Arxivum

by eddisonpham

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Server Quality Checklist

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Latest release: v0.1.0
Disambiguation5/5
Each tool targets a distinct action and resource: searching/importing vs. querying local library vs. retrieving details vs. removing vs. generating summaries vs. generating ideas vs. verifying novelty vs. listing papers vs. activity log. There is no overlap or ambiguity between them.
Naming Consistency5/5
All tools follow a consistent pattern: `research_` prefix + imperative verb + optional noun (e.g., `research_search_papers`, `research_generate_summary`). All use snake_case with no mixed conventions, making the pattern predictable and readable.
Tool Count5/5
With 9 tools, the server is well-scoped for an academic research assistant. Each tool provides essential functionality without overloading the interface, covering search, import, retrieval, generation, and verification. The count is within the ideal 3–15 range.
Completeness4/5
The tools cover the core lifecycle: importing, querying, retrieving details, removing, generating summaries/ideas, and verifying novelty. Minor gaps exist—for example, no tool to manually add a paper by ID or to directly edit metadata—but agents can work around these using existing tools.
Average 4/5 across 9 of 9 tools scored. Lowest: 3.4/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 37 commits in the last 12 weeks
- No stable releases found
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- No high-severity vulnerability alerts
- No code scanning findings
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This repository is licensed under MIT License.
This repository includes a README.md file.
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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 bears full responsibility for behavioral disclosure. It does not mention that this is a read-only operation, potential rate limits, or any side effects. The description is minimal.
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 short (two sentences plus an arg list) and immediately states the purpose. However, it could be better structured by front-loading the key information more prominently.
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?
Given the tool's simplicity (2 optional params, no nested objects) and the presence of an output schema (not shown but indicated), the description adequately covers purpose and parameters. However, it lacks behavioral context like whether entries are ordered or paginated.
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?
Although schema description coverage is 0%, the description compensates by listing the allowed values for action_type (search, import, etc.) and explaining the limit parameter's role. This adds meaning beyond the raw 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 clearly states the tool returns recent agent actions from the activity log for supervision. The name and description together uniquely identify this tool among siblings, which are all paper/library-focused.
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 like research_search_papers or research_list_library. No exclusions or context about selecting this tool are provided.
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?
The description discloses that removal affects 'all derived data (summaries, ideas, embeddings)', which is important behavioral context. However, it does not state whether removal is permanent or reversible, nor does it mention any confirmation requirement. With no annotations, this is adequate but incomplete.
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 concise, with a clear structure: a one-line summary of purpose followed by an Args section that explains each parameter. No extraneous information; every sentence adds value.
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 removal tool with two parameters and an output schema, the description covers the main action and side effects. It does not detail error handling or prerequisites (e.g., paper must exist), but it is largely complete for typical use.
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 meaning to both parameters: 'arxiv_id' is described as 'arXiv ID of the paper to remove', and 'delete_files' as 'Also delete cached files (default True)'. Since the input schema has 0% description coverage, the description compensates well by explaining the purpose and default behavior.
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 clearly states the action 'Remove a paper and all derived data' and specifies the resource 'from the local library'. It distinguishes this tool from siblings like research_search_papers and research_list_library by focusing on deletion.
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 provide explicit guidance on when to use this tool versus alternatives. There is no mention of prerequisites (e.g., paper must exist) or situations where removal is not recommended. Siblings like research_get_paper_details or research_verify_novelty are not mentioned in contrast.
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 provided, so the description must carry the burden. It mentions output includes search queries for novelty verification, but does not disclose other behaviors like computational cost or potential limitations.
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 concise with no unnecessary words. It front-loads the purpose and uses a clear Args list for parameters.
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 presence of an output schema and simple inputs, the description sufficiently covers the tool's behavior. However, it lacks context on when to choose this tool over related tools, which is a minor gap.
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%, but the description's Args section clearly explains each parameter's meaning, including constraints (num_ideas 1-5) and valid values (focus_area options). This adds significant value beyond 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 clearly states the tool generates novel research ideas based on a paper, with a specific verb and resource. It is distinct from sibling tools like research_generate_summary or research_verify_novelty.
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 explicit guidance on when to use this tool versus siblings. The description does not provide context for selection or exclusions.
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 carries full behavioral burden. It discloses the caching/regenerate behavior via the 'force' parameter but does not mention destructive potential, required permissions, or side effects. It is adequate but not thorough.
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 relatively concise with a clear header sentence, a bullet list of sections, and separate parameter explanations. However, it could be slightly more streamlined.
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 presence of an output schema (so return format is handled), the description covers the main purpose, parameters, and caching behavior. It lacks integration context with sibling tools but is otherwise complete for its complexity.
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 coverage is 0%, so the description compensates by explaining 'arxiv_id' (arXiv ID), 'sections' (which sections, default all, lists them), and 'force' (regenerate even if cached). 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.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Generate or retrieve a structured summary') and the resource ('a paper'). It lists specific sections, making the output explicit. The tool is well-distinguished from siblings like research_get_paper_details and research_generate_ideas.
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 implies usage by naming parameters but lacks explicit guidance on when to prefer this tool over alternatives. No when-not or sibling comparisons are provided.
