registrum-mcp

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden. It discloses that the tool returns derived fields like company_age_years and accounts.overdue, which is useful context. However, it does not mention any side effects, data freshness, rate limits, or authentication requirements, and being a read operation, it never explicitly confirms non-mutating behavior.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is two sentences, front-loaded with the main action and resource, then efficiently enumerates the returned fields. Every sentence adds value and there is no redundancy or fluff.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a one-parameter lookup tool with no output schema, the description is quite complete: it lists the main output categories and highlights derived fields that add value. It does not specify the response format or error behaviors, but given the low complexity, this is a minor gap.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100% for the single parameter, with a clear description including format and example. The tool description only repeats 'Companies House number' without adding new meaning beyond the schema, so the parameter semantics are adequately covered by the schema itself.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool retrieves an enriched profile for a UK company using a Companies House number. This is a specific verb+resource+scope, and the mention of 'enriched profile' distinguishes it from sibling tools like get_directors or get_financials.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies this is the tool for a comprehensive company profile, but it does not explicitly state when to use it versus sibling tools or mention any exclusions. It notes that derived fields are 'not available from the raw Companies House API', which hints at uniqueness but stops short of naming alternatives or providing when-to-use guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations present, the description carries the full burden of behavioral disclosure. It does this thoroughly: it clarifies that 'pending' is not a failure and must not be reported as one, defines 'overdue,' notes the Pro plan requirement and 24-hour caching, and explains that unverified officers can block filings. This goes well beyond basic safety expectations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is compact but packs in essential information: purpose, compliance context, exact return fields, and a critical warning about the meaning of 'pending.' The emphasis on 'IMPORTANT' ensures the agent does not misinterpret the status. Every sentence earns its place with no filler.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Even without an output schema, the description enumerates the exact returned counts (directors_total, directors_verified, directors_pending, directors_overdue, etc.) and the unverified_persons structure. It also covers caching, plan requirements, and the regulatory backdrop, making the tool fully self-explanatory for an AI agent.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% for the single parameter company_number, including a pattern, a concrete example, and zero-padding instructions. The description does not repeat or extend the parameter documentation, but given the schema's completeness, a baseline of 3 is appropriate. No additional parameter-level semantic information is needed.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

