mcp-semclone

by SemClone

Overview Schema Related Servers Score Discussions

Python

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Server Quality Checklist

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Latest release: v1.6.2
Disambiguation5/5
Every tool has a clearly distinct purpose. While generate_legal_notices and generate_legal_notices_from_purls are similar, their descriptions explicitly differentiate them by input method and use case, preventing confusion. All other tools target unique aspects of compliance analysis.
Naming Consistency4/5
Tool names predominantly follow a verb_noun pattern (e.g., scan_directory, validate_policy), but there is minor inconsistency: some use hyphens (check_license_compatibility) while others use underscores consistently, and verbs vary (analyze, check, generate, scan, validate). Overall, the pattern is predictable and readable.
Tool Count5/5
With 14 tools, the server covers a comprehensive compliance workflow—from scanning and package analysis to license validation, legal notice generation, and SBOM creation. Each tool serves a clear role without redundancy, and the count is well-scoped for the domain.
Completeness5/5
The tool surface covers the entire lifecycle: scanning (source, binary), package analysis (simple and deep), license checks (details, obligations, compatibility), risk assessment, policy validation, legal notices (source and PURL-based), SBOM generation, and an end-to-end compliance workflow. No significant gaps are apparent.
Average 4.4/5 across 14 of 14 tools scored. Lowest: 2.2/5.
See the Tool Scores section below for per-tool breakdowns.
- 1 of 1 community issues answered or closed in the last 6 months
- 6 commits in the last 12 weeks
- Last stable release on July 24, 2026
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is passing
This repository is licensed under Apache 2.0.
This repository includes a README.md file.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
Tip: use the "Try in Browser" feature on the server page to seed initial usage.
Add a glama.json file to provide metadata about your server.
If you are the author, simply .
If the server belongs to an organization, first add glama.json to the root of your repository:
```
{
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}
```
Then . Browse examples.
Add related servers to improve discoverability.

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.

