CERN GitLab MCP Server
Server Quality Checklist
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Tool Scores
- Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full disclosure burden. It mentions key behavioral traits (recursive listing, path-specific lookups) but omits critical details like pagination behavior, rate limiting, safety profile (though implied by 'List'), or the structure of returned data.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences with zero waste. The first sentence front-loads the core purpose, and the second efficiently highlights the key capabilities. Every word earns its place without redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given 5 well-documented parameters but no output schema or annotations, the description is minimally adequate. It successfully conveys the tool's function but has clear gaps regarding return values, error conditions, and operational constraints that would help an agent invoke the tool effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, establishing a baseline of 3. The description references parameters implicitly ('recursive listing', 'path specific lookups') but adds no semantic meaning, syntax examples, or parameter relationship constraints beyond what the schema already documents.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('List') and resource ('files and directories in a CERN GitLab project's repository'). It implicitly distinguishes from siblings like get_file_content by emphasizing directory listing capabilities, though it doesn't explicitly name alternatives or clarify when to browse vs. read.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no explicit guidance on when to use this tool versus alternatives like get_file_content or search_code. While it mentions capabilities (recursive, path lookups), it doesn't state prerequisites, exclusion criteria, or decision criteria for tool selection.
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 provided, the description carries the full burden. It correctly identifies the tool returns a 'comprehensive summary' and performs read-only analysis, but fails to disclose execution time expectations, rate limiting, cache behavior, or the specific structure/format of the returned technical stack summary.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
Three sentences efficiently cover: (1) core purpose and scope, (2) aggregation characteristic distinguishing it from atomic operations, and (3) return value description. No redundancy or filler content; information density is high with clear logical progression.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the absence of both annotations and output schema, the description adequately explains the tool's analytical scope but insufficiently compensates for missing return value documentation. For a complex analysis tool aggregating multiple inspection types, the 'comprehensive summary' description is too vague regarding output structure.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage with clear examples (numeric ID vs. path format for 'project', branch/tag/commit for 'ref'). The description implies the 'project' parameter targets CERN GitLab repositories but does not add semantic constraints or usage notes beyond what the schema already provides, warranting the baseline score.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool analyzes CERN GitLab repositories with specific focus on structure, build systems, and dependencies. It distinguishes itself from siblings like get_project_info and get_file_content by emphasizing deep technical analysis (CI/CD inspection, dependency analysis) rather than basic metadata or file retrieval.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description notes that the tool 'combines functionality' of multiple analysis types into a single operation, implying it's a convenience wrapper for comprehensive analysis. However, it lacks explicit guidance on when to use this aggregated tool versus specific sibling tools, or prerequisites like repository access permissions.
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 provided, the description carries the full burden of behavioral disclosure. It successfully explains the automatic Git reference resolution logic (a key behavioral trait), but lacks critical safety information such as rate limits, authentication requirements, or what happens when a stack name is invalid.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
Extremely efficient two-sentence structure with zero waste. The first sentence establishes scope and resource, the second discloses key automation behavior. Every word earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Adequately covers the input intent and search scope, mentioning 'code snippets' as the return type. However, with 7 parameters, no output schema, and no annotations, the description should ideally disclose more about result structure (e.g., whether matches include line numbers, file paths, or repository metadata) to be fully complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so parameters are well-documented in the structured fields. The description adds minimal semantic value beyond the schema—it repeats the 'sim11' example already present in the schema and restates the auto-resolution logic also described in the ref parameter's description. Baseline 3 is appropriate when schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
Clearly states the tool searches for code snippets within a specific LHCb software stack, using 'sim11' as a concrete example. The mention of 'Automatically resolves the correct Git references' distinguishes it from the generic sibling tool 'search_code' by highlighting stack-specific behavior, though it could explicitly name the sibling for clearer differentiation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
Implies usage context through the LHCb-specific stack terminology and auto-resolution feature, but fails to explicitly state when to use this versus the generic 'search_code' tool or other siblings. No guidance on prerequisites (e.g., valid stack names) or exclusion criteria.
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. Discloses return payload (metadata, statistics, description) but omits safety profile, error behavior, auth requirements, or rate limits. 'Get' implies read-only operation.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
Two well-constructed sentences: first states purpose and return value, second clarifies input format. Front-loaded with no redundant or wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a single-parameter read tool, description adequately compensates for missing output schema by listing return contents (metadata, statistics, description). Would benefit from safety/auth notes given zero annotations, but acceptable scope for simple getter.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema has 100% description coverage ('Project identifier...'). Description adds minimal value beyond schema, essentially repeating the format options with the same example ('atlas/athena'). Baseline 3 appropriate when schema does heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
Clear verb ('Get') and resource ('CERN GitLab project' information), specifying output contents (metadata, statistics, description). Distinguishes from file/issue/search siblings, though could explicitly differentiate from similarly-named 'inspect_project' sibling.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides input format guidance (numeric ID vs path) with examples, but lacks explicit 'when to use' guidance comparing it to sibling tools like 'inspect_project' or 'get_project_readme'.