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 a specific verb and resource: 'Check a UK company's ECCTA identity-verification status.' It also enumerates what the status includes (verified, pending, overdue) and distinguishes it from sibling tools by focusing on compliance rather than general company data, directors, or PSC information.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides strong contextual guidance by explaining the ECCTA legal requirement and the enforcement date, which implicitly tells users when they would need this tool. It does not explicitly contrast with alternatives like get_directors or get_psc, but the unique compliance angle and the detail about enforcement make the use case clear. A clear exclusion or explicit alternative would bump this to 5.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It does not mention that the tool is read-only, any permission/authentication requirements, rate limits, or potential data limitations. The only behavioral hint is the scoping to 'current and past' and conditional resignation dates, which is more about data variability than operational behavior.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is two sentences, front-loaded with the verb and resource, followed by a concise list of output fields and a useful summary. Every phrase adds value and there is no filler or redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With no output schema, the description compensates by explicitly naming all major return fields, including conditional resignation dates and cross-company director roles. It does not cover edge cases like errors or empty results, but for a single-parameter read tool, it is largely complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema already documents the single parameter company_number with a pattern and example, achieving 100% coverage. The description adds no additional semantic meaning about the parameter itself, so it stays at the baseline of 3.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly specifies the action ('Get') and resource ('current and past directors for a UK company'), and enumerates the returned fields, leaving no ambiguity about the tool's function. However, it does not explicitly distinguish itself from sibling tools like get_company or get_psc, so it misses the 5-level differentiation.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage when a complete director history is needed ('This gives you a full picture of a director's corporate history in one call'), but it does not provide explicit when-to-use guidance or mention alternative tools for other company data. No exclusions or alternatives are stated.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries full burden and delivers: it explains the data source (iXBRL accounts), details the returned fields including a data_quality block, and discloses a 7-day cache. This provides thorough behavioral context beyond a simple 'get'.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured with a clear purpose first, then a detailed list of returned data, and finally a behavioral note about caching. Every sentence adds value without redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the single parameter with full schema coverage and the absence of an output schema, the description thoroughly enumerates all return values, the data source, and caching behavior, making it complete for operational use.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema description for company_number is 100% covered, including format and example, so the description adds no extra parameter meaning beyond what the schema already states. Baseline of 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly specifies the verb 'Get' and the resource 'structured financial data for a UK company', and distinguishes it from siblings by focusing on financials parsed from iXBRL accounts. It is unambiguous and directly reflects the tool's function.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description makes it clear that this tool is for retrieving financial data from Companies House filings. It implies the appropriate context without naming alternatives or exclusions, which is acceptable given the obvious distinction from sibling tools.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the burden. It discloses the read-only nature ('Returns all companies...') and details the output fields, which is transparent. It does not warn about potentially large result sets for depth 2 (though the schema does), nor does it explicitly declare itself read-only, so it falls short of fully comprehensive behavioral disclosure.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is three concise sentences that front-load the core purpose, then describe the return contents and use cases. Every sentence contributes meaning without redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given no output schema and no annotations, the description compensates by specifying return fields and the domain (UK companies, shared directors). It covers the main aspects an agent needs: what it does, what it returns, and when to use it. The only gap is a lack of explicit warnings about result size at larger depths, but the schema addresses that.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so the baseline is 3. The description adds context linking 'depth' to 'up to the specified depth' and 'shared directors' to 'shared board members', but these are already conveyed in the schema. No additional parameter-level meaning is provided beyond the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the verb 'Map' and the resource 'corporate network connected to a UK company via shared directors', distinguishing it from sibling tools like get_directors or get_company. It also specifies the output contents (company name, number, status, shared directors), making the tool's function unmistakable.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides explicit use cases ('useful for identifying corporate group structures, related party relationships, and director interlocks') which guides when to apply this tool. However, it does not explicitly name alternative tools for different scenarios or state when not to use it, so it lacks the exclusionary guidance typical of a 5.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden and succeeds: it discloses return entity types, ownership thresholds, decoded control types in plain English, company numbers for corporate PSCs, exemption detection for listed PLCs, and 24-hour caching. This is comprehensive behavioral disclosure.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is four sentences, each earning its place: core purpose, entity types and criteria, decoding and chain-traversal notes, and caching. It is front-loaded and free of fluff or repetition.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Despite lacking an output schema, the description covers return content, formatting (decoded control types), special cases (exemptions), and caching. For a single-parameter read tool, this is complete enough to set correct expectations.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The only parameter (company_number) is fully described in the schema with pattern, example, and zero-padding rule, giving 100% schema coverage. The description adds no additional parameter-specific semantics, so the baseline score of 3 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states it retrieves the PSC register for a UK company, enumerates the types of entities returned (individuals, corporates, legal persons), and specifies the ownership/control criteria. It distinguishes from sibling get_psc_chain by highlighting decoded control types and chain-traversal data, though it doesn't explicitly compare.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides clear context on what the tool returns and mentions a caching behavior, which helps the agent gauge typical usage. However, it doesn't explicitly state when to prefer this tool over siblings like get_psc_chain, nor does it provide exclusion criteria.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, description proactively discloses recursion behavior, return tree structure, terminal_reason enum variants, and chain_metadata fields (resolved count and API credit cost). This goes well beyond minimal expectations, though it could mention lack of side effects or auth requirements.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Four sentences, each adding distinct value: main function, tree output, terminal reasons, metadata, and use cases. Slightly longer than strictly necessary but no repetition or fluff. Front-loaded with the core purpose.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

No output schema exists, yet the description fully describes the return shape (tree with terminal reasons and metadata). Combined with clear use cases and 100% schema coverage for parameters, this is complete for a 2-parameter tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, and the schema descriptions already explain both parameters (max_depth and company_number) with examples and cost implications. The description adds no additional parameter semantics beyond what the schema provides, so baseline 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

Description uses specific verb 'Trace' with clear resource ('full ownership chain for a UK company') and explicitly distinguishes from sibling get_psc by stating 'beyond the immediate PSC layer.'

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Gives explicit use cases ('KYB checks, AML screening, or any task requiring beneficial ownership beyond the immediate PSC layer') and implicitly contrasts with the immediate PSC tool. Lacks explicit 'when not to use' or named alternative, but context is strong.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description must carry the behavioral disclosure burden. It does state that the tool returns a list of matching companies with specific fields, giving some behavioral context. However, it omits details such as whether the operation is read-only, any potential side effects, ordering, or error behavior. The description adds some value but not rich behavioral transparency.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is two sentences, front-loaded with the core purpose and followed by a usage directive. Every word earns its place, with no redundancy or filler.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

This is a simple two-parameter search tool with no output schema. The description adequately explains what the tool does and what it returns, covering the essential information an agent needs. Slight deduction because the lack of annotations means some behavioral details (e.g., rate limits, authentication needs) are not addressed, but otherwise it is complete for its simplicity.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, with both 'query' and 'limit' having meaningful descriptions ('Company name or keywords to search for' and 'Maximum number of results to return (default 10)'). The tool description does not add any additional parameter semantics beyond the schema, so a baseline score of 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific verb ('Search'), resource ('UK companies'), and the desired output (company number, status, type, registered address). It also clearly distinguishes this tool from siblings by noting it should be used first when only a company name is available, 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.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly says 'Use this first when you only have a company name and need its company number,' which provides a clear when-to-use directive and implicitly points to alternatives like get_company when a company number is already known. This is strong usage guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.