Tool Scores

Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must fully convey behavioral traits. It only says 'Analyze', implying a read-only operation, but does not state safety info, required permissions, or side effects. This is insufficient for a tool performing analysis.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single concise sentence, but it fails to include essential details (parameters, usage context). It is short but under-informative, so it earns a middle score for efficiency at the cost of completeness.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Despite having an output schema (not shown), the overall description is minimal. Given 13 sibling tools and 2 parameters, the description lacks details on scope, prerequisites, and specific use cases, making it incomplete for safe tool selection.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, and the description adds no information about parameters 'path' or 'include_data_files'. The agent cannot determine what values are valid or how parameters affect behavior.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Analyze commercial licensing risk for a project.' clearly states a verb and resource, but it is generic and does not differentiate from sibling tools like 'check_license_compatibility' or 'run_compliance_check'. The term 'commercial risk' is specific, but the description lacks scope or uniqueness.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. There is no mention of prerequisites, exclusions, or contrast with siblings like 'scan_directory' or 'validate_license_list'.
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?
The description explains the fallback logic and tool selection process, which adds transparency. However, there is a contradiction: description states check_vulnerabilities defaults to False, but the input schema shows default true. This inconsistency harms reliability.
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 well-structured with numbered steps, clear argument descriptions, and a return statement. It is concise yet informative, with no unnecessary repetition.
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?
The description covers the core logic and input, and an output schema exists. However, it lacks mention of whether the tool is read-only, and the default inconsistency reduces completeness. Overall adequate for a tool with output schema.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage, the description fully explains each parameter: identifier format (PURL, file path), and the purpose and defaults of the two boolean parameters. This adds significant meaning beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool checks a package using intelligent selection, and lists specific actions (archive extraction, license detection, PURL registry APIs). This distinguishes it from general scanning tools, but does not explicitly differentiate from all siblings.
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 package files or PURLs, but does not explicitly state when to use this tool over alternatives like scan_binary or check_license_compatibility. No when-not-to-use guidance is provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are present, so the description carries full burden. It clearly describes return categories (obligations, permissions, etc.) implying read-only behavior, but does not explicitly state no side effects or required permissions. Adequate but not exhaustive.
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?
Well-structured with separate sections for purpose, args, returns, and example. Slightly wordy with bullets, but every sentence adds value. Front-loaded with purpose.
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 the tool's scope (getting license obligations), the description is complete: covers input parameters, return categories with examples, and fits within the sibling context. No missing elements apparent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage, the description fully compensates by explaining each parameter: licenses as list of SPDX IDs with example, and output_format with possible values (json, text, checklist, markdown). Adds significant meaning beyond the schema.
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?
Clearly states verb 'Get' and resource 'obligations for specified licenses', with a specific question 'What must I do to comply?'. Differentiates from sibling tools like get_license_details by focusing on compliance obligations.
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?
While the description implicitly guides by stating the critical question it answers, it lacks explicit when-to-use guidance or mention of alternatives. No exclusions or comparisons with sibling tools are 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?
Describes data sources (purl2notices scan mode), output format, and included fields. Discloses that at least one of purls or path is required. However, does not discuss potential side effects like overwriting an existing output file or behavior when both purls and path are provided.
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?
Well-structured with sections and examples, but somewhat verbose with repetition (e.g., CycloneDX mentioned multiple times). Could be tightened without losing clarity.
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 the existence of an output schema (not shown), the description need not detail return values, yet it still provides a structured dictionary summary. Covers input modes, optional parameters, and examples, making it fully adequate for agent selection and invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Despite 0% schema coverage, the description provides detailed parameter explanations in the 'Args' section and examples. Each parameter's purpose, type, and default are clearly explained, adding significant meaning beyond the raw schema.
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 it generates an SBOM in CycloneDX format. It distinguishes from sibling tools (e.g., scan_directory, check_package) which focus on scanning or checking packages, not generating structured SBOM output.
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?
Includes a 'Use this tool when:' section listing specific scenarios like compliance documentation and security audits. Lacks explicit exclusions or comparisons to alternative tools, but the scenarios are clear and relevant.
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?
Describes the tool's behavior: analyzes compiled binaries, detects components, extracts licenses, identifies security issues. Notes that deep mode is slower and requires more thorough analysis. No side effects or modifications are implied, but does not explicitly state it is read-only. Sufficient given no annotations.
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?
Well-structured with summary, usage list, Args, Returns, and Examples. Each section adds value, but the 'Use this tool when' list slightly overlaps with the file-type examples. Still efficient and front-loaded with key purpose.
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 7 parameters, one required, and presence of many sibling tools, the description covers all necessary aspects: tool purpose, target file types, parameter details, return structure, and examples. No output schema provided but return dictionary is described adequately.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, but the description compensates fully with a detailed 'Args' section explaining all 7 parameters, including defaults, possible values (fast/standard/deep), numeric range for confidence_threshold, and boolean toggles. Examples illustrate 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?
Clearly states 'Scan binary files for OSS components and licenses using BinarySniffer', with specific verb and resource. Lists many file types and distinguishes from siblings like scan_directory or check_package which handle non-binary or package-level analysis.
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 this tool when' list covering multiple scenarios (mobile apps, executables, libraries, firmware, SBOM generation). However, it does not mention when to avoid using it or suggest alternative sibling tools for specific subtasks (e.g., using generate_sbom alone).
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. It thoroughly explains the tool's behavior: returns approve/deny/review decisions, describes output fields, and lists distribution-specific rules. However, it does not explicitly state that the tool has no side effects or is read-only, though the validation nature implies this.
