proof-of-commitment

Server Quality Checklist

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Latest release: v1.30.0
Disambiguation5/5
Each tool targets a distinct entity type (business, GitHub repo, Go module, npm package, PyPI package, domain) or a distinct operation (batch audit). No two tools have overlapping purposes; even the two business lookup tools are differentiated by search method (name vs. org number).
Naming Consistency3/5
Most tools use 'lookup_' prefix, but 'audit_dependencies' and 'query_commitment' break the pattern. Additionally, 'lookup_business' and 'lookup_business_by_org' have differing suffixes, though still consistent. The mix of verbs (lookup, audit, query) is a minor inconsistency.
Tool Count5/5
With 8 tools, the server covers the essential entity types (business, GitHub, Go, npm, PyPI, domain) plus a batch audit function, without unnecessary bloat. This count is well-scoped for the domain of commitment verification across multiple ecosystems.
Completeness4/5
The tool set covers the main package ecosystems (npm, PyPI, Go) and GitHub repos, but lacks other popular ecosystems like Ruby gems or Cargo crates. Businesses are limited to Norway only. A minor gap given the stated focus on 'commitment profiles' for common supply chain vectors.
Average 4.1/5 across 8 of 8 tools scored. Lowest: 3.4/5.
See the Tool Scores section below for per-tool breakdowns.
- 0 of 11 community issues answered or closed in the last 6 months
- 138 commits in the last 12 weeks
- Last stable release on June 13, 2026
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is passing
This repository is licensed under MIT License.
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.
This repository includes a glama.json configuration file.
If you are the author, simply .
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```
{
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}
```
Then . Browse examples.
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How is the quality score calculated?

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

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

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

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

Tool Scores

Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It explains the data source (Brreg) and the categories of commitment signals, but lacks details on edge cases (e.g., business not found, rate limits, required permissions).
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 a single paragraph with front-loaded purpose. It is concise but includes some marketing language ('can't be faked') that slightly reduces efficiency. Overall, it is well-structured for quick scanning.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
No output schema exists, so the description must explain return values. It lists the commitment categories, which is helpful, but omits details on output format, error handling, pagination, and data freshness. It is moderately complete but not fully sufficient for all agent needs.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with clear parameter descriptions. The description does not add substantial semantic value beyond the schema; it mentions the query parameter implicitly but does not enrich the meaning of 'maxResults' or provide additional constraints.
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 searches for a Norwegian business and returns its commitment profile from public data. It specifies the verb 'search' and the resource 'Norwegian business', but does not explicitly distinguish from the sibling tool 'lookup_business_by_org'.
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 verifying Norwegian businesses via government registers and lists the return data. However, it does not provide explicit guidance on when to use this tool versus alternatives like 'lookup_business_by_org' or when not to use it.
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 explicitly states the tool performs a lookup from public data (Brønnøysund Register Centre), implying read-only and non-destructive behavior. This is sufficient for transparency, though it does not detail any caveats like rate limits or data freshness.
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 a single sentence that efficiently conveys purpose, input, and output. It is front-loaded and contains no redundant information, earning its place. However, it could be slightly more structured (e.g., separate sentence for output details).
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple lookup tool with one parameter and no output schema, the description adequately explains the input and the expected return values (four signals). It is complete enough for an agent to understand what results to expect, though adding a brief note on the format of the returned data would improve completeness.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema covers 100% of parameters (orgNumber) with description. The tool description adds context about what the parameter is used for (Norwegian organization number) and example format, but essentially repeats schema info. Baseline 3 is appropriate as schema already does the heavy lifting.
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 action (lookup), the resource (Norwegian business by organization number), and the output (commitment profile with specific signals: longevity, financial health, operational activity, overall commitment score). It distinguishes itself from sibling tools like lookup_github_repo or lookup_npm_package by focusing on Norwegian businesses.
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 when to use this tool (when needing commitment profile for a Norwegian business), but it does not explicitly state when not to use it or provide direct comparison to sibling tools such as lookup_business or query_commitment. Usage context is clear but lacks explicit alternatives 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, the description must fully disclose behavior. It correctly indicates a read operation returning aggregated signals, but omits details like data freshness, verification method, potential rate limits, or error conditions. The claim 'verified' lacks elaboration.
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 consists of two clear, front-loaded sentences (35 words) with no filler. Every sentence provides essential information: query action, return values, and a qualitative remark about authenticity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple single-parameter tool without output schema, the description adequately covers purpose, inputs, and outputs. However, adding data source or recency info would improve completeness. Sibling tools are distinct enough to not cause confusion.
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 sole parameter 'domain' has 100% schema coverage, and the description adds value by specifying normalization ('lowercase without protocol or path'). This extra context clarifies input processing 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 queries 'verified behavioral commitment data for a domain' and lists exact return values (unique verified visitors, repeat visit rate, average time spent). It distinguishes from siblings by emphasizing 'real human engagement' as opposed to generic lookups.
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 the tool is useful for assessing authentic engagement ('harder to fake than reviews or content'), but lacks explicit guidance on when to use this tool versus alternatives (e.g., when to prefer audit_dependencies). No exclusions or prerequisites are mentioned.
