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khushiiagrawal

MCP Research Server

by khushiiagrawal
OverviewSchemaRelated ServersScoreDiscussions
Python
Hybrid

Server Quality Checklist

58%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v0.1.0

  • Disambiguation5/5

    The two tools have clearly distinct purposes: extract_info retrieves information about a specific paper by ID, while search_papers finds papers on arXiv by topic and stores them. There is no overlap or ambiguity between these operations.

    Naming Consistency5/5

    Both tools follow a consistent verb_noun pattern (extract_info and search_papers), using snake_case and descriptive action-object naming. The naming is predictable and readable throughout.

    Tool Count2/5

    With only 2 tools for a research server, the set feels thin and incomplete for the apparent scope. A research domain typically requires more operations like managing papers, updating information, or handling citations, making this count inadequate.

    Completeness2/5

    There are significant gaps in the tool surface for a research server. While search and retrieval are covered, missing operations include creating, updating, or deleting paper records, organizing topics, or accessing stored data beyond extraction, which will limit agent workflows.

  • Average 3.6/5 across 2 of 2 tools scored.

    See the Tool Scores section below for per-tool breakdowns.

    • No community issues in the last 6 months
    • 0 commits in the last 12 weeks
    • No stable releases found
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI status not available

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  • This repository includes a README.md file.

  • No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.

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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

  • Behavior3/5

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

    With no annotations provided, the description carries the full burden. It successfully discloses return behavior ('JSON string with paper information if found, error message if not found') and scope ('across all topic directories'). However, it lacks explicit safety classification (read-only vs destructive) or side-effect disclosure despite the implicit 'search' verb.

    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 uses a clean, structured format with clear 'Args' and 'Returns' sections. It is appropriately concise with no redundant or wasted sentences; every clause provides specific functional information.

    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 single-parameter lookup tool with an output schema present, the description provides adequate completeness. It documents the sole parameter (compensating for schema gaps) and summarizes return behavior, which is sufficient given the tool's low 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?

    Given 0% schema description coverage, the Args section effectively compensates by defining 'paper_id' as 'The ID of the paper to look for.' This adds necessary semantic meaning that the raw schema lacks, clearly indicating the parameter represents a paper identifier.

    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 tool 'Search[es] for information about a specific paper across all topic directories,' providing specific verb (search), resource (paper information), and scope (all topic directories). It implicitly distinguishes from sibling 'search_papers' by emphasizing 'specific paper' lookup by ID rather than general searching.

    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 provides no explicit guidance on when to use this tool versus the sibling 'search_papers'. While it implies usage by stating it looks for a 'specific paper' (suggesting use when paper_id is known), it fails to explicitly contrast with alternatives or state prerequisites.

    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 carries the full burden of behavioral disclosure. It appropriately notes the side effect of storing information and specifies the return value (List of paper IDs), but omits other critical details such as idempotency, what 'store' entails (persistent cache, session memory, etc.), error handling behavior, or rate limiting.

    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 uses a docstring format with distinct Args and Returns sections. While slightly more structured than typical prose descriptions, it efficiently organizes information with no wasted sentences. The format is machine-parseable and front-loads the core purpose before detailing 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 this is a simple 2-parameter search tool with a straightforward output (list of IDs), the description is adequately complete. It covers the search domain (arXiv), the side effect (storage), and the return type. While additional context on storage scope would be helpful, the description suffices for tool selection and basic invocation.

    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 input schema has 0% description coverage (only titles). The description compensates via the Args section, documenting both 'topic' (the search query) and 'max_results' (with default value). While it documents the parameters, it lacks rich semantic detail such as expected format for topics, examples, or constraints on max_results.

    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 searches for papers on arXiv based on a topic and stores their information. It uses specific verbs ('Search', 'store') and identifies the specific resource (arXiv papers), implicitly distinguishing it from the sibling 'extract_info' tool which likely operates on existing papers rather than searching for them.

    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 provides no guidance on when to use this tool versus the sibling 'extract_info' tool, nor does it specify prerequisites (e.g., whether a topic should be broad or specific) or when not to use it. Agents must infer usage solely from the tool name.

    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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