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 provided, the description carries the full burden of behavioral disclosure. It successfully explains the dual operational modes (listing vs. retrieving specific pages) tied to the optional parameter. However, it fails to disclose whether the operation is read-only, what format content is returned in (markdown/HTML), or error handling behavior when pages/projects don't exist.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description consists of three efficient sentences with no redundancy. The first establishes scope, the second explains the dual functionality, and the third provides use-case context. Every sentence earns its place with zero waste words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the simple schema (2 parameters, no nesting, 100% coverage) and lack of output schema, the description provides adequate context for basic usage. However, it could be improved by describing the return format (e.g., whether it returns raw markdown, HTML, or a structured list) since no output schema exists to document this.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has 100% description coverage, establishing a baseline of 3. The description aligns with the schema by explaining that omitting page_slug results in listing all pages, while providing it retrieves specific content. It adds minimal semantic meaning beyond the schema itself but confirms the behavioral mapping of the optional parameter.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly identifies the resource (wiki pages from CERN GitLab) and the actions (list all or retrieve specific content). It distinguishes from siblings like get_file_content and get_project_readme by specifying 'wiki' specifically. However, the initial verb 'Access' is slightly vague, though clarified by 'list' and 'retrieve' in the second sentence.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides implied usage context ('Useful for accessing project documentation that lives in the wiki'), suggesting when to use it. However, it lacks explicit guidance on when to choose this over sibling documentation tools like get_project_readme or get_file_content, and does not mention prerequisites or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It successfully discloses the return structure ('tag names with their associated commit references'), but omits other critical behavioral traits like pagination behavior, rate limiting, or authorization requirements. The read-only nature is implied by 'List' but not explicitly stated.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description consists of three well-structured sentences with zero waste. It is front-loaded with the core action, followed by return value details, and ends with use case context. Every sentence earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (4 simple parameters with 100% schema coverage) and lack of output schema, the description is appropriately complete. It compensates for the missing output schema by describing the return values, and provides sufficient context for an agent to invoke the tool correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, establishing a baseline score of 3. The description adds minimal parameter-specific semantics beyond the schema, though it provides valuable domain context by specifying 'CERN GitLab repository' which helps interpret the 'project' parameter format.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('List tags') and resource ('CERN GitLab repository'), and specifies the return format ('tag names with their associated commit references'). It loses one point because it does not explicitly distinguish from the sibling tool 'list_releases', which handles GitLab releases (distinct from git tags).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides implied usage context ('Useful for finding version history and release points'), indicating when the tool is valuable. However, it lacks explicit guidance on when NOT to use it or when to prefer 'list_releases' or 'get_project_info' instead.
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 effectively discloses the auto-detection behavior for filenames and raw content return format. However, it omits error handling (what happens if no README exists?) and safety confirmation (though 'Get' implies read-only).
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
Two well-structured sentences with zero waste. Front-loaded with core action, followed by behavioral specifics (auto-detection) and utility (useful for...).
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a 2-parameter tool without annotations or output schema, the description is nearly complete. It compensates for missing output schema by specifying 'raw content' return. Minor gap on error cases, but sufficient for agent selection.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, establishing baseline 3. Description adds domain context by specifying 'CERN GitLab project' but does not elaborate on parameter syntax or validation rules beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
Description uses specific verb 'Get' with resource 'README file content' and scope 'CERN GitLab project'. The auto-detection capability clearly distinguishes it from generic file retrieval siblings like get_file_content.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides implicit usage context via 'useful for understanding what a project does', but lacks explicit when-to-use guidance or named alternatives (e.g., 'use get_file_content instead for specific paths').
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 successfully discloses return value structure ('release tags, dates, and descriptions') compensating partially for missing output schema, but omits explicit safety declarations (read-only status), pagination behavior details, or error conditions.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
Three well-structured sentences: action statement, return value disclosure, and use case. Every sentence earns its place with zero redundancy. Information is front-loaded with the core action in the first sentence.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema exists, the description appropriately documents return values ('tags, dates, and descriptions'). With only 2 simple parameters and no nested objects, the description is sufficiently complete, though it could explicitly confirm the read-only nature of the operation given lack of annotations.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100% with clear descriptions and examples for both parameters ('project' identifier formats, 'per_page' constraints). Description adds no parameter-specific semantics beyond the schema, which is appropriate when schema coverage is high (baseline 3).
