Semantic Scholar MCP Server

by HenryKautz

Overview Schema Source Related Servers Score Discussions Admin

Python

Hybrid

Server Quality Checklist

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Latest release: v0.1.0
Disambiguation5/5
Each tool targets a distinct operation: search vs. lookup by ID, citation graph traversal, author retrieval, recommendations, and batch lookup. There is no meaningful overlap or ambiguity between the tools.
Naming Consistency5/5
All tools follow a consistent snake_case verb_noun pattern: search_papers, get_paper, get_citations, get_author, recommend_papers, batch_get_papers. The naming is predictable and makes the action and target clear.
Tool Count5/5
Nine tools is well-scoped for a scholarly literature and author discovery server. Each tool has a distinct purpose without the surface feeling bloated or sparse.
Completeness5/5
The server covers the core read-only academic search workflow: searching papers, retrieving details, batch lookups, citation/reference exploration, author search and paper lists, plus recommendations. No obvious gaps exist for its stated purpose.
Average 3.6/5 across 9 of 9 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
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- No stable releases found
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How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden for behavior. It adds a useful limit range (1-500, default 10), but it does not disclose how recommendations are computed, expected ordering, possible rate limits, or failure behavior. The description is not misleading, but it is thin on behavior beyond the basic request-response contract.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is compact and front-loaded with the main purpose, followed by concise parameter explanations. The Args block partially duplicates the input schema, but it remains clear and useful without unnecessary fluff.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool is relatively simple and has an output schema, so return-value details are not required. However, the description does not cover when to choose recommendations over the related citation/reference tools or how the recommendation source is defined, leaving some selection and expectation gaps for an agent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, and the description compensates by explaining both parameters: paper_id is a Semantic Scholar paper ID and limit is the maximum recommendations count with range and default. This adds meaning beyond the schema's type and title fields.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb and resource: 'Get paper recommendations based on a given paper.' This distinguishes it from search_papers, but it does not explicitly contrast with get_citations or get_references, so sibling differentiation is only implicit.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description does not say when to use this tool versus alternatives such as get_citations or get_references. Usage context is only implied by the tool name and the phrase 'based on a given paper.' There is no explicit when-to-use guidance or exclusion criteria.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
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 of behavioral disclosure. It only restates the parameters and gives no details about match behavior, result ordering, pagination, rate limits, or edge cases. For a read-only search this is less risky than a mutation, but still minimal.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is compact and front-loaded with the purpose, followed by a short Args block. Every sentence adds value, with no filler or unnecessary repetition of schema details.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple two-parameter search tool with an output schema, the description covers the core call adequately. However, with no annotations and no guidance on distinguishing it from sibling tools, some contextual information is left to inference.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, but the description compensates by explaining that 'query' is the author name and 'limit' is the max results with a range of 1-100 and default of 10. This adds meaningful semantic information beyond the bare schema types.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a specific verb ('Search') and resource ('authors on Semantic Scholar'), making the core purpose clear. It is implicitly distinct from siblings like search_papers, but it does not explicitly differentiate itself from get_author or other author-related tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
There is no guidance on when to use this tool versus alternatives such as search_papers or get_author. The description only states what the tool does, leaving the agent to infer selection criteria from the sibling list.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It indicates this is a read operation ('Get') but does not mention response format expectations, error behavior for invalid IDs, rate limits, or any other quirks. 'Detailed information' is vague about what is included.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is very concise, with a clear first sentence and a short parameter note. Every word is functional and there is no redundancy or extraneous detail.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the simple interface (one required parameter) and the presence of an output schema, the description is largely sufficient. The main gaps are the lack of explicit usage context and alternative routing, but these are not essential for calling the tool correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description adds important semantic meaning to the single parameter by explaining author_id is a 'Semantic Scholar author ID.', which goes beyond the schema's bare title 'Author Id'. It compensates for the schema's 0% coverage, though it could have provided an example or format hint.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a specific verb ('Get') and resource ('detailed information about an author'), clearly indicating the tool's purpose. It is distinguishable from sibling tools like search_authors and get_author_papers because it targets author details rather than searching or listing papers, though it does not explicitly name these alternatives.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided about when to use this tool versus alternatives such as search_authors (for finding an author ID) or get_author_papers (for an author's publications). The context of use is only implied by the description.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description is the sole behavioral signal. It adds useful facts: limit range (1-1000), default 10, offset pagination, and accepted ID forms. It does not mention auth, rate limits, side effects, or empty-result behavior, though this is a read-only fetch operation.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is compact: one purpose sentence followed by a three-line argument list. Every sentence carries useful information, with no filler or repetition.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a straightforward retrieval tool with three parameters, an output schema, and documented argument constraints, the description is nearly complete. The only gap is a lack of explicit comparison to get_citations, but the purpose sentence already conveys the distinction.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description compensates by explaining that paper_id accepts DOI/arXiv identifiers, providing limit's range and default, and defining offset as a pagination offset. This adds real meaning beyond the schema's type-only definitions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a specific verb and resource: 'Get papers referenced by a given paper.' The term 'referenced' clearly indicates outgoing citations, which distinguishes it from the sibling get_citations, though it doesn't explicitly name the alternative.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided about when to use this tool instead of siblings such as get_citations, get_paper, or search_papers. There are no stated conditions or exclusions, leaving the agent to infer appropriate usage.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must carry the behavioral disclosure burden. 'Get' implies a read-only operation, and the Args mention limit and offset, but the description does not disclose response shape, pagination behavior, rate limits, error cases, or whether the result is ordered. This is thin for a tool with no annotation backing.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is compact and front-loaded with the core purpose, followed by a scannable Args list. Every sentence earns its place; there is no padding or repetition of the schema.