Search Daily-Updated Scientific Data
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Average 4.8/5 across 2 of 2 tools scored.
The two tools are clearly distinct: one retrieves a specific paper by DOI, the other searches for papers based on a query. There is no overlap or ambiguity in their purposes.
Both tool names follow a consistent verb_noun pattern (lookup_paper, search_papers), with a minor pluralization difference that does not affect consistency. The naming style is uniform and predictable.
With only two tools, the server feels borderline thin for a scientific data domain. However, both tools are substantial and cover the core functions of searching and retrieving papers, so it is not overly limiting.
The two tools cover the primary workflow of searching and retrieving papers by DOI. Minor gaps exist (e.g., no advanced filtering or sorting), but there are no obvious dead ends for the stated purpose of accessing claim-level evidence.
lookup_paperLook up paper by DOIALook up a single paper by its DOI.
Args: doi: The DOI of the paper (e.g. "10.1038/s41586-024-07386-0"). output_format: "evidence" for compact claim-level evidence (default), "legacy" for original paper metadata, or "full" for both.
Returns: An envelope with found status and the paper in result, or a not-found message. A found paper counts as one result.
| Name | Required | Description | Default |
|---|---|---|---|
| doi | Yes | ||
| output_format | No | evidence |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Beyond the readOnlyHint and idempotentHint annotations, the description discloses meaningful behavior: it returns an envelope with found status, a paper in result or a not-found message, and notes that a found paper counts as one result. It also explains the output_format options, providing transparency beyond annotations.
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 well-structured with clear 'Args' and 'Returns' sections, is appropriately concise, and front-loads the purpose. Every sentence adds useful information without 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 two-parameter lookup tool with an output schema, the description is complete: it covers purpose, parameter semantics, return envelope structure, and the not-found case. No critical information appears to be missing for an agent to invoke this 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?
With 0% schema description coverage, the description fully compensates by explaining both parameters: doi includes a concrete example, and output_format enumerates the three possible values ('evidence', 'legacy', 'full') with their meanings and the default. This adds significant meaning beyond the bare 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's function as 'Look up a single paper by its DOI.' This is a specific verb+resource combination that immediately distinguishes it from the sibling tool search_papers, which implies broader search behavior.
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 the tool should be used when you have a specific DOI and need a single paper, which is clear context. However, it does not explicitly mention the sibling tool search_papers as an alternative or provide any when-not-to-use guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
search_papersSearch scientific evidenceASearch claim-level evidence extracted from full-text scientific papers.
Args: query: Search terms (e.g. "CRISPR gene editing efficiency"). SHORT, concise queries are best. English language only. Use days_back and num_results instead of adding years or filters to the query. num_results: Number of results to return (1-100, default 16). First 50 results are free, then metered per result for paid users. days_back: Only return papers published within the last N days. output_format: "evidence" for compact claim-level evidence (default), "legacy" for original paper metadata, or "full" for both.
Returns: An envelope whose results list contains papers with claims, experiments, exact results, demonstrated scope, limitations, and provenance.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | ||
| days_back | No | ||
| num_results | No | ||
| output_format | No | evidence |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint and idempotentHint, but the description adds rich behavioral context: it explains the output envelope structure, the impact of 'days_back' and 'num_results', and notably the metering caveat ('First 50 results are free, then metered per result for paid users'). It also clarifies the 'output_format' options and their meaning, exceeding what annotations convey.
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 well-structured with a clear purpose statement, an 'Args' section that uses bullet-like formatting, and a 'Returns' section. Every sentence adds value—no fluff, and it is appropriately sized for the tool's complexity.
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 4-parameter tool, existing output schema, and annotations, the description covers all essential aspects: what the tool does, query constraints, result counts, time filtering, output formats, and the structure of the returned envelope. It is complete and self-contained.
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 carries the full burden. It thoroughly explains each parameter: 'query' with example and language constraint, 'num_results' with bounds/default/cost implication, 'days_back' with meaning, and 'output_format' with all three enum-like values and their effects. This goes far beyond the minimal 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 a specific verb and resource: 'Search claim-level evidence extracted from full-text scientific papers.' This clearly distinguishes it from the sibling tool 'lookup_paper' (which likely retrieves a single paper) by emphasizing claim-level evidence and search behavior.
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 concrete guidance on when to use it and how to formulate queries: 'SHORT, concise queries are best' and 'Use days_back and num_results instead of adding years or filters to the query.' However, it does not explicitly mention the sibling tool 'lookup_paper' as an alternative for retrieving a specific paper, so exclusions are absent.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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{
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