euacc-mcp

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

No annotations are provided, so the description must fully disclose behavior. It indicates a read operation ('Get') and describes the input, but does not state whether any side effects occur, what the response format is, or any required permissions. This is adequate but lacks depth for a tool with no annotation backup.

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 action and purpose, and contains no redundant information. Every word contributes value.

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 simplicity (one required parameter, no output schema, no annotations), the description covers the essential information: what it does, when to use it, and what input to provide. It could mention the output format or that it returns a recommendation, but overall it's sufficiently complete for the tool's complexity.

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 one parameter 'name' described as 'comparison slug or title'. The description echoes 'Pass a slug or title' without adding new semantic meaning. Baseline 3 is appropriate as the schema already documents the parameter fully.

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 'Get EUACC's side-by-side comparison of two EU funding options with a recommendation', specifying the verb 'Get', the resource 'comparison', and providing examples like 'EIC Accelerator vs EIC Pathfinder'. This distinguishes it from sibling tools like 'get_guide' or 'get_programme'.

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 for 'X vs Y' funding questions', giving clear context for when to use the tool. However, it does not specify when not to use it or mention alternatives among siblings, missing a small opportunity for exclusion 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?

No annotations are provided, so the description carries the full burden. It describes the tool as retrieving data (read-only implied) but does not explicitly state it is non-destructive, nor does it mention any limitations like data freshness or 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.

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

The description is two sentences, front-loading the purpose and providing immediate usage guidance. No redundant or extraneous text.

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 gives a useful summary of the returned data (national grants, startup ecosystem, EU funding). For a simple lookup tool with one parameter, it is sufficiently complete, though it could be more specific about response format.

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 covers the single parameter (name) with a description 'country ISO code or name'. The tool description adds concrete examples (DE, Germany, FR, France), which aids correct invocation 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 a country funding profile, including national grants, startup ecosystem, and EU funding details. It distinguishes from sibling tools like get_programme or get_sector by specifying the country-level scope.

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 usage patterns ('Use for...') and example queries. It does not explicitly mention when not to use or suggest alternatives, but the context is clear enough for an agent to decide.

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. It describes the core behavior (define term) but does not disclose what happens for unknown terms or any other behavioral traits. Minimal but adequate for a simple lookup.

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 sentences, zero wasted words, front-loaded with purpose and examples. Efficient and direct.

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 glossary tool with one parameter and no output schema, the description is sufficient. Lacks mention of response format or error handling, but the context (definitions in plain English) is implied.

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% (only one parameter 'term' described as 'the term to define'). The description adds 'Pass the term' which is redundant. No additional semantic value 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 defines an EU-funding term in plain English, provides examples (TRL, consortium, etc.), and distinguishes it from sibling tools (get_comparison, get_guide, etc.) which serve different purposes.

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?

Explicitly says 'Use when the user hits jargon', giving a clear context of use. Does not explicitly name alternatives, but the sibling list provides differentiation.

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 must fully disclose behavior. It only states what the tool does and how to pass a parameter, but does not describe return format, error cases, or whether it is destructive. This is insufficient for a tool with no annotation support.

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, no fluff. First sentence defines purpose, second sentence gives usage guidance. Every sentence earns its place.

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 single-parameter retrieval tool with no output schema, the description adequately covers purpose and usage. It could mention return structure or example response, but given the simplicity, it is 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 coverage is 100% with a single parameter 'name' described as 'guide slug or title'. The description adds a concrete example 'eic-accelerator-guide', which helps the agent understand valid values beyond the schema, earning a score above 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 states it retrieves a full EUACC how-to guide, using specific examples like 'writing an EIC Accelerator application'. The verb 'Get' and resource 'guide' are explicit, and it distinguishes from sibling tools such as get_comparison and get_glossary_term.

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 states when to use it: 'for 'how do I apply for X' or 'guide to Y''. It provides clear usage context but does not mention when not to use or list alternatives, missing some 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?

No annotations are provided, so the description bears full burden. It implies a read operation ('Get full details') and lists return fields, but does not explicitly state that the tool is non-destructive or whether authentication is required. The description is adequate but could be more explicit about its safety and side-effect profile.

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: the first explains the purpose, the second gives parameter guidance. Every sentence earns its place; no wasted words. Front-loaded with the action and key 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?

There is no output schema, so the description must explain return values. It lists the main fields (amount, funding type, TRL range, eligibility, deadlines, how to apply), which is sufficient for the tool's low complexity. It could also mention the response format (e.g., JSON object) but 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 input schema has 100% coverage with a single parameter 'name' described as 'programme slug or name'. The description adds value by providing example values ('eic-accelerator', 'horizon-cluster4') and clarifying that either a slug or name works, which goes beyond the schema description.

