EUTenders Procurement

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 the HTTP method (GET), a clear read-only indication, and the price ($0.05). It also outlines what data will be returned. It does not mention auth/rate limits, but the GET method and cost disclosure add meaningful context beyond the schema.

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 a single, well-structured sentence that packs endpoint, price, and content summary with zero redundant words. It is front-loaded with the most critical information.

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 and annotations, the description sufficiently conveys what the tool returns (award history, supplier concentration, recurrence). It omits response format and potential error cases, but for a simple GET with clear data points, it is complete enough. The payment_token is explained in the schema, so that gap is covered.

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% with detailed descriptions for both parameters (id and payment_token). The description adds no parameter-specific information beyond what the schema already provides, so the 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 identifies the tool as returning a buyer profile with specific data: award history, supplier concentration, and recurrence. It distinguishes from sibling tools like get_company_awards by focusing on buyer-level metrics and unique data points (concentration, recurrence), and from search_tenders by targeting a single buyer id.

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 buyer-specific history is needed but provides no explicit guidance on when to use this over alternatives like get_company_awards or get_company. No exclusions or comparison to sibling tools is mentioned, leaving the agent to infer context.

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 does specify the HTTP method (GET) and cost ($0.05), and enumerates the returned data, which implies a read-only operation. However, it does not explicitly state side-effect-free behavior, auth requirements, or potential error states, leaving some transparency gaps.

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 a single, dense sentence that packs endpoint, cost, resource, and return fields with no wasted words. It is front-loaded with the ID parameter and immediately followed by useful output details.

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 simple GET-by-id tool with two params and no output schema, the description is well-rounded: it lists the profile fields and aggregate stats returned. It does not describe the exact output structure, but the absence of an output schema makes the field-level summary sufficient for invocation.

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 input schema has 100% description coverage for both parameters (id and payment_token), so the baseline is 3. The description reinforces that 'id' is the company identifier but adds no additional meaning beyond the schema descriptions.

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 a company profile by id and lists specific fields (name, country, NIF, aliases, identifiers, stats), distinguishing it from sibling tools like get_company_awards and get_company_opportunities. The verb 'GET' and endpoint pattern reinforce the resource being accessed.

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 phrase 'Company profile by id' provides clear context for when to use this tool: when you need a company's profile and aggregate statistics. It does not explicitly name alternative tools or exclusion criteria, but the description implies that award-specific or opportunity-specific data should be fetched via 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, the description carries the transparency burden. It discloses the HTTP method (GET), the cost ($0.05), pagination, and the kind of data returned. This provides solid behavioral context, though it does not cover 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.

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

The description is a single, front-loaded sentence that includes the endpoint, cost, and a concise list of included data. Every word 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?

The description covers purpose, method, cost, pagination, and content, which is reasonably complete for a simple read-only list endpoint. It lacks explicit return-shape details, but without an output schema, the included content list provides enough context for an agent to select and invoke the tool correctly.

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 50%, so the description must compensate, but it only adds general context ('Paginated') rather than explaining individual parameters. The schema already documents id and payment_token, while page and size are self-evident from the pagination mention, so the description adds marginal value.

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 names the resource ('award history for a company') and the verb ('GET'), and specifies the core contents ('dates, lots, values, tender + buyer context'). This distinguishes it from sibling tools like get_company or get_buyer_history.

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 phrase 'Paginated award history for a company' implies when to use this tool, but there is no explicit guidance about when not to use it or which sibling tool to prefer. No alternatives are mentioned, so the usage context is inferred rather than 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 provides some behavioral context: it notes the endpoint is a GET (suggesting read-only), mentions a 'deterministic similarity score' with an explanation field, and states the cost ($0.10). However, it does not disclose pagination behavior, authentication requirements, or what happens if the company has no matching opportunities. The description adds meaningful detail but is 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.

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

The description is a single, dense sentence that leads with the endpoint and cost, then explains the data returned and the score explanation. No filler words; every part adds value, making it highly efficient.

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?

The tool has no output schema and no annotations, so the description is the primary source of behavior. It covers the core purpose and the score explanation, but lacks details on response structure, pagination limits, and error scenarios. Given the tool's moderate complexity, it is minimally adequate but leaves gaps.

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 50%, covering id and payment_token, but page and size lack descriptions. The tool description does not mention any parameter semantics, failing to compensate for the uncovered parameters. The description only hints at the company id via the URL path, so the agent gets no additional guidance for pagination or token handling 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 tool retrieves 'active/recent tenders matching a company's historical CPV/buyer profile' with a 'deterministic similarity score'. The verb 'GET' and resource '/v1/companies/:id/opportunities' are explicit, and the mention of CPV/buyer profile distinguishes it from sibling tools like get_company_awards or get_buyer_history.

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 you need to find tenders similar to a company's past activity, but it does not explicitly state when to use this instead of siblings or provide exclusions. There's no direct comparison to alternatives like get_company_awards or search_tenders, leaving the decision partially to the agent.