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple list-by-author endpoint with an output schema, the call parameters are well covered. The main gaps are contextual: no alternative routing to sibling tools, no behavioral notes about ordering or limits, and no warning about potential long lists despite the offset parameter. Still, the output schema reduces the need to document return values.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, and the Args block fully compensates: author_id is identified as a Semantic Scholar ID, limit is given a range (1-1000) and default (10), and offset is described as a pagination offset. This adds real meaning beyond the bare schema types.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a clear verb and resource: 'Get papers written by a specific author.' This is unambiguous and implicitly separates the tool from siblings like get_author or search_papers, but it does not explicitly name or contrast those alternatives, so it misses full sibling differentiation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The intended use is implied: call this when you have an author_id and want that author's papers. However, there is no explicit when-not-to-use guidance, no prerequisites beyond author_id, and no mention of alternatives like search_papers for keyword search or get_author for profile details.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must carry behavioral disclosure. It only says 'Get' and lists ID formats, which are input constraints rather than behavioral context. It does not state read-only status, rate limits, coverage, or what 'detailed information' includes beyond what an output schema would show.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is short, front-loaded with the tool's purpose, and every line earns its place. The Args section packs all identifier variants into a compact format without redundant prose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With a single required parameter, an existing output schema, and full identifier enumeraton, the description is nearly complete for a simple lookup tool. It would improve from a brief note routing users to search_papers when no identifier is available or any behavioral caveats.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema provides no description for paper_id, but the Args block fully compensates by enumeratting all accepted formats: Semantic Scholar paper ID, DOI, ArXiv, CorpusId, PMID, ACL ID, and URL. This gives the agent the exact input semantics needed to invoke the tool correctly.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
States a clear verb-resource pair ('Get detailed information about a specific paper'). It distinguishes the tool from search-oriented siblings at a basic level, but does not explicitly contrast it with get_citations or get_references.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The accepted identifier list implies this tool is for lookups when you already have a paper ID or URL. It does not explicitly guide the agent toward search_papers when no ID is available, nor does it describe when not to use this tool.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It lists parameters and their filter effects, but it does not state what the tool returns, how results are ordered (e.g., relevance, date), whether it is a read-only operation, or any potential side effects or rate limits. The behavior beyond 'search' is largely opaque.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two components: a one-sentence purpose statement and a compact argument list. Every element serves a function; there is no filler or redundant phrasing. The purpose is front-loaded, and the parameter definitions are easy to scan and parse.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description covers all parameters and the basic purpose, which is a good foundation. However, it lacks any indication of output shape (though an output schema exists), sorting behavior, or how this search relates to the search_authors sibling. These are relatively minor gaps for a search tool, but they prevent a perfect score.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description is the only source of parameter meaning. It provides clear explanations for each parameter, including value ranges for limit, format examples for year, and a comma-separated example for fields_of_study. This goes well beyond the typed schema, making the tool safely callable.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description's first sentence, 'Search for academic papers on Semantic Scholar,' clearly identifies the specific action (search) and resource (academic papers). It naturally distinguishes this from siblings like get_paper (retrieves a specific paper), get_citations (retrieves citations), and search_authors (searches for authors). The argument list further clarifies the intended scope.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description does not explicitly state when to use this tool versus alternatives. It does not mention that get_paper should be used when a paper ID is already known, or that recommend_papers serves a different discovery purpose. The only implicit guidance is the verb 'search,' which is insufficient for an agent to make reliable routing decisions among multiple search-related siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the burden for behavioral context. It discloses the core behavior (incoming citations) and pagination via limit/offset, but it does not mention result ordering, error behavior, or any rate-limit/authentication considerations. This is adequate but not rich.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences plus a compact Args list deliver purpose and parameters with no filler. The key behavior is front-loaded before parameter details.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple read-only lookup with an output schema and three straightforward parameters, the description is nearly complete. The main remaining gap is the absence of explicit alternative routing (e.g., get_references for outgoing citations), but the semantics and parameter details are sufficient for correct invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage, the description fully compensates: paper_id explains accepted ID formats ('DOI, ArXiv ID, etc.'), limit adds a range and default ('1-1000, default 10'), and offset explains its role. This is meaningful beyond the raw types/titles in the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
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: 'Get papers that cite a given paper.' This clearly identifies the tool's operation and distinguishes it from siblings such as get_references (papers cited by) and get_paper (a single paper).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description conveys its intended use—retrieve citing papers for a paper ID—but it does not explicitly contrast this with get_references or other sibling tools, nor state when not to use it. Usage is implied by the purpose rather than spelled out.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It adds valuable behavioral constraints beyond the schema: accepted ID formats (S2 IDs, DOIs, ArXiv IDs), a hard limit of 500, and that fields default to standard detail fields. The verb 'Look up' also makes the read-only nature clear. It does not discuss error behavior for invalid IDs, but the output schema covers return structure.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is compact and front-loaded, stating the core behavior in one sentence and then documenting the two parameters succinctly. No filler or repetition of schema fields.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a 2-parameter lookup with an output schema present, this is complete: it defines the operation, both parameters, accepted ID types, the max batch size, and field behavior. An agent has everything needed to invoke correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, and both parameters are fully explained in the description: paper_ids includes accepted identifier types and the 500 limit, while fields specifies comma-separated output format and default behavior. This fully compensates for the bare schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description opens with a specific action and resource: 'Look up multiple papers at once by their IDs.' This directly distinguishes it from get_paper (single paper) and search_papers (query-based), so an agent can pick it correctly without opening schemas.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The phrase 'multiple papers at once by their IDs' supplies clear usage context: use this when you already have a batch of paper IDs and need their details. It does not explicitly name alternatives or list exclusions, so it stops short of a 5.
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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