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 fully explains the risk flag criteria (CRITICAL, HIGH, WARN), sorting by commitment score, and batch size constraints (up to 20 packages). It lacks explicit read-only safety confirmation but is otherwise transparent.
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 a summary, risk definitions, and examples. It is concise and front-loaded, though slightly verbose in the examples section.
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 simplicity (2 params, no output schema), the description fully explains input, output format, risk logic, and usage scenarios. No gaps remain.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the baseline is 3. The description adds no new parameter semantics beyond what the schema provides, but the schema is clear.
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 specifies the verb 'batch-score', the resource 'multiple npm or PyPI packages', and the outcome 'supply chain risk' with a risk table. It clearly distinguishes from sibling tools that perform single-package lookups.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit use cases like auditing package.json or requirements.txt, and examples of applications. However, it does not explicitly state when not to use this tool, though the batch nature implies it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It states the tool is read-only ('Get' and 'Returns'), works only with public repos, and describes the nature of the returned signals (behavioral commitments). It does not mention rate limits or authentication, but the read-only nature is clear.
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 a first paragraph explaining functionality and a second paragraph for use cases and examples. It is concise without redundant words, but could be slightly tighter.
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 low complexity (one parameter, no nested objects), the description fully covers what the tool does, what it returns, and when to use it. No output schema is present, but the listed signals provide adequate expectations.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with the 'repo' parameter's description already explaining the format and providing an example. The tool description repeats the example without adding new meaning beyond the schema, so baseline of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it gets a 'behavioral commitment profile' for any public GitHub repository and lists the returned signals. It distinguishes itself from sibling tools which cover different domains (business, npm, Go modules, etc.).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly lists use cases: 'vetting open-source dependencies, evaluating AI tools/frameworks, assessing vendor reliability.' It provides examples but does not specify when not to use or mention alternatives. Since siblings cover other package types, the context is clear enough.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It discloses behavioral signals: 'package age, download volume and trend, release consistency, publisher/owner count, and linked GitHub activity.' It also mentions a real-world supply chain attack to illustrate relevance. While it lacks details on output format or performance, the description is sufficiently transparent about what the tool returns.
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 concise, with three clear sections: purpose, detailed signals, and usage scenarios. It uses bold text for key phrases and provides specific examples. Every sentence adds value without redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a lookup tool with a single parameter and no output schema, the description is fairly complete. It explains what the tool does, what signals are returned, and when to use it. Minor gaps include lack of output format details, but these are secondary given the tool's simplicity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema covers the sole parameter 'package' with a description of 'PyPI package name' and examples. The description does not add new information beyond the schema; it merely repeats examples. With 100% schema coverage, a score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Get a behavioral commitment profile for any PyPI (Python) package.' It specifies the resource (PyPI packages) and the action (getting a profile). Sibling tools like lookup_npm_package and lookup_go_module differentiate by ecosystem, so the purpose is distinct.
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 explicit usage contexts: 'vetting Python dependencies, identifying abandonware, supply chain risk due diligence.' It also lists examples of packages. However, it does not explicitly state when not to use the tool or mention alternatives, which would justify a 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the burden. It discloses the nature of the tool (read-only, returns signals) and mentions supply chain attack relevance, publisher depth, and behavioral signals. It does not describe rate limits or API dependencies, but for a simple lookup tool, the transparency is sufficient.
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 concise: 4 sentences plus bullet-point use cases and examples. It front-loads the purpose, details outputs, then provides context and examples. Every sentence is informative and earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given one parameter and no output schema, the description explains the return values (signals list) and usage scenarios. It covers input semantics well, though lacks potential notes on data freshness or API limitations. Overall, it is adequately complete for a straightforward tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The single parameter 'package' has a schema description covering 100%. The description adds value by specifying scoped packages need '@' prefix and giving examples, which aids correct input. This exceeds the baseline of 3 for full schema coverage.
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 identifies the tool as retrieving a 'behavioral commitment profile' for npm packages, listing specific signals (package age, download volume, trend, etc.). It distinguishes from siblings like lookup_pypi_package and lookup_github_repo by focusing on npm packages, making the purpose precise and unambiguous.
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 explicitly states use cases ('vetting dependencies, identifying abandonware, due diligence') and provides examples. It does not mention when not to use it or name alternative tools, but the context signals and sibling list imply differentiation. The guidance is strong but lacks explicit exclusions.
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: input constraints, return signals, and ecosystem nuances (no download counter, GitHub-primary). 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?
Well-structured with separate paragraphs for output, ecosystem notes, and usage; not overly verbose. A bit lengthy but justified by the need to explain Go ecosystem specifics.
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 lacking output schema, the description enumerates all returned signals and addresses potential ambiguities (e.g., publisher vs contributor). The single parameter is fully documented.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema covers 100% of the single parameter, and the description adds value with examples, case-sensitivity mention, and context about module paths. Exceeds baseline.
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 obtains a 'behavioral commitment profile' for a Go module, listing specific signals (age, versions, etc.). It distinguishes from sibling tools which target different ecosystems or actions.
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?
Explicit usage scenarios are given ('vetting Go dependencies, abandonware identification, supply chain risk') with concrete examples. Lacks exclusion criteria but is sufficient for correct invocation.
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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