nordic-financial-mcp

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

No annotations provided, so description carries full burden. Discloses return value structure ('Dict with company name, status...'), but omits error handling (not found scenarios), rate limits, or authentication requirements common in registry APIs.

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?

Efficient Args/Returns structure with zero waste. Four sentences covering purpose, parameter detail, and output format. Front-loaded action verb in first sentence.

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?

Adequate for a single-parameter lookup tool. Output schema exists, yet description helpfully summarizes return content. Minor gap: lacks error case documentation (e.g., invalid orgnr format or non-existent company handling).

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?

Excellent compensation for 0% schema coverage. Adds critical semantics: 'Norwegian organisation number without hyphens' explains format constraint, and example '923609016' clarifies expected pattern—information completely absent from 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?

Specific verb 'Look up' + resource 'Norwegian company' + scope 'Brønnøysund Register (Enhetsregisteret)'. Naming the specific registry distinguishes it from get_market_data and search_filings siblings.

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?

Implies usage via specific registry domain (Enhetsregisteret), but lacks explicit when-to-use guidance or comparison to siblings like search_filings. Agent must infer appropriateness from registry name.

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 disclosure burden. It successfully documents the extraction method ('page by page'), output format ('single string' with 'page separators'), and error handling ('or an error message'). Minor gaps remain regarding operational constraints like file size limits, timeouts, or authentication 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?

Well-structured with logical flow (action → use case → args → returns). Each sentence adds value. The Args/Returns documentation style is clear, though the 'This is useful for...' clause could be more concise. Appropriately front-loaded with the core operation.

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 tool's low complexity (single parameter, simple read operation), the description provides comprehensive coverage of intent, parameters, and return values. Minor improvements would include noting any file size limitations or timeout behaviors for network downloads.

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?

Despite 0% schema description coverage, the description fully compensates by documenting the 'pdf_url' parameter with both semantic meaning ('Direct URL to the PDF file') and a concrete example ('https://example.com/report.pdf'), providing sufficient guidance for correct invocation.

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 specific action ('Download a PDF from a URL and extract all text as a single string, page by page') using distinct verbs and resources. It effectively differentiates from sibling search_filings by noting the tool is for content 'not directly searchable in the main database.'

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?

Provides clear positive guidance on when to use ('read report attachments, press releases, or any PDF content'), implicitly contrasting with database search tools. However, it does not explicitly name the alternative tool (search_filings) or state negative conditions ('do not use for...').

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 of behavioral disclosure. It indicates the tool returns a greeting to confirm server status, but omits details about side effects, rate limits, or what the greeting format looks like.

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?

Two efficient sentences with zero waste. The first establishes purpose, the second explains the return value. Every word earns its place and the description is appropriately sized for a simple utility tool.

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?

While the core behavior is documented and an output schema exists (reducing the need to describe returns), the description is incomplete due to the undocumented 'name' parameter. For a simple 1-parameter tool, this gap prevents a higher score.

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 0% and the description fails to compensate by explaining the 'name' parameter (which customizes the greeting). The parameter's purpose and default value usage are not mentioned in the text.

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 identifies this as a connectivity test with a specific return value (a greeting), distinguishing it from the data-oriented siblings. It does not explicitly contrast when to use this versus the data retrieval alternatives.

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?

No explicit guidance is provided on when to use this tool versus the sibling data retrieval tools, nor are prerequisites mentioned. The phrase 'connectivity test' only vaguely implies usage context without specific direction.

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 provided, so description carries full burden. Discloses data scope (specific companies listed, Nordic countries only), update frequency ('hourly updates Mon–Fri'), and processing behavior ('reranked by relevance'). Returns section documents output structure including metadata fields. Missing rate limits or error conditions.

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

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

Well-structured with clear headers (COMPANY FILINGS, PRESS RELEASES, MACROECONOMIC SUMMARIES, Args, Returns). Front-loads purpose. Length is justified by the need to enumerate specific covered companies and macro indicators. Args section mimics Python docstring for readability.

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?

Comprehensive given 7 parameters with 0% schema coverage and rich output. Covers: database contents (specific tickers, countries), query patterns, output format with metadata fields, and filtering combinations. With output schema present in Returns section, description appropriately completes the picture without redundancy.

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 has 0% description coverage, requiring full compensation. Description excels: provides 5 usage examples for 'query', valid ticker examples, enumerated options with semantic meaning for report_type (6 options) and sector (3 options), country codes mapped to full names, and default/max values for limit. Fully compensates for empty 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?

Opens with specific verb+resource: 'Search the Nordic financial database for company filings, press releases and macroeconomic summaries.' Immediately distinguishes from siblings (get_company_info, get_market_data) by focusing on historical document retrieval rather than current market data or static company info.

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?

Extensive examples under Args (e.g., 'salmon price Q3', 'Norwegian housing market 2024 Q1') and explicit parameter combinations ('Use report_type='macro_summary' and country='NO'...'). Lacks explicit 'when not to use' or sibling alternatives naming, but content enumeration provides clear contextual boundaries.

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