Skip to main content
Glama
gauravfs-14

Literature Review Assistant

by gauravfs-14
OverviewSchemaRelated ServersScoreDiscussions
Python
Remote

Server Quality Checklist

67%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v1.0.0

  • Disambiguation5/5

    The two tools have clearly distinct purposes: one searches arXiv for papers, while the other searches DBLP for computer science papers. There is no overlap in their target databases or functionality, making them easily distinguishable.

    Naming Consistency5/5

    Both tools follow a consistent naming pattern with a database prefix followed by '_search' (arxiv_search, dblp_search). This verb_noun structure is uniform and predictable across the set.

    Tool Count2/5

    With only two tools, the server feels thin for a 'Literature Review Assistant' scope. It lacks essential operations like retrieving paper details, managing references, or accessing other databases, limiting its utility for comprehensive reviews.

    Completeness2/5

    The tool set is severely incomplete for literature review tasks. It only provides search functionality for two databases, missing critical operations such as fetching full-text, citation analysis, or integration with reference managers, which are core to the domain.

  • Average 3.1/5 across 2 of 2 tools scored.

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

    • 0 of 1 community issues answered or closed 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 is passing

  • This repository is licensed under MIT License.

  • This repository includes a README.md file.

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

    Tip: use the "Try in Browser" feature on the server page to seed initial usage.

  • Add a glama.json file to provide metadata about your server.

  • If you are the author, simply .

    If the server belongs to an organization, first add glama.json to the root of your repository:

    {
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}

    Then . Browse examples.

  • Add related servers to improve discoverability.

How to sync the server with GitHub?

Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.

To manually sync the server, click the "Sync Server" button in the MCP server admin interface.

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 of behavioral disclosure. It states the tool searches for papers but doesn't describe any behavioral traits such as rate limits, authentication needs, response formats, or error handling. This leaves significant gaps for a search tool with an output schema.

    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 a single, efficient sentence that directly states the tool's purpose without unnecessary words. It's appropriately sized and front-loaded, making it easy to parse quickly. Every word earns its place.

    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 low complexity (search function) and the presence of an output schema, the description is somewhat complete but lacks details on usage guidelines and behavioral traits. It covers the basic purpose but doesn't provide enough context for optimal agent decision-making, especially with no annotations.

    Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

    Parameters3/5

    Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

    Schema description coverage is 0%, so the description must compensate, but it adds no information about parameters beyond what the schema provides. The description doesn't explain what 'query' should contain or how 'max_results' affects the search. With two parameters and no param info in the description, this meets the baseline for minimal adequacy.

    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 'Search for papers on arXiv' clearly states the action (search) and resource (papers on arXiv), making the purpose immediately understandable. It distinguishes from the sibling tool 'dblp_search' by specifying the arXiv database, though it doesn't explicitly contrast them. The description avoids tautology by not merely restating the tool name.

    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 alternatives. It doesn't mention the sibling tool 'dblp_search' or any other search options, nor does it specify contexts or exclusions for use. The agent must infer usage from the tool name and description alone.

    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 full burden for behavioral disclosure. It mentions searching but doesn't describe rate limits, authentication requirements, result format, or whether this is a read-only operation. The description is minimal and lacks important behavioral context.

    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 - a single sentence that states the core purpose without any fluff. It's front-loaded with the essential information and wastes no words.

    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 that there's an output schema (which presumably describes the return format), the description doesn't need to explain return values. However, for a search tool with no annotations and 0% schema description coverage, the description is quite minimal and could benefit from more context about the search scope, limitations, or result characteristics.

    Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

    Parameters3/5

    Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

    Schema description coverage is 0%, but the description doesn't add any parameter-specific information beyond what's obvious from the parameter names. The description mentions 'search' which aligns with the 'query' parameter, but provides no details about query syntax, result ordering, or what 'max_results' actually controls beyond the obvious.

    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 action ('Search') and target resource ('DBLP database for computer science papers'), making the purpose immediately understandable. However, it doesn't differentiate from the sibling tool 'arxiv_search' which likely searches a different database for similar content.

    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 the sibling 'arxiv_search' or any other alternatives. The description simply states what it does without context about appropriate use cases or limitations.

    Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

GitHub Badge

Glama performs regular codebase and documentation scans to:

  • Confirm that the MCP server is working as expected.
  • Confirm that there are no obvious security issues.
  • Evaluate tool definition quality.

Our badge communicates server capabilities, safety, and installation instructions.

Card Badge

lit-mcp MCP server

Copy to your README.md:

Score Badge

lit-mcp MCP server

Copy to your README.md:

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/gauravfs-14/lit-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server