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, and the description does not disclose behavioral traits such as whether the tool modifies data (it appears read-only but not stated), authorization requirements, or rate limits. It only describes the search algorithm.
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 concise, with a clear structure: first sentence states purpose and method, followed by a bulleted Args list. Every sentence is informative and necessary, with no redundancy.
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 tool's complexity (6 parameters, output schema exists), the description covers input meaning, method, and return type adequately. However, it does not mention prerequisites like the library being initialized, but this is minor for a query tool.
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%, but the description includes an Args section that clearly explains each parameter's purpose, default behavior, and acceptable values (e.g., 'Natural-language query', 'Filter out papers with fewer citations'). This fully compensates for the lack of schema descriptions.
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 clearly states the tool's function: searching the local library using hybrid vector + metadata retrieval. It specifies the return format (JSON with scored results, abstracts, citations), making the purpose unambiguous.
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 implies usage for searching the local library, contrasting with potential global search siblings, but does not explicitly state when to use this tool versus alternatives like research_search_papers or research_list_library.
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 provided, the description bears the full burden of behavioral disclosure. It correctly implies a read-only operation ('List'), but does not explicitly mention that it is non-destructive or discuss permissions, rate limits, or other behavioral traits.
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 concise, front-loaded with the main purpose, and each sentence adds value by detailing a parameter. There is no redundant or irrelevant content.
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 that an output schema exists, the description need not cover return values. It comprehensively explains input parameters and basic behavior, but lacks details such as sorting direction (asc/desc) or pagination limits, which would enhance completeness for a list tool.
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 fully compensates by explaining all 6 parameters (limit, offset, sort_by, primary_category, min_citations, venue) with their purpose, defaults, and examples (e.g., 'e.g. cs.LG', 'e.g. NeurIPS'), adding significant meaning beyond the schema properties.
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 clearly states 'List papers in the local library with pagination and optional filters', specifying the action (list), resource (papers in local library), and distinguishing features (pagination and filters) that differentiate it from sibling tools like research_search_papers and research_query_library.
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 implies usage for listing papers with filters and pagination, but it does not explicitly state when to use this tool versus alternatives, nor does it provide any exclusions or prerequisites.
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?
No annotations are provided, so the description carries the burden. It discloses the tool returns metadata, citation metrics, summaries, and ideas, implying it is a read-only operation. However, it does not mention error handling for missing papers or prerequisites like the paper being in the library.
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 extremely concise with two sentences plus an Args section, front-loading the purpose and immediately providing parameter details without any superfluous content.
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 tool has one parameter, an output schema, and low complexity, the description adequately explains what is returned and the parameter format. It could mention the requirement that the paper exists in the library, but overall it is sufficiently complete.
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?
With 0% schema description coverage, the description adds value by clarifying the arxiv_id format ('Normalized arXiv ID (e.g. 2106.00001)'), which is not obvious from the schema alone.
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 clearly states the tool retrieves 'full metadata, citation metrics, summaries, and ideas' for a specific paper, distinguishing it from sibling tools like 'research_search_papers' (searching) and 'research_remove_paper' (removing).
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 implies usage for papers already in the local library and specifies the arxiv_id format, but does not explicitly guide when to use this tool over siblings like 'research_generate_summary' or 'research_generate_ideas'.
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, so description covers behavior: checks library and arXiv, uses LLM for overlap, returns verdicts. However, no mention of side effects, permissions, or cost implications.
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?
Description is structured with purpose, process, and args. It is slightly lengthy but each sentence adds value. Could be more concise without the verbatim typo.
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 an output schema, return values are not required, but description mentions verdicts. It covers inputs, process, and output clearly. Lacks error handling or performance notes.
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 has 0% description coverage, but description's Args section fully explains both parameters: idea_id (from generate_ideas) and search_query (optional override). This adds critical meaning beyond 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?
Description uses specific verb 'Run' on resource 'novelty re-verification on a previously generated idea'. It clearly distinguishes from siblings like research_search_papers and research_query_library by focusing on re-verification of existing ideas.
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?
Description states the tool is for ideas from generate_ideas, implying post-generation usage. It explains the verification process but lacks explicit when-not-to-use or alternatives.
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?
No annotations provided, so description carries full burden. Discloses that it imports papers (mutation), returns a list with specific fields, and notes performance impacts of enrich and summarize options.
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?
Concise 4-line description with clear docstring structure. Front-loaded with purpose, parameter details in bullet list, no wasted words.
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?
Covers all 5 parameters meaningfully, mentions return structure (arXiv IDs, titles, citation counts), and notes performance trade-offs. Output schema exists, so return details need not be exhaustive.
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%, but description fully compensates by explaining query format, max_results range (1-50), primary_category as optional filter, auto_enrich as Semantic Scholar fetch, and summarize as post-import generation.
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?
Description clearly states it searches arXiv and imports papers into the local library, with specific verb (search) and resource (arXiv papers). Differentiates from siblings like research_query_library (queries existing library) and research_get_paper_details.
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?
Provides guidance on parameters like max_results, auto_enrich, and summarize with speed implications. Lacks explicit when-to-use versus alternatives but gives enough context for typical use.
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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