SAP Clean Core MCP Server

Server Quality Checklist

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Latest release: v0.1.0
Disambiguation5/5
Each tool targets a distinct operation: bulk classification, level explanation, successor search, statistics, object type listing, single lookup, partner search, component-based search, pattern search, and semantic search. There is no overlap or ambiguity.
Naming Consistency4/5
All tools share the 'cc_' prefix and most follow a verb_noun pattern (e.g., classify_objects, list_object_types). However, a few like 'partner_objects' and 'semantic_search' deviate from this pattern, introducing minor inconsistency.
Tool Count5/5
With 10 tools covering classification, search, statistics, and partner objects, the count is well-scoped for the SAP Clean Core compliance domain. Each tool serves a clear purpose without redundancy.
Completeness5/5
The tool set provides comprehensive coverage for Clean Core compliance: bulk and single object classification, level explanation, successor discovery, statistics, multiple search methods (exact, pattern, semantic, by component, by partner), and object type listing. No obvious gaps are present.
Average 4.1/5 across 10 of 10 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 2 commits in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI status not available
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How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the burden but only conveys basic purpose and supported partners. It doesn't disclose read-only nature, auth needs, or side effects, though the use case implies reading.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise with three sentences, front-loading purpose and supported partners, then adding use case. Minor redundancy (partner list repeated from schema) but overall efficient.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given 5 parameters and no output schema, the description does not explain the response structure or default behavior beyond the schema. It's adequate but could elaborate on output format or pagination details.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so baseline 3 applies. The description repeats the partner list from the schema but adds no new parameter-level meaning beyond what the schema already provides.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it searches ABAP objects from SAP partner namespaces, distinguishes from siblings by emphasizing 'partner' and 'cross-target', and provides a specific use case (check Clean Core).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for partner objects but does not explicitly state when not to use it or mention alternatives like cc_search_objects for non-partner objects.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. It discloses the return structure but does not mention any side effects, authentication needs, or limits. Since it is a read-only statistics tool, moderate transparency is adequate.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, front-loaded with purpose, no wasted words. Efficiently communicates the tool's output.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema, the description lists the return fields adequately. However, it does not mention pagination or potential size limits, which would be helpful for a statistics tool. Still, it is mostly complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100% (both parameters have descriptions). The tool description does not add extra meaning beyond the schema, so baseline of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states the tool provides 'Dataset summary and statistics' with a specific list of returns (total count, breakdowns, top items). It is distinct from sibling tools like cc_search_objects or cc_classify_objects, which perform other functions.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage as a summary tool but does not explicitly state when to use it over siblings, nor does it provide when-not or alternative guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden. It states the tool searches/finds objects, implying read-only, but does not disclose pagination, rate limits, authentication requirements, or output format beyond what is implied by response_format parameter.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Two concise sentences that front-load the purpose. Every word is useful, with no redundancy or fluff.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given 6 parameters and no output schema, the description explains the main purpose but does not clarify return behavior (e.g., what fields are returned in 'summary' vs 'full') or how the level_filter and other parameters affect results. The examples help but are insufficient for complete context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema covers 100% of parameters with descriptions. The tool description adds no additional parameter details; it only references components in a general way. Baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool finds ABAP objects by software or application component, and provides concrete examples like 'all MM-IM objects' and explicit use cases for Clean Core compliance and API discovery.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description gives explicit use scenarios (auditing functional areas, discovering APIs) but does not mention when to avoid this tool or provide alternatives like cc_search_objects for general searches.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided. The description lists return contents (classification, names, levels, advice) but does not disclose any behavioral traits like destructive potential, rate limits, or authentication needs. It adequately implies 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences: first states purpose, second lists return contents. No redundant words, front-loaded, highly efficient.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given 3 parameters all described in schema, no output schema, the description covers purpose and return value sufficiently. It could elaborate on how to interpret classification or migration advice, but overall it is adequate for a lookup tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with good descriptions for all three parameters. The tool description adds context about the return value but does not elaborate on parameter specifics beyond what the schema provides. Baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'find' and the resource 'successor APIs for deprecated or classic API ABAP objects'. It is distinct from sibling tools like cc_classify_objects or cc_search_objects, which serve different purposes.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description says 'Tells you exactly which Released API to migrate to when retiring a deprecated dependency', which implies usage context. It does not explicitly state when not to use or name alternatives, but the context is clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Discloses two key behaviors: uses most restrictive state for multiple entries (safety-first) and classifies not-found objects as Level C with a citation. No annotations exist, so the description carries the full burden; it adequately covers read-only nature and handling edge cases.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Four sentences front-load the core purpose, then add behavioral details and use cases. Every sentence adds value with no redundancy or filler.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema, the description explains the return table fields and key behaviors (multiple entries, missing objects). Minor gap: 'json' response format is only described as 'full detail per object' without specifics, but overall adequate for a 4-parameter tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so baseline is 3. The description adds overall context (e.g., object limit, classification logic) but does not significantly enhance understanding of individual parameters beyond the schema descriptions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
Description explicitly states 'bulk compliance check' and 'classify up to 500 ABAP objects at once,' with clear output fields. Distinguishes from sibling tools by focusing on batch classification for auditing transport lists or dependency inventories, which is unique among the listed siblings (e.g., cc_lookup_object for single lookups).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides explicit use cases: 'audit a transport's object list or a codebase's dependency inventory.' Lacks negative guidance or explicit alternatives, but the context strongly implies when to use this tool (batch) versus siblings (single lookups, explanations).
