RegistrumMCP

Server Quality Checklist

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Latest release: v1.0.2
Disambiguation5/5
Each tool has a clearly distinct purpose with no overlap: get_company retrieves company profiles, get_directors focuses on director histories, get_financials provides structured financial data, get_network maps corporate connections, and search_company handles company searches. The descriptions explicitly differentiate their scopes, eliminating any ambiguity.
Naming Consistency5/5
All tool names follow a consistent verb_noun pattern using snake_case (e.g., get_company, search_company, get_financials). The naming is predictable and readable throughout, with no deviations in style or convention.
Tool Count5/5
With 5 tools, the set is well-scoped for the domain of UK company data analysis. Each tool serves a specific, non-trivial function, and the count is appropriate for covering key aspects like profiles, directors, financials, networks, and searches without being too sparse or bloated.
Completeness5/5
The tool surface provides complete coverage for the domain, including retrieval (get_company, search_company), director analysis (get_directors), financial insights (get_financials), and network mapping (get_network). There are no obvious gaps, as it supports end-to-end workflows from search to detailed corporate analysis.
Average 4.1/5 across 5 of 5 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 26 commits in the last 12 weeks
- No stable releases found
- 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.
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}
```
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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

Behavior2/5
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 describes what data is returned but lacks behavioral details such as whether this is a read-only operation, if it requires authentication, rate limits, error handling, or pagination. The phrase 'in one call' hints at completeness but doesn't clarify limitations.
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 front-loaded with the core purpose in the first sentence, followed by additional context in the second. Every sentence adds value by detailing returned data and benefits, with no wasted words or redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no annotations and no output schema, the description covers the purpose and data scope well but lacks completeness on behavioral aspects like safety, performance, or error handling. For a tool with one parameter and no complex schema, it's adequate but has clear gaps in operational context.
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 has 100% description coverage for the single parameter (company_number), so the baseline is 3. The description adds value by specifying this is for 'UK company' and mentioning 'Companies House', which provides context beyond the schema's pattern example, though it doesn't detail parameter usage further.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'Get' and the resource 'current and past directors for a UK company', with specific details about the data returned (name, role, dates, nationality, etc.). It distinguishes from sibling tools by focusing on director information rather than company details, financials, networks, or search capabilities.
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 when director information is needed for a UK company, but it doesn't explicitly state when to use this tool versus alternatives like get_company or get_network. No exclusions or prerequisites are mentioned, leaving some ambiguity about 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 full burden. It discloses key behavioral traits: the tool returns all connected companies up to specified depth, includes specific fields for each company, and mentions that depth 2 'can return many results for large companies.' However, it doesn't cover rate limits, authentication needs, or potential performance implications beyond the depth warning.
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 efficiently structured in three sentences: first states purpose and scope, second describes output format, third provides usage context. Every sentence adds value with zero waste. It's appropriately sized and front-loaded with the core functionality.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (network traversal), no annotations, and no output schema, the description does well but has gaps. It explains what the tool does, when to use it, and output format at a high level, but doesn't detail the exact structure of returned data or potential limitations beyond depth considerations. For a network analysis tool without output schema, more detail on result format would be helpful.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already fully documents both parameters. The description adds marginal value by mentioning 'up to the specified depth' and the traversal concept, but doesn't provide additional syntax or format details beyond what the schema provides. 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.
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: mapping corporate networks via shared directors for UK companies. It specifies the verb ('Map'), resource ('corporate network'), scope ('connected to a UK company via shared directors'), and distinguishes from siblings like get_company (single company) or get_directors (directors of one company).
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 clear context for when to use this tool: 'identifying corporate group structures, related party relationships, and director interlocks.' It doesn't explicitly state when not to use it or name alternatives among siblings, but the context strongly implies this is for network analysis rather than retrieving individual company or director data.
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 full burden and does well by disclosing key behavioral traits: it returns enriched data with derived fields not available from the raw API, specifies the data source (Companies House), and indicates the geographic scope (UK companies). It doesn't mention rate limits, authentication needs, or error behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured in two sentences: the first states the core purpose and scope, the second details the returned data and key differentiators. Every element serves a clear purpose with no wasted words.
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 single-parameter read operation with no output schema, the description provides good completeness by detailing what data is returned and the enrichment aspect. It could be more complete by specifying the exact format of returned data or any limitations, but covers the essential context well.
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% with the parameter well-documented in the schema. The description adds minimal value beyond the schema by mentioning 'UK company' context and the example of Tesco PLC, but doesn't provide additional semantic context about the parameter.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('Get an enriched profile'), target resource ('UK company'), and key differentiators from raw API data. It distinguishes from siblings by focusing on comprehensive company profile data rather than directors, financials, or network relationships.
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 clear context for when to use this tool (to get enriched company profile data) and implies alternatives by mentioning 'raw Companies House API.' However, it doesn't explicitly state when to choose this over sibling tools like get_financials or search_company.
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 key behaviors: the tool parses iXBRL accounts, returns specific financial metrics for current and prior years, includes accounts type and data quality information, and mentions caching for 7 days. This covers data source, output structure, and performance characteristics, though it doesn't detail error handling or rate limits.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with the core purpose, followed by specific details on returned data, accounts type, data quality, and caching. Every sentence adds value without redundancy, making it efficient and well-structured for an AI agent to understand the tool's scope and behavior.
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 tool with no output schema and no annotations, the description provides strong contextual completeness. It details the returned financial fields, accounts type, data quality block, and caching behavior. However, it doesn't specify the output format (e.g., JSON structure) or potential error scenarios, leaving some gaps for an agent to infer.
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 has 100% description coverage, with the company_number parameter well-documented in the schema itself (including pattern and example). The description adds no additional parameter semantics beyond what the schema provides, such as explaining how the company number relates to financial data retrieval. The baseline score of 3 is appropriate given the schema's comprehensive 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 states the tool's purpose with specific verbs ('Get structured financial data') and resources ('UK company'), distinguishing it from siblings like get_company (general info), get_directors (people data), get_network (relationships), and search_company (search functionality). It specifies the data source (iXBRL accounts from Companies House) and the type of financial data returned.
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 clear context for when to use this tool: to retrieve financial data for UK companies from Companies House filings. It implies usage by specifying the data source and company number parameter, but does not explicitly state when not to use it or name alternatives among siblings (e.g., use get_company for non-financial data).
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 describes what the tool returns ('list of matching companies with their company number, status, type, and registered address') and geographical scope ('UK companies'), but doesn't mention rate limits, authentication requirements, error conditions, or pagination behavior. It adequately covers the basic operation but lacks deeper behavioral context.
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 perfectly concise with two sentences that each earn their place. The first sentence states the purpose and scope, while the second provides usage guidance. There's zero wasted text, and the most important information (what the tool does) is front-loaded.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (search operation with 2 parameters), no annotations, and no output schema, the description does a good job covering the essentials. It explains what the tool does, when to use it, what it returns, and geographical scope. However, without an output schema, it could benefit from more detail about the return format structure.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already fully documents both parameters. The description adds no additional parameter semantics beyond what's in the schema descriptions. The baseline score of 3 is appropriate when the schema does all the parameter documentation work.
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 specific action ('Search for UK companies by name'), resource ('companies'), and scope ('UK companies'). It distinguishes this tool from siblings by specifying it's for searching by name rather than retrieving specific company details (get_company), directors (get_directors), financials (get_financials), or network (get_network).
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 guidance on when to use this tool ('Use this first when you only have a company name and need its company number'). It also implies when not to use it (when you already have a company number or need other types of data, suggesting the sibling tools as alternatives).
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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