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
Clear specific verb ('List') + resource ('releases') + scope ('CERN GitLab repository'). Distinguishes from sibling 'get_release' (singular) and 'list_tags' by specifying it returns full release metadata including 'tags, dates, and descriptions' rather than just git tags.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides implied usage context ('Useful for tracking software versions and finding changelogs') but lacks explicit guidance on when to use this versus siblings like 'get_release' (for single releases) or 'list_tags' (for lightweight tag listings without full release metadata).
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 successfully discloses scope limitation ('public' projects only) and what projects contain, but omits safety confirmation (read-only nature), authentication requirements, rate limits, or error handling behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
Two well-constructed sentences with zero waste. Front-loaded with the core action and scope (public CERN GitLab projects), followed by use-case context. The parenthetical explaining project contents adds value without verbosity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the 100% schema coverage and simple flat structure, the description adequately covers the tool's purpose and domain. Minor gap: no output schema exists, so a brief note about return format (list of projects with metadata) would improve completeness.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema has 100% description coverage, establishing baseline of 3. Description mentions the three primary filter parameters (keywords, topics, language) but adds no syntax guidance, example values, or semantic context for the sorting/pagination parameters beyond what the schema provides.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states the specific action (search), resource (public CERN GitLab projects), and searchable fields (keywords, topics, programming language). It distinguishes from siblings like search_code and search_issues by specifying it finds 'projects' rather than code snippets or issues.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides context on when to use ('discovering HEP code, analysis frameworks, and physics tools'), but lacks explicit differentiation from sibling search tools like search_code, search_issues, or search_lhcb_stack regarding when to use each search modality.
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 provided, the description carries the full burden of behavioral disclosure. It effectively describes the return structure ('file content along with metadata like size, encoding, and a language hint') and crucial edge case handling ('Binary files are detected and reported without attempting to decode them'). It does not mention authentication requirements or rate limits, preventing 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.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
Three sentences with zero waste: sentence 1 states purpose, sentence 2 describes return values (critical given no output schema), sentence 3 handles the binary file edge case. Information is front-loaded and every sentence earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema exists, the description appropriately explains return values and metadata structure. With 100% parameter coverage and no annotations, the description adequately covers behavioral traits. A score of 5 would require mention of error cases (e.g., file not found behavior) or authentication prerequisites.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, establishing a baseline of 3. The description adds minimal semantic value beyond the schema, though it frames the 'project' parameter within the 'CERN GitLab repository' context. The schema already fully documents parameter formats and examples.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb ('Retrieve') with a clear resource ('content of a specific file') and scope ('CERN GitLab repository'). It clearly distinguishes from siblings like list_project_files (directory listing) and search_code (search functionality) by specifying this retrieves content of a specific, identified file.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context about what the tool does ('specific file'), implying it should not be used for directory listings (where list_project_files would apply). However, it lacks explicit when-to-use guidance, exclusions, or named alternatives (e.g., it doesn't clarify relationship to get_project_readme for README files).
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 full burden. Compensates well by disclosing return contents ('release notes, assets, commit info, and download links') since no output schema exists. However, lacks mention of error conditions (e.g., behavior if tag doesn't exist) or authorization requirements.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
Three efficient sentences with zero waste: purpose front-loaded, return values specified, and key requirement stated. No redundant information or filler text.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple 2-parameter retrieval tool with flat schema structure, description is adequate. Compensates for missing output schema by detailing return fields. Could improve by noting relationship to 'list_releases' for tag discovery, but functionally complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, documenting both 'project' (numeric ID or path) and 'tag_name' formats. Description reinforces that tag_name is required but adds minimal semantic value beyond what the schema already provides. Baseline 3 appropriate for high-coverage schemas.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
Clear specific verb ('Get') and resource ('detailed information about a specific release'). Explicitly mentions 'specific release' which effectively distinguishes it from sibling tool 'list_releases'. Scope 'from a CERN GitLab repository' is precise.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
States 'Requires the tag name of the release' implying prerequisite knowledge, but does not explicitly reference sibling 'list_releases' as the alternative for discovering available tags. Usage is implied rather than explicitly guided.