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 for behavioral disclosure. It adds the fact that the tool is 'always free' and shows the endpoint pricing, but does not disclose response format, authentication requirements, error behavior, or how the payment_token affects the operation.

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 a single, concise sentence that front-loads the HTTP method, endpoint, cost, and the tool's purpose. Every word earns its place, with no fluff 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?

For a simple read-only price lookup tool, the description covers the core purpose and cost, but it lacks details about the output structure and the role of the payment_token. With no output schema and no annotations, the description could be more complete about what the agent should expect in the response.

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 provides 100% coverage with a detailed description of the optional payment_token, so the description does not need to repeat parameter details. It adds no additional semantic value beyond what the schema already conveys, which aligns with the baseline for high schema coverage.

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 function: providing a machine-readable price ladder for all endpoints/tools and the payment flow. It uses specific resource terminology ('price ladder') and scope ('all endpoints/tools plus the payment flow'), which distinguishes it from sibling tools focused on companies, tenders, and renewals.

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 notes 'Always free' which gives context about when this tool can be used without cost concerns. However, it does not explicitly state when to use this tool versus alternatives, nor does it mention exclusions or prerequisites beyond the optional payment_token in the schema.

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?

The description discloses the HTTP GET method, the $0.25 price, and key filter constraints (window_months max 36, min_confidence levels). However, with no annotations, the description carries the full burden and omits details like payment token workflow, rate limits, and response shape. Valuable context is added, but significant behavioral gaps remain.

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 entire description is one compact sentence that front-loads the endpoint and price, then uses a semicolon-separated filter list. Every phrase earns its place, with no filler or repetition of schema details.

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 7-parameter paid tool with no output schema, the description covers the main filters and cost, but it does not describe the return format or pagination behavior, and the payment token requirement is only present in the schema. The tool is likely usable with the given information, but important contextual details are missing.

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 description adds meaningful semantics beyond the schema: it clarifies that cpv is a prefix, lists buyer, and highlights window_months/min_confidence defaults and ranges. Although page/size/payment_token are not explained in the description, payment_token is already described in the schema and page/size are conventional, so the description sufficiently compensates for the low schema description coverage.

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 function: it returns 'Forecast signals for likely re-tenders' and specifies contracts/frameworks approaching renewal. It differentiates from siblings like get_company_opportunities or search_tenders by focusing specifically on renewals and forecast signals.

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 usage context is clear from the phrase 'likely re-tenders: contracts/frameworks approaching renewal.' Filter options and defaults are listed, giving the agent enough context for typical use. However, no explicit when-not-to-use instructions or alternative tool mentions are provided.

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 the full burden. It discloses the HTTP method (GET), the cost ($0.02), and the comprehensive return fields, implying a read-only operation. It doesn't describe error behavior or payment token requirements, but for a simple lookup tool this is reasonably transparent.

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 a single, well-structured sentence that front-loads the HTTP verb, endpoint, and price, followed by a concise list of returned data. There is no wasted wording.

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, the description does a good job of summarizing the response content (buyer, CPVs, deadline, estimated value, awards/lots, provenance). It lacks details on error handling or authentication, but for a get-by-id tool with 2 parameters, the coverage is adequate.

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 does not add meaning beyond the schema; it only refers to the id, and the schema already provides a description for both parameters. No additional value is provided.

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 full tender detail by id, listing specific content (buyer, CPVs, deadline, estimated value, awards/lots with winners, provenance). This is a specific verb+resource+scope and distinguishes it from sibling tools like get_company or search_tenders.

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 use when you need detailed tender information by id, and the parameter schema adds context that the id comes from search results. However, it does not explicitly state when not to use it or mention alternatives, so it lacks exclusions.

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. It discloses the HTTP method (GET), cost ($0.02), scope, and return format (compact rows). It does not mention pagination or payment prerequisites, but the schema partially covers these, so the disclosure is adequate.

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 a single, information-dense sentence that packs in the endpoint, cost, scope, filters, and return format with no wasted words. It is well front-loaded and easy to parse.

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 has 11 parameters, no output schema, and no annotations, the description covers the core aspects: what is searched, the available filters, and the compact return format. It could explicitly mention pagination or the payment token requirement, but the schema parameters imply these, making it sufficiently 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?

Schema description coverage is only about 45%, so the description compensates by explaining key parameters: q is full-text, cpv is a prefix, region uses NUTS codes, from/to are YYYY-MM-DD, and type lists the three enum values. This adds meaning beyond the raw 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 opens with the endpoint and price, then clearly states 'Search Spanish public-sector IT/software/cyber procurement: awards, tenders and contracts.' This specifies the exact resource and action, and the mention of multiple procurement types distinguishes it from the sibling tools, which are focused lookups for specific entities.

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?

'Returns compact rows with ids for the other tools' explicitly indicates that this tool is the entry point for discovery and that other tools should be used for full details. However, it does not explicitly name alternatives or provide exclusions, so it falls short of a full 5.

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