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 well-structured with clear sections for purpose, use cases, return values, args, examples, and workflow. It is slightly verbose but every section adds value. The front-loading of purpose and key use cases is effective.
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 the tool's complexity (4 parameters, output schema), the description is fully complete. It covers all input parameters, output fields, behavior per distribution type, and provides multiple examples. It integrates with workflow and addresses typical use cases.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has 0% description coverage, so the description must compensate. It does so excellently: explains each parameter in detail (licenses as SPDX IDs, policy_file optional, distribution types with policies, context examples), provides allowed values, and includes examples showing 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's purpose: validate licenses against organizational policies for a specific distribution type. It uses specific verbs and resources, and distinguishes itself from sibling tools by positioning itself as the primary tool for answering 'Can I use these licenses for my project?'.
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 provides extensive usage context, including key use cases (mobile, commercial, SaaS, etc.) and workflow integration (post-scan, quick check, enforcement). It does not explicitly mention when not to use this tool or direct to alternatives, but given the clear use cases, it is well-guided.
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 the full burden. It thoroughly explains the return structure: compatible, reason, restrictions, recommendations. It also documents the context parameter options. There is no mention of destructive actions or auth requirements, but the tool appears read-only and safe.
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 well-structured: a one-line summary, a clarifying question, then clear sections for Args, Returns, and Example. Every sentence adds value. The example at the end concretely illustrates the tool's output, enhancing clarity without redundancy.
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 the tool's moderate complexity (3 parameters, output schema exists), the description is complete. It covers purpose, parameters with examples, return structure, and a concrete usage example. There is no need for additional context like rate limits or authorization for this type of tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, meaning the schema provides no descriptions. The description compensates fully by explaining each parameter: license1/2 are SPDX IDs with examples, and context lists its possible values (general, static_linking, dynamic_linking). This adds significant meaning beyond the raw schema.
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 explicitly states the tool's purpose: 'Check if two licenses are compatible for use together.' It includes a clarifying question 'Can I combine code under these two licenses?' and distinguishes from sibling tools like get_license_details or get_license_obligations by focusing specifically on compatibility.
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 clearly implies usage context: when combining code under two licenses. It provides a direct question the tool answers, but does not explicitly state when not to use it or mention alternative tools. The context parameter hints at different use cases (static vs dynamic linking), adding nuance.
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 covers key behavior: it retrieves data from an external API (SPDX), warns about large text size (~5-20KB), and indicates optional full-text inclusion. It doesn't mention auth or rate limits, but the disclosure is strong.
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 well-structured with sections for Args, Returns, and Example, using clear language. Every sentence adds value without redundancy, and the size is appropriate for the tool's detail level.
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?
Despite having an output schema, the description provides rich context about return fields and usage. It covers all necessary aspects for an agent to use this tool correctly, especially given the sibling tools focusing on analysis rather than raw data.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, but the description provides complete parameter details: license_id with examples (Apache-2.0, MIT, GPL-3.0) and include_full_text with size warning and default. It also exhaustively lists return fields, adding substantial value beyond the bare schema.
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 gets comprehensive details about a specific license, including full license text. It distinguishes from sibling tools like check_license_compatibility or get_license_obligations by focusing on raw 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 Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description specifies it's for generating NOTICE files and understanding license requirements, providing clear usage context. It lacks explicit when-not-to-use or 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.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden. It is highly transparent: details the workflow (three methods in order), performance expectations, security notes (verified downloads, no execution, cleanup), and explicitly warns that it WILL download source code. This exceeds typical behavior disclosure.
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 lengthy but well-organized with headers, bullet points, and sections. Each part adds value, and the structure aids readability. Could be slightly more concise, but the information density is justified given the tool's complexity.
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 the tool's complexity (multi-step workflow, 2 parameters, detailed outputs), the description is complete. It covers all relevant aspects: workflow, use cases, examples, performance, security, and return values. Even with an output schema (context indicates it exists), the description enriches understanding.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate fully. It explains 'purl' with examples and format, and 'keep_download' with its effect and default. Also details the return structure, adding significant meaning beyond the input schema.
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's function: 'Download package source from registry and perform comprehensive analysis.' It distinguishes itself from siblings like 'check_package' (which likely checks metadata) and 'scan_directory' (which scans local files) by emphasizing downloading and analyzing actual source files from registries.
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 'When to use this tool' section with bullet points and a real-world example. While it doesn't list alternative tools explicitly, the context makes it clear when this tool is appropriate versus others that might check metadata or scan local directories.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Although no annotations are provided, the description thoroughly explains the tool's behavior: it scans source directories, detects all packages including transitive dependencies, extracts copyright holders, fetches license texts from SPDX, and generates formatted output. It also describes the backend (purl2notices in scan mode) and the mode field in the return value.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is overly verbose and repetitive, with 'WHEN TO USE' and 'WHEN NOT TO USE' sections appearing multiple times. While well-structured overall, it contains redundant information and could be shortened without losing clarity.
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 the tool's complexity (4 parameters, many siblings) and lack of annotations, the description covers all necessary aspects: purpose, usage guidelines, parameter explanations, return value structure, and examples. The output schema is present and referenced in the Returns section.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 0% description coverage, but the description provides detailed explanations of all four parameters (path, output_format, output_file, include_license_text) with defaults and examples. This adds significant meaning beyond the schema's basic types and required field.
