Search Daily-Updated Scientific Data
Look 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 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, num_results, min_citations, and study_type 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. min_citations: Only return papers with at least this many references cited. study_type: Only return papers of this study type. One of: primary study | systematic review | meta-analysis | narrative review | protocol | dataset | commentary | other. 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 | ||
| study_type | No | ||
| num_results | No | ||
| min_citations | 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, so the description's job is lighter, but it still adds valuable behavioral context: the first 50 results are free then metered for paid users, the output can be evidence/legacy/full, and the return envelope contains claims, experiments, exact results, scope, limitations, and provenance. This goes well beyond the 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 organized into Args and Returns sections with no filler. Each sentence adds operational value, and the most important usage constraints are front-loaded in the query guidance.
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 is fully sufficient for a six-parameter tool with no schema descriptions. It covers all parameter semantics, output format choices, pricing/metering behavior, language constraints, and return structure. Nothing an agent needs to call this tool correctly is missing.
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 schema description coverage at 0%, the description carries the full burden and succeeds. Every parameter is explained with ranges, defaults, allowed values, and behavioral meaning — e.g., num_results '(1-100, default 16)', study_type lists all valid options, and output_format explains each option.
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 states what the tool does and distinguishes it from the sibling lookup_paper, which implies retrieving a single known paper rather than searching across evidence.
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 gives clear usage context, including 'SHORT, concise queries are best,' 'English language only,' and guidance to use days_back, num_results, min_citations, and study_type instead of embedding filters in the query. However, it does not explicitly explain when to choose this tool over the sibling lookup_paper.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
search_papers2 fields changedInput schema / properties / min_citationsAdded value: +{
+ "anyOf": [
+ {
+ "type": "integer"
+ },
+ {
+ "type": "null"
+ }
+ ],
+ "default": null
+}Input schema / properties / study_typeAdded value: +{
+ "anyOf": [
+ {
+ "type": "string"
+ },
+ {
+ "type": "null"
+ }
+ ],
+ "default": null
+}lookup_paper3 fields changedInput schema / additionalPropertiesRemoved value: -falseInput schema / properties / doi / descriptionRemoved value: -"The DOI of the paper (e.g. \"10.1038/s41586-024-07386-0\")."Input schema / properties / output_formatAdded value: +{
+ "default": "evidence",
+ "type": "string"
+}search_papers5 fields changedInput schema / additionalPropertiesRemoved value: -falseInput schema / properties / days_back / descriptionRemoved value: -"Only return papers published within the last N days."Input schema / properties / num_results / descriptionRemoved value: -"Number of results to return (1-100, default 16). First 50 results are free, then billed at $0.01/result for paid users."Input schema / properties / output_formatAdded value: +{
+ "default": "evidence",
+ "type": "string"
+}Input schema / properties / query / descriptionRemoved value: -"Search terms (e.g. \"CRISPR gene editing efficiency\") Short, concise queries are best. English language only. Don't include years or filters — use the days_back and num_results params instead."lookup_paper2 fields changedInput schema / properties / api_keyRemoved value: -{
- "anyOf": [
- {
- "type": "string"
- },
- {
- "type": "null"
- }
- ],
- "default": null
-}Input schema / properties / doi / descriptionAdded value: +"The DOI of the paper (e.g. \"10.1038/s41586-024-07386-0\")."search_papers4 fields changedInput schema / properties / api_keyRemoved value: -{
- "anyOf": [
- {
- "type": "string"
- },
- {
- "type": "null"
- }
- ],
- "default": null
-}Input schema / properties / days_back / descriptionAdded value: +"Only return papers published within the last N days."Input schema / properties / num_results / descriptionAdded value: +"Number of results to return (1-100, default 16). First 50 results are free, then billed at $0.01/result for paid users."Input schema / properties / query / descriptionAdded value: +"Search terms (e.g. \"CRISPR gene editing efficiency\") Short, concise queries are best. English language only. Don't include years or filters — use the days_back and num_results params instead."lookup_papersearch_papersClaiming proves that you control a remote MCP connector. It does not move, proxy, or interrupt the server.
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Add one secure layer between your agents and this server.
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.