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 provided, the description carries the full burden. It discloses the return format ('Returns matching files with line-level context') and search scope, but omits safety characteristics (read-only status), rate limits, or detailed pagination behavior that annotations would typically cover.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
Three well-structured sentences with zero waste. The description front-loads the core action, follows with scope constraints, and concludes with return value and use-case utility. Every sentence earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given 100% schema coverage and no output schema, the description adequately compensates by describing the return value structure ('matching files with line-level context'). It covers the essential behavioral contract for a search tool, though it could further clarify result pagination or result object structure.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, establishing a baseline of 3. The description adds high-level semantic context ('globally across all public projects' reinforces the project parameter) but does not elaborate on specific parameter syntax or the 'blobs' vs 'filenames' enum distinction beyond what the schema already provides.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description opens with a specific verb-resource combination ('Search for code snippets across CERN GitLab repositories') and clearly distinguishes from siblings like search_issues and search_projects by specifying 'code snippets' and 'line-level context' as distinct outputs.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides clear use-case guidance ('Useful for finding usage examples of specific libraries, functions, or patterns') and scope context ('globally across all public projects or within a specific project'), though it does not explicitly name alternative tools like get_file_content for when full file retrieval is needed.
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. Adds valuable scope context ('CERN GitLab', 'discussions') not fully captured in schema parameter descriptions. However, lacks disclosure of pagination behavior, result ordering, result format, or safety profile (read-only nature).
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
Two well-structured sentences with zero waste. First sentence defines the core action and scope; second provides use case context. Information is front-loaded and appropriately sized for the tool's complexity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given 4 simple parameters with complete schema coverage, the description adequately covers tool purpose and usage context. Minor gap: no output schema exists, and description does not specify what data structure or fields are returned in results.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema has 100% description coverage, establishing baseline 3. Description provides high-level use cases that implicitly guide search_term content but does not add explicit parameter syntax, format constraints, or examples beyond what the schema already documents.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
Specific verb ('Search') + resource ('issues and discussions') + scope ('CERN GitLab projects') clearly stated. Explicitly distinguishes from sibling tools like search_code and search_projects by specifying the exact resource type (issues vs code/projects).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides clear positive guidance via three specific use cases (understanding library usage, finding error solutions, checking feature support). However, lacks explicit 'when not to use' guidance or direct comparison to alternatives like search_code.
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. Discloses return payload contents (version, auth status, health) which compensates for missing output schema, but omits safety profile (read-only/destructive), error conditions, or side effects.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
Two efficient sentences with zero waste. Front-loaded with action (test connectivity), followed by return value disclosure. Every word earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Adequately complete for a simple diagnostic tool with no parameters. Effectively compensates for absent output schema by documenting the three return value categories. Would need explicit usage timing (when to call) to achieve 5.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Zero parameters present. With 100% schema coverage (trivially) and no parameters to describe, meets baseline of 4.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
Specific verb 'Test connectivity' and resource 'CERN GitLab instance' clearly define scope. Explicitly distinguishes from operational siblings (get_, list_, search_ tools) by being a diagnostic/health-check tool rather than data retrieval.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
Implies usage pattern (checking connection health before operations) by describing return values (auth status, health), but lacks explicit guidance like 'use this first to verify connectivity' or 'call when experiencing connection errors'.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
GitHub Badge
Glama performs regular codebase and documentation scans to:
- Confirm that the MCP server is working as expected.
- Confirm that there are no obvious security issues.
- Evaluate tool definition quality.
Our badge communicates server capabilities, safety, and installation instructions.
Card Badge
Copy to your README.md:
Score Badge
Copy to your README.md:
How to claim the server?
If you are the author of the server, you simply need to authenticate using GitHub.
However, if the MCP server belongs to an organization, you need to first add glama.json to the root of your repository.
{
"$schema": "https://glama.ai/mcp/schemas/server.json",
"maintainers": [
"your-github-username"
]
}Then, authenticate using GitHub.
Browse examples.
How to make a release?
A "release" on Glama is not the same as a GitHub release. To create a Glama release:
- Claim the server if you haven't already.
- Go to the Dockerfile admin page, configure the build spec, and click Deploy.
- Once the build test succeeds, click Make Release, enter a version, and publish.
This process allows Glama to run security checks on your server and enables users to deploy it.
How to add a LICENSE?
Please follow the instructions in the GitHub documentation.
Once GitHub recognizes the license, the system will automatically detect it within a few hours.
If the license does not appear on the server after some time, you can manually trigger a new scan using the MCP server admin interface.
How to sync the server with GitHub?
Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.
To manually sync the server, click the "Sync Server" button in the MCP server admin interface.
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.
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