICE DETAILS' and lists specific data returned (pricing, roadmap, documentation). This clearly distinguishes it from sibling tools like sap_discovery_center_search (search) and sap_get_object_details (general details).

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 bulleted list of when to use, covering pricing, roadmap, docs, service plans, etc. It does not explicitly state when not to use, but it implies context (e.g., needing a service name or UUID). Lacks explicit alternatives but clear 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, and it does well: it discloses the TADIR fallback behavior, that a TADIR lookup resolves to a single member while multiple members may exist, and enumerates the returned JSON fields including successor and complianceStatus. It does not cover error/not-found behavior or any auth or rate constraints, which is the remaining 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?

Well front-loaded with a concrete invocation example followed by scoping rules, a use-when list, a level glossary, parameter notes, and a returns block. It is long, and the RETURNS block plus parameter bullet list partially duplicate the schema, but the structure keeps each section scannable and purposeful.

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?

There is no output schema, so the description must describe returns, and it does so field by field (found, objectType, tadirObject, state, cleanCoreLevel, applicationComponent, successor, complianceStatus). Combined with the Clean Core level glossary and system_type semantics, an agent has everything needed to call and interpret this 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%, so the baseline is 3, but the description adds real meaning beyond the schema: it explains the distinction between 'actual' vs TADIR type for object_type selection and clarifies that target_clean_core_level triggers an explicit compliance verdict. Only minor value-add, but it goes past restating 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?

States a specific verb and resource ('Get complete release state details by actual object type and name') and immediately distinguishes itself from the sibling sap_search_objects by describing the TADIR fallback and the one-member-per-lookup behavior. An agent can tell what this returns (release state, Clean Core level, successor) without opening the schema.

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 an explicit bulleted 'Use this tool when you:' list covering verification, Clean Core compliance, successor lookup, and full object context. It also names the alternative route explicitly ('To list all members, use sap_search_objects with its TADIR type as object_type, name as query, and clean_core_level="D"'), so the routing decision is unambiguous.

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 largely succeeds: it names the data source, discloses the return shape (objects/total/hasMore/nextOffset) and pagination, and explains the cumulative nature of clean_core_level. It omits auth/permission requirements and rate limits, which keeps it short of a 5.

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?

Front-loaded with the intent and a usage/anti-usage section, which is efficient for an agent scanning for routing. The trailing parameter enumeration duplicates structured schema data and is the one part that does not fully earn its space.

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

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

For an 8-parameter filter tool with no output schema, the description compensates by documenting the return object, pagination fields, the data source, and the semantics of the trickier filters. It is largely complete, with only operational details (auth, freshness of release data) left unstated.

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

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

Schema description coverage is 100%, so the schema already documents all eight parameters. The description's parameter block largely restates the schema (query examples, defaults, enums), and the cumulative clean-core note adds only marginal meaning over the schema's 'A=Released only ... D=all'. 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?

States a specific verb+resource+scope: searching SAP released objects from the official SAP API Release State repository. It explicitly names the sibling it is not for (use `search` for docs/code/help), so an agent can separate it from the other search tools without opening a schema.

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 an explicit bulleted list of when-to-use scenarios (topic discovery, application component lookup, system-type browsing, clean-core filtering) plus a negative instruction routing documentation queries to the `search` tool. Both the selecting condition and the alternative are stated.

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 behavioral burden. It discloses important traits such as offline vs. online source behavior, a 10-second per-source timeout, English-language ranking bias, and the fact that sample-heavy sources can dominate broad queries. It also clarifies that abapFlavor and product are query-domain signals rather than system flags, preventing misconfiguration.

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 headings and front-loads the purpose, but it is overly verbose and duplicates content already present in the schema (e.g., parameter details and return format). The RETURNS section is unnecessary because an output schema exists, and the parameter descriptions could be shorter given the high schema coverage.

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 complex tool with 8 parameters, many sources, and no annotations, the description is complete: it covers source types, online/offline behavior, parameter routing, typical workflow, and escalation. Nothing an agent needs to call it correctly is missing, even though some details are redundant with the 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?

Schema description coverage is 100%, and the schema already contains detailed parameter descriptions (including discovery instructions for product and version, and abapFlavor routing). The main description largely repeats this information in its PARAMETERS section without adding significant new meaning 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 states a specific verb (search) and resource (SAP development documentation), enumerating covered domains (UI5, CAP, BTP, ABAP, RAP). It explicitly distinguishes from the fetch tool ('then call fetch(id=...)') and from sap_community_search (escalation path), so an agent can identify its role without opening the schema.

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 explicit when-to-use guidance, including the typical workflow (search then fetch) and a clear escalation rule to sap_community_search when results are unhelpful. It also provides detailed conditions for toggling includeOnline, includeSamples, abapFlavor, product, and version, leaving no ambiguity about how to route queries.

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

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

With no annotations provided, the description carries full burden. It clearly discloses the data source (local all-changes bundle), runtime behavior (local-only, no hosted URLs), version resolution logic (nearest lower available patch), and signals like meta.notes for stale data. No contradictions.

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

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

The description is well-structured with sections, front-loaded purpose, and clear examples. However, it is quite verbose; some information could be condensed without losing clarity. Still, the structure aids readability.

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

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

Given the tool's complexity (7 parameters, no required, output schema exists), the description is highly complete. It covers data source, version resolution, return structure, and use cases. The presence of output schema does not reduce the need because description still adds context on meta fields and version resolution.

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% (baseline 3), but the description significantly adds value: it explains range semantics (exclusive from, inclusive to), details each parameter's role, provides examples, and clarifies edge cases like single version usage. This goes well beyond the 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?

Description clearly states the tool lists new features, fixes, deprecations, and What's New entries between two UI5 releases or for one release. It also specifies the use case of upgrade planning. This makes the purpose distinct from sibling tools which cover ABAP, community search, etc.

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 says to use this tool for upgrade planning and gives concrete use cases (upgrade planning, workaround cleanup, library-scoped review). It also mentions combining with other @ui5/mcp-server tools. However, it does not explicitly contrast with alternative tools or state when not to use it, so it's slightly below perfect.

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