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 'Get full details on an EU funding programme' and lists specific attributes (amount, funding type, TRL range, etc.), making the tool's purpose explicit and distinguishing it from sibling tools like 'list_programmes' which likely return summary lists.

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 instructs to 'Pass a slug or name' and provides concrete examples ('eic-accelerator', 'horizon-cluster4'), giving clear guidance on how to call the tool. However, it does not explicitly mention when to use this tool versus siblings (e.g., when to use 'get_comparison' or 'list_programmes').

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. It indicates a read operation (retrieving a deep-dive) but does not disclose limitations, prerequisites, or error behaviors. For a simple get tool, this is adequate but not exceptional.

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 concise, consisting of two sentences with no wasted words. It is front-loaded with the main purpose, followed by usage examples, making it easy to parse quickly.

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 tool with one parameter and no output schema, the description sufficiently covers what the tool returns (deep-dive on funding landscape, programmes, stats, ecosystem). It does not detail output format, but this is acceptable given no output schema.

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 describes the single parameter 'name' as a slug or name, and the description adds concrete examples ('artificial-intelligence', 'cleantech', 'health'). Since schema coverage is 100%, the description adds meaningful context 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 a deep-dive on a sector's EU funding landscape, including programmes, stats, and ecosystem. It uses specific verbs and resources, and distinguishes itself from sibling tools like get_country and get_programme by focusing on sectors.

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 advises when to use this tool ('for 'what EU funding is there for <sector>''), providing clear context. However, it does not mention when not to use it or suggest alternative tools among siblings, which would strengthen the score.

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. It states a read-only action ('List'), but does not disclose potential behaviors like authentication needs, rate limits, or whether the list is cached. For a simple tool, this is adequate but not comprehensive.

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 sentence of 14 words, extremely concise and front-loaded with the key action and outcome. Every word adds value, with no 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 tool's simplicity (no parameters, no output schema), the description fully covers its functionality. It explains the returned data (name + code) and its intended use with get_country. No additional information is necessary for correct 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 tool has zero parameters, and schema coverage is 100% (trivially). Baseline is 3. The description does not add parameter information, but none is needed. It mentions output fields (name + code), not input semantics.

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 uses a specific verb 'list', identifies the resource 'countries with funding profiles', and specifies the output 'name + code'. It also explicitly states the tool's purpose: 'for use with get_country', which distinguishes it from sibling tools like get_country or list_programmes.

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 by indicating this tool is a prerequisite for get_country. However, it does not explicitly state when to use alternatives or when not to use it, but for a zero-parameter enumeration tool, the guidance is sufficient.

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, but description adequately covers behavior: it lists all programmes. Could mention ordering or formatting, but not required for a simple list.

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 concise sentences, front-loaded with purpose and immediate follow-up action. No wasted words.

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 parameters, simple output, and absence of output schema, description covers purpose and usage. Could mention if list is ordered, but not critical.

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?

No parameters, schema coverage 100%. Description adds no parameter info, but baseline 4 is appropriate because none 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?

Clearly states it lists EU funding programmes with name and slug, and distinguishes from sibling get_programme by indicating next step.

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?

Explicitly says when to use: when user asks 'what programmes are there' or to pick the right programme. Provides clear 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?

No annotations are provided, so the description carries the full burden. It explains the tool lists sectors with name+slug, implying a read-only, idempotent operation. For a simple list tool, this is clear, though it could mention that it returns all sectors or that the list is static.

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 sentence with no extra words. It efficiently conveys the purpose, content, and usage context. Every part earns its place.

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 parameters, no output schema, and no annotations, the description is concise but covers the essential: what the tool returns and its role. It could more explicitly state the output structure (e.g., array of objects), but it is sufficient for a list-all 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?

There are zero parameters, so the baseline is 4. The description adds value beyond the schema by specifying what the list contains (name and slug) and its purpose (for get_sector). This fully compensates for lack of parameter details.

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 'list' and the resource 'sectors', specifies the content ('name + slug'), and distinguishes it from the sibling tool 'get_sector' by noting it is for use with that tool. No ambiguity.

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 by stating the tool lists sectors for use with 'get_sector', implying a workflow. It does not explicitly state when not to use it or list alternatives, but the context is sufficient for a list-all tool with no direct siblings.

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 exist, so the description must provide behavioral context. It mentions 'real companies with verified websites and their EU funding', indicating data quality. However, it does not disclose limitations such as pagination, rate limits, or data freshness. For a search tool, this is adequate but not comprehensive.

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 sentences, front-loaded with the purpose and use cases. Every word contributes value, no 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?

With 4 parameters, no schema descriptions, and no output schema, the description provides a high-level overview but lacks details on return format, pagination, or parameter constraints. Adequate for a simple search tool but not fully 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 0%, so the description must compensate. It names three parameters (sector, country, keyword implicitly as 'query') but omits 'limit'. It adds meaning beyond the schema by explaining the search scope, but not all parameters are explicitly covered.

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 searches 'EUACC's directory of EU-funded startups & SMEs' by sector, country, or keyword. The verb 'Search' and specific resource (directory of EU-funded companies) are provided, and it is distinct from sibling tools that search other entities like incubators or consortium partners.

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?

Explicitly says 'Use for 'who's been funded in <sector>/<country>' or competitor/landscape scans.' This guides the agent on when to invoke this tool versus alternatives like search_incubators or search_consortium_partners.

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 must disclose behavior. It mentions ranking by track record, but does not state that it is a read-only operation, any authentication requirements, or rate limits. The description is adequate but not comprehensive.

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 efficient sentences with no wasted words. It front-loads the purpose and criteria, then adds the data source.

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 adequately indicates output fields (projects won, times as coordinator, EC funding). It is mostly complete for a search tool, though missing pagination or default limit details.

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 low (33%) and the description only adds meaning for domain, country, and type, leaving sme, limit, and coordinatorsOnly unexplained. The description does not fully compensate for the missing 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 finds Horizon Europe consortium partners by science domain, country, and type, ranked by track record. It distinguishes from sibling search tools (search_companies, search_incubators) by specifying 'consortium partners' and the Horizon Europe focus.

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 for finding consortium partners but does not explicitly state when to use it versus alternatives like search_companies or when not to use it. No exclusions or alternative guidance 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?

No annotations are provided, so the description must disclose all behavioral traits. It only gives high-level purpose without mentioning permissions, pagination, idempotency, or return format. The description adds minimal behavioral context beyond the obvious.

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 sentences with essential information, front-loaded with the resource and offerings. No unnecessary words.

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 complexity (4 parameters, no output schema, no annotations), the description is adequate to convey the tool's purpose and scope but lacks details on output format, pagination, and parameter usage for query and limit.

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% (only country and technology have descriptions). The description adds value by showing how to use country and technology (e.g., country use case), but does not explain the query or limit parameters. It also mentions 'sector' which is not a parameter, causing slight confusion.

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 searches for European Digital Innovation Hubs and incubators, with specific offerings. It distinguishes itself from sibling search tools like search_companies or search_open_calls by focusing on incubators/hubs.

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 a concrete use case example ('where can I get support / test my tech in <country>'), giving clear context for when to use. However, it does not mention when not to use or suggest alternatives among 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?

No annotations provided, so description carries full burden. It discloses data freshness ('Refreshed daily') and source, but does not mention authorization, rate limits, or whether it's read-only. It partially covers behavioral traits but lacks depth.

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 sentences, no fluff. Every sentence is informative: first sentence defines the tool, second provides usage and freshness. Front-loaded with key info.

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, missing details on return format or pagination. But description adequately covers purpose, scope, and data source for most use cases. Could be more complete with result structure.

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 67% (2 of 3 parameters have descriptions). The description adds context via examples (e.g., 'battery', 'AI') but does not elaborate on the `limit` parameter. Overall, schema does most of the work, and 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?

Description clearly states verb 'Search' and resource 'live, open EU funding calls with real deadlines'. It lists specific programmes and provides example use cases. It differentiates from sibling tools like search_companies or search_incubators by focusing on EU funding calls.

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?

Description gives explicit use cases ('what EU calls are open for X' or 'grants closing soon') and notes daily refresh. It implicitly differentiates from siblings by domain, but lacks explicit 'when not to use' or alternative tool names.

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 burden. It discloses the database size (1,200+ funds) and filter criteria (sector, stage, country), but does not detail behavioral aspects like pagination, default ordering, or result structure. Adequate but not rich.

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 with no wasted words. It is concise, though it sacrifices some completeness for brevity.

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 search tool with four parameters and no output schema, the description is too sparse. It does not explain the return format, pagination, or how to combine parameters. The mention of 'stage' without a parameter adds confusion.

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%, so the description must compensate. It mentions sector, stage, and country as filters, but 'stage' is not present in the schema. It omits 'query' and 'limit'. This mismatch and incompleteness reduce clarity.

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 searches for European VC funds by sector, stage, and country, targeting founders. It is specific (verb+resource+scope) and distinguishes itself from sibling tools like search_companies or search_incubators.

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: 'for founders raising alongside or instead of grants.' This implies when to use, but it does not explicitly state when not to use or compare with alternatives.

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