science-ai-mcp-server

Server Quality Checklist

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

Latest release: v0.1.3
Disambiguation5/5
Each tool has a clear, distinct purpose: duplicate checking, research gaps, journal recommendations, paper pre-check, expert review, and writer pipeline status/start. No significant overlap, and tools like check_duplicate_publication and pre_check_paper serve different aspects of pre-submission.
Naming Consistency5/5
All tool names follow a consistent verb_noun pattern with underscores (e.g., check_duplicate_publication, find_research_gaps, start_writer_pipeline). Even hakem_review_paper integrates a proper noun but still follows the same structure. No mixing of styles or conventions.
Tool Count5/5
7 tools is well-scoped for an academic research workflow server, covering pre-submission checks, gap analysis, journal recommendations, review, and writing pipeline. Not too few to be trivial, nor too many to be overwhelming.
Completeness4/5
The tool set covers the main pre-submission and review stages comprehensively, but lacks tools for editing, submitting, or tracking submissions beyond the writer pipeline. Minor gaps exist, such as no cancel or update for the pipeline, but core workflows are well-represented.
Average 4.4/5 across 7 of 7 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- No commit activity data available
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI status not available
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

Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are present, so the description carries the full burden. It mentions terminal statuses but does not describe non-terminal states, the response structure, or error behavior (e.g., if no job exists). This leaves some gaps in behavioral understanding.
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: first states the core function