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 generates legal notices by scanning source code, specifying it as the primary and default tool. It distinguishes itself from siblings like generate_legal_notices_from_purls by emphasizing direct source code scanning for speed and automatic detection.
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?
The description provides explicit when-to-use and when-not-to-use sections, including specific alternative tools for different scenarios. It also includes workflow positions and common workflows, giving clear context for selecting this tool over others.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description fully discloses behavioral traits: performance warning (slow downloads), the fact that it downloads packages from registries, and the output mode in the return dictionary. It also cautions against incorrect usage, ensuring the agent understands the tool's cost and correct workflow.
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 lengthy but well-organized into sections (performance warning, critical note, when to use/not use, args, returns, examples). Every sentence serves a purpose, but the level of detail could be trimmed slightly without losing clarity. The structure aids readability and prioritizes critical information.
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 the tool's complexity (4 parameters, output schema, sibling alternative), the description is thorough. It covers parameter semantics, return values, performance characteristics, pitfalls, and example workflows. The output schema is provided, so return value explanation is sufficient. There are no gaps in context for effective tool invocation.
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?
The input schema provides basic parameter types and defaults (e.g., output_format defaulting to 'text', include_license_text defaulting to true). The description adds semantic value by explaining the 'purls' parameter as a list of Package URLs, describing the purpose of each parameter, and providing concrete examples. However, the schema already conveys some information, so the description's additional 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 the tool generates legal notices from a PURL list and distinguishes itself from the sibling tool 'generate_legal_notices' by specifying when each should be used. It provides a specific verb ('generate legal notices') and resource ('PURL list'), 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.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit when-to-use (dependencies not installed, have PURL list) and when-not-to-use (source code available, use generate_legal_notices) guidance. It also includes a critical warning against manual PURL extraction and illustrates proper usage with scan_directory examples.
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 fully carries the behavioral disclosure burden. It explains that the tool is a quick validation without deep analysis, and it details the return fields (safe_for_distribution, app_store_compatible, etc.). While it doesn't mention authentication or rate limits, it is transparent about its read-only nature and what the output contains.
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 well-structured with clear sections (intro, WHEN TO USE, WHEN NOT TO USE, WORKFLOW, RETURNS, Args, Returns). It is front-loaded with the core question and every sentence adds value without being verbose.
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 the tool's three parameters and output schema (described in Returns section), the description is complete. It covers the input parameters, return fields, workflow integration with siblings, and provides a common workflow example. No gaps are evident.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, but the description compensates by explaining each parameter: licenses (list of SPDX identifiers with example), distribution (target with enumerated options), and check_app_store_compatibility (boolean with effect). It adds meaning beyond the bare schema by providing context and examples.
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's purpose as providing a quick answer to 'Can I ship this with these licenses?' and specifies it analyzes a list of licenses without requiring a filesystem path. It distinguishes itself from sibling tools by explicitly listing when to use and when not to use them.
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?
The description includes explicit 'WHEN TO USE' and 'WHEN NOT TO USE' sections, naming alternative tools like scan_directory, validate_policy, and generate_legal_notices. It also provides the workflow position and a common workflow example, making it clear when to select this tool over others.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description fully discloses behavior: it executes a 6-step workflow, returns a comprehensive dictionary with decision, risk level, violations, etc. It also lists what each step does and the return structure, leaving no ambiguity.
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 well-structured with clear sections (workflow, args, returns, example) and is front-loaded with purpose. However, it is somewhat verbose with repeated lists and could be slightly trimmed without losing value. Still efficient overall.
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 the tool complexity (5 parameters, no annotations, output schema present but not shown), the description covers all necessary information: workflow, parameter details, return values with field explanations, and an example. It is complete for agent invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Although the input schema has 0% description coverage (only titles and types), the description provides detailed explanations for all 5 parameters, including defaults, optionality, and how they affect the workflow. This fully compensates for the schema's lack of 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?
The description explicitly states 'UNIVERSAL COMPLIANCE WORKFLOW: One-shot compliance check for ANY project type' and lists the steps. It clearly distinguishes itself from sibling tools like scan_directory by being a convenience tool that orchestrates multiple steps.
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?
The description includes dedicated 'WHEN TO USE' and 'WHEN NOT TO USE' sections, explicitly naming alternative tools such as scan_directory, validate_policy, etc. It provides clear guidance on when to call this tool vs. individual tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It comprehensively discloses behavior: scans entire dependency trees (including transitive), recursive scanning with max depth 3, deprecation of parameters, and what is detected (licenses, packages, vulnerabilities, policy). 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.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is long but well-structured with clear sections. It is slightly verbose due to repetition (e.g., 'FIRST STEP' appears multiple times), but the structure (uppercase headers, bullet points, examples) makes it easy to scan. Every sentence adds value.
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 the tool's complexity (6 parameters, no schema descriptions, 13 sibling tools), the description is remarkably complete. It covers purpose, scope, usage guidelines, parameter details, return values, deprecation notes, and workflow examples. No gaps remain for an AI to misuse the tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, but the description explains each parameter in detail: path (required), recursive (default true, max depth 3), check_vulnerabilities (default false, checks CVEs), check_licenses (deprecated, always true), identify_packages (deprecated, always false), policy_file (optional). It also describes the return dictionary structure, adding significant value beyond the schema.
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's purpose: scanning a directory for compliance issues using purl2notices. It distinguishes itself from siblings by explicitly marking itself as the FIRST STEP and providing alternative tools for specific scenarios (e.g., generate_legal_notices, scan_binary).
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?
The description includes explicit 'WHEN TO USE' and 'WHEN NOT TO USE' sections, clearly listing conditions and alternative tools. It also provides workflow examples showing typical next steps, making it easy for the AI to decide when to invoke this tool.
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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