research-intel

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

No annotations exist, so the description must carry full behavioral disclosure. It only mentions filtering by recency and the kind of data returned, but lacks details on default behavior, pagination, or side effects, which are critical for an agent to invoke safely.

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 efficiently conveys the core purpose without any superfluous 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, no output schema, and no annotations, the description is too brief. It does not explain how parameters combine (e.g., conjunction), default days, or result format, leaving significant gaps 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?

With 77% schema description coverage, the schema already documents most parameters. The tool description adds no additional semantic context beyond what is in 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 clearly states the tool returns 'New grants + new trials' filtered over a recency window, with specific mention of 'weekly-digest payload' and 'Best for BD teams', distinguishing it from sibling tools like search_records or entity_watch.

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 indicates it's 'Best for BD teams' and implies use for a weekly digest, but does not explicitly exclude scenarios or compare against sibling tools, leaving room for ambiguous selection.

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 tool returns 'recent funding/trial events' and limits to one entity type. It does not mention pagination, limits, or side effects, but the behavior (read-only query) is implied. A score of 3 reflects adequate but not thorough transparency.

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 extremely concise—one sentence with clear action and a second sentence for usage context. Every word adds value with no repetition or 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?

Given the simple input schema (2 required params, no nesting, no output schema), the description provides sufficient context: what it retrieves (recent events) and for whom (one entity). It could optionally specify recency window or result format, but the current level is adequate for a straightforward query 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 coverage is 100%, and the description largely restates the schema's parameter descriptions (e.g., 'institution | pi | sponsor | topic'). It adds no additional meaning or usage examples beyond what the schema already provides. 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 states it retrieves recent funding/trial events for a single entity (institution, PI, sponsor, or topic). It also provides usage context ('enrich an account or check a competitor'), which distinguishes it from sibling tools like 'search_records' or 'new_funding' that likely cover broader or different scopes.

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 includes explicit usage advice ('Use to enrich an account or check a competitor'), giving a clear when-to-use signal. However, it does not explicitly mention when not to use or direct comparison with siblings, though the context implies 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?

With no annotations, the description carries full burden. It mentions 'newest' and default limit but lacks details on data freshness, sorting order, pagination, or data source 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 plus a list of filter options, front-loaded with the core purpose. No redundancy or 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?

Given 13 parameters and no output schema, the description lacks details on output format, rate limits, or data scope. More context would improve completeness for agents.

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 high (77%). The description summarizes key filters but adds little meaning beyond what the schema already provides.

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 newest NIH grants and clinical trials, framing it as a high-intent buying signal. This purpose is distinct 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 positions the tool as a GTM trigger for sales prospecting and lists filterable fields, implying its use case. However, it does not explicitly state when not to use it or compare to alternatives.

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 mentions the data is 'snapshotted', indicating it is not real-time, which is useful. However, it does not disclose side effects (likely read-only but unstated), authentication needs, rate limits, or behavioral traits like pagination (implied by 'limit' param). The description adds some transparency but leaves 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 sentence with no wasted words. It front-loads the key action ('Ad-hoc search/enrichment') and immediately specifies the target data and filter types. Every part contributes to understanding.

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 13 optional parameters and no output schema, the one-sentence description is insufficient for full understanding. It does not explain return format, pagination behavior, or how multiple filters interact. However, it captures the core functionality, which is typical for a search tool. The combination with schema descriptions partially compensates.

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%, so the schema already documents most parameters. The tool description adds high-level context by mentioning 'keyword, sponsorClass, topic, $ range' which maps to several parameters, but it doesn't provide additional meaning beyond what's in the schema. For the undocumented parameters (e.g., keyword), the description partially compensates by including 'keyword' in the list. Overall, marginal added 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 the tool performs 'Ad-hoc search/enrichment over snapshotted grants + trials' with specific filter types (keyword, sponsorClass, topic, $ range). The verb 'search' and resource 'grants + trials' are specific, and the sibling tool names (delta_digest, entity_watch, etc.) suggest distinct non-search functions, so this tool is well-differentiated.

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 and filtering of grants and trials, but does not explicitly state when to use this tool versus siblings or when not to use it. No alternative tools are mentioned, leaving the agent to infer context from sibling names alone.

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, description explains the cursor-based incremental update behavior and mentions it returns new/modified entries. However, does not disclose rate limits, authentication requirements, or error handling for invalid cursors.

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, 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?

For a tool with 14 parameters and no output schema, the description explains the core cursor mechanism and references sibling for filters, but lacks details on response format, pagination, or error states.

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%, so the schema already documents most parameters. The description adds value by stating 'Same filters as new_funding', but does not elaborate on individual parameters beyond the cursor explanation.

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?

States verb 'Return', resource 'grants/trials', and key concept 'NEW or MODIFIED since a cursor token'. Distinguishes from siblings by mentioning 'Same filters as 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?

Explicitly instructs to 'Pass the cursor from a prior call to get only changes since then' and references new_funding for filters, giving clear usage context but no explicit when-not-to-use.

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