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

With no annotations, the description carries the behavioral transparency burden. It clearly indicates a read-only operation ('Fetch') and sets expectations about the record contents. It does not discuss errors, auth, or pagination, but for a simple single-record retrieval these are not significant 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?

A single, focused sentence that states the action, target, identifier scheme, and expected contents with no redundant wording. All important information is front-loaded.

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-project fetch with fully documented parameters, the description is largely complete. It lists the main output areas and makes the input identifier clear. It could be slightly more explicit about when not to use it versus search_projects, but that is minor given the sibling names.

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 already fully documents both parameters with descriptions and a default, so the baseline is 3. The description adds little beyond what the schema provides, though mentioning 'participating organisations' aligns with the include_participants parameter.

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 ('Fetch') and clearly identifies the resource: one EU-funded project identified by grant agreement number. It also enumerates key content areas, making it distinct from search_projects, which implies finding projects, and list_erc_schemes.

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 says the tool fetches by grant agreement number, making it clear this is for retrieving a known project rather than searching. It does not explicitly name sibling alternatives or exclusion conditions, but the usage context is clear enough for an agent. A fully explicit routing statement would earn 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?

No annotations are provided, and the verb 'List' plus 'present in CORDIS with their grant counts' conveys a read-only enumeration and the shape of the returned data. It could add explicit no-side-effect/no-pagination statements, but for a zero-param controlled-list tool this is sufficient 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?

One tight sentence front-loads the action and resource, then adds the purpose clause. Every part earns its place; no fluff or repetition of the tool name.

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 no-parameter, no-output-schema list tool, the description fully covers what the agent needs: what is returned (ERC schemes and grant counts) and why to call it (to populate the funding_scheme filter). No critical 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?

The input schema has zero parameters and 100% coverage, so the parameter baseline is 4. There are no parameter details to add; the description instead explains how the output will be consumed, which is useful context.

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'), defines the exact resource (ERC funding schemes present in CORDIS), and includes the output content (grant counts). It clearly distinguishes itself from sibling search_projects and get_project by framing the result as the filter vocabulary for search_projects.

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 tells the agent when to use the tool: to obtain valid values for the funding_scheme filter in search_projects. It does not explicitly spell out when-not-to-use or list alternatives, but the intended workflow is unambiguous.

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 behavioral burden. It usefully discloses the return structure {total, items, attribution}, but does not describe search semantics beyond keyword matching, default behavior, pagination, or slowness of abstract search. The description adds some value but leaves important runtime behavior to 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?

A single, information-dense sentence that front-loads the action and resource, lists filters economically, and closes with the return shape. There is no fluff or repetition of schema content.

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 9 parameters and no output schema, the description gives the essential orientation: scope, filters, and the top-level response fields. It could be more complete by mentioning the optionality of query or directing an agent to get_project for single-project lookups, but the core calling context is present.

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%, so the schema already documents all parameters in detail. The description groups filters (ERC, funding scheme, country, minimum funding, start date) in a readable way but adds no new syntactic or semantic detail beyond the schema, 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?

The description states a specific action ('Search'), a specific resource ('EU-funded research projects'), and the programme scope (Horizon Europe, H2020, FP7). It also names the key filters and the return shape, distinguishing it from the siblings get_project and list_erc_schemes.

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 clearly implies when to use the tool: when you want to search across multiple projects by keyword or filter. However, it does not explicitly contrast this with get_project for retrieving a single project or list_erc_schemes for scheme metadata, so the guidance 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.