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the burden. It is a read-only explanation tool with no side effects mentioned. The description is adequate for a simple query tool, but does not disclose auth needs or rate limits.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, zero waste. The first sentence clearly defines functionality, the second suggests usage context. Well front-loaded and efficient.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (one parameter, no annotations, no output schema), the description is sufficiently complete. It explains the tool's purpose and its output content, which compensates for the lack of output schema.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with a single parameter. The description adds meaning beyond the schema's enum description by explaining what the tool outputs for a given level (states, ATC priority, action, governance). This helps the agent understand the value of the parameter.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool 'Explains what a SAP Clean Core level means' and specifies the aspects covered: states, ATC check priority, action required, governance guidance. It distinguishes from sibling tools that handle object classification, statistics, etc.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description includes 'Use this to communicate Clean Core compliance requirements to developers and architects', providing clear context for when to use. It does not explicitly state when not to use or list alternatives, but the purpose is well-defined.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. It implies a safe read-only search operation but does not explicitly state behavioral traits like destructiveness, authentication needs, or rate limits. The description is adequate but lacks extra context beyond the obvious safe nature of search.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Description is two sentences, front-loaded with purpose, no unnecessary words. Every sentence adds value (usage guidance and alternative).
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a 5-parameter tool with no output schema, the description covers core usage and provides alternative. Parameter details are well-documented in schema. Lacks explicit description of return format, but the response_format parameter partially covers it. Overall complete enough for typical usage.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
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 parameters thoroughly. The description repeats examples from schema but adds minimal new meaning. Baseline score of 3 is appropriate as description doesn't significantly enhance parameter understanding.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states the tool searches ABAP objects by name pattern with wildcard support, providing specific verb and resource. It distinguishes from sibling cc_semantic_search by specifying when to use each.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly provides when to use (prefix search or wildcards) and when not (natural language search), with clear alternative cc_semantic_search. Includes examples for proper usage.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It discloses behavioral traits: returns multiple entries if both DDLS and BDEF exist, and classifies not found objects as Level C. It also mentions default deployment and response format. However, it doesn't explicitly state read-only nature or authentication needs.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, well-structured paragraph. Every sentence adds value: it states purpose, lists return fields, and covers edge cases. No wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
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 explains return values and covers edge cases (duplicate entries, not found). For a 4-parameter lookup tool, it is complete and provides enough context for correct usage.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, baseline 3. The description adds value beyond schema by providing examples for object_name, explaining the effect of omitting object_type, expanding enum values for deployment_target and response_format. This clarifies parameter usage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool looks up a single ABAP object by name and returns Clean Core classification. It specifies the return fields and mentions special cases like returning both entries if the object exists as CDS view and RAP business object.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for looking up a single object but does not explicitly state when to use this tool versus alternatives like cc_search_objects or cc_list_object_types. No exclusions or when-not-to-use guidance provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries burden. It states listing all object types with counts, implying read-only behavior. Could explicitly mention safe/idempotent, but current description is adequate for a simple list operation.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, 25 words, front-loaded with action and purpose. Every sentence adds value without redundancy. Highly concise.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
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, and parameter hints. Could describe return structure (e.g., list of type-code and count), but is reasonably complete for a simple listing tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, and the description adds an example for the filter parameter ('e.g. 'CL' returns CLAS'), going beyond the schema. This provides concrete usage guidance.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool lists all ABAP object types with counts, using specific verb 'List' and resource 'ABAP object types'. It distinguishes from sibling tools like cc_lookup_object and cc_search_objects by indicating this tool is for understanding what types exist.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly says 'Use this to understand what types of objects are documented and discoverable via cc_lookup_object or cc_search_objects', providing a clear when-to-use scenario and naming specific alternative tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It discloses that the vector index builds in the background (~60 seconds) and that results are ranked by semantic similarity score. While it doesn't explicitly state read-only behavior, it implies a non-destructive search. The background index build is a useful behavioral detail.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Three sentences, no wasted words. First sentence states purpose and method. Second gives usage context and examples. Third mentions initialization latency and return structure. Efficient and well-organized.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
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, no output schema, and no annotations, the description covers the core functionality and usage. It could mention that the first call may be slow due to background indexing, but it already hints at that. Adequate for an AI agent to correctly invoke the tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so baseline is 3. The description adds value by providing concrete examples for the query parameter and clarifying the top_k range and response_format options. However, it largely restates the schema descriptions, so the added value is moderate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it performs natural language semantic search across ~200,000+ ABAP objects using AI embeddings. The verb 'search' and resource 'ABAP objects' are specific, and the semantic approach distinguishes it from sibling tools like cc_lookup_object (exact name) or cc_search_by_component (component-based).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly says 'Use this when you know WHAT the API should do but not its exact name' and gives concrete example queries. This provides clear usage context and implies alternatives (e.g., use exact-name tools when name is known).
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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