research-intel

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

No annotations are provided, so the description must fully disclose behavioral traits. It only states the output type (new grants and trials) but omits critical details like whether results are paginated, how 'new' is defined (e.g., by creation date), any rate limits, or side effects. This leaves significant ambiguity for an AI agent.

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 very concise (one sentence plus audience note) and front-loaded with the core action. However, given 13 parameters, it could be slightly more detailed without losing conciseness.

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 13 optional parameters and no output schema. The description fails to mention output format, ordering, limits, pagination, or how 'new' is determined. Compared to sibling 'whats_changed_since', it lacks clarity on what differentiates this digest from other change-tracking tools.

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 77% of parameters with descriptions, so the baseline is 3. The tool description does not add any parameter-specific context beyond what the schema provides, missing an opportunity to highlight key filters like 'kind' or 'days'.

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 what the tool does: it returns 'New grants + new trials' filtered over a number of days, and specifies the use case as 'weekly-digest payload' for BD teams. This differentiates it from sibling tools like 'search_records' (general search) and 'new_funding' (likely broader).

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 context by stating 'Best for BD teams' and 'weekly-digest payload', implying it is used for periodic reporting. However, it does not explicitly specify when not to use this tool or mention any alternative tools among the 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 are provided, so the description must fully disclose behavior. It mentions 'recent' events but does not specify the time range, output structure, pagination, or any potential side effects. This leaves the agent with incomplete understanding of the tool's 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 and directly conveys purpose and usage context. It is efficient with no redundant information, though the second sentence could be slightly more specific.

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 adequately covers the tool's purpose and how to use it for the two parameters, but it omits details about return format, error conditions, and the definition of 'recent.' Given no output schema, more completeness is warranted.

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 schema already documents both parameters. The description restates the entity types but adds no additional semantic meaning beyond the schema's descriptions. 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?

Description clearly states the tool returns 'Recent funding/trial events' for a specific entity. It provides concrete use cases ('enrich an account or check a competitor') and specifies the four entity types. While it distinguishes from siblings in intent, it does not explicitly compare to sibling tools like delta_digest or new_funding.

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 suggests when to use ('enrich an account or check a competitor') but lacks explicit guidance on when not to use it or alternatives among siblings. The context is implied rather than explicit.

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 carry the full burden. It correctly indicates a read operation (returns data) but does not disclose any behavioral traits such as rate limits, authorization needs, or pagination behavior, which is a notable gap.

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 exceptionally concise with two sentences. The first sentence front-loads the core purpose and value proposition ('highest-intent buying signal'), and the second lists filters efficiently. 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 13 parameters and no output schema, the description leaves gaps: it doesn't explain the return structure, pagination, or how results are ordered. For a tool that returns many records, this is a significant omission.

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 77%, already providing decent parameter documentation. The description adds context about filtering by 'institution, PI, sponsor, topic, state, IC, phase' but does not significantly enhance clarity beyond the schema. 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 clearly states the tool returns 'the newest NIH grants and newly-registered clinical trials in a territory', specifying both verb and resource. It explicitly distinguishes itself as a 'GTM-trigger tool' for life-sciences sales, differentiating from sibling tools like delta_digest or search_records.

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 context on when to use (high-intent buying signal for sales) but does not explicitly state when not to use or compare to alternatives. It implies usage for initial outreach but lacks direct 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?

No annotations are provided, so the description carries full burden. It discloses that the data is 'snapshotted' (not real-time), but does not mention read-only nature, authentication needs, or side effects. Some behavioral context but limited.

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 key verbs and resource. It is efficient but could be slightly more structured by grouping filter types.

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 13 parameters and no output schema or annotations, the description is too brief. It does not explain return format, pagination (despite a limit parameter), or what 'enrichment' entails. More details are needed for a complex search 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 description coverage is 77% (high), so baseline is 3. The description adds high-level filter categories but does not compensate for the 3 undocumented parameters (keyword, maxAmount, minAmount). The schema already documents most parameters.

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 performs ad-hoc search/enrichment over snapshotted grants and trials, listing example filters (keyword, sponsorClass, topic, $ range). It distinguishes from siblings by specifying the scope and flexibility.

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 ad-hoc searching but does not explicitly state when to use this tool versus alternatives like delta_digest or whats_changed_since. No exclusions or when-not guidance is 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 that the tool returns new/modified records based on a cursor, implying it is a read operation with state tracking. However, it does not mention any side effects, authentication needs, rate limits, or other behavioral traits, leaving significant gaps for an agent assessing safety.

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 at three sentences, front-loading the purpose and cursor usage. It avoids fluff but could be slightly more structured to separate usage guidance from filter reference.

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 14 parameters, no output schema, and no annotations, the description is incomplete. It explains the cursor mechanism well but does not describe return values, pagination, limits, or any caveats. The cross-reference to new_funding helps but relies on the agent knowing that tool's capabilities.

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 71% (high), so the baseline is 3. The description adds value by stating 'Same filters as new_funding,' which cross-references another tool for context. However, it does not explain individual parameters beyond what the schema already provides, and the reference assumes knowledge of new_funding.

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 returns grants/trials that are NEW or MODIFIED since a cursor token, with a specific verb and resource. However, it does not explicitly differentiate from sibling tools like delta_digest or entity_watch, relying on the cursor mechanism for implicit 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 instructs to pass the cursor from a prior call and notes that filters are the same as new_funding, providing some context on when to use. But it lacks explicit when-not-to-use instructions or alternative tools for different scenarios (e.g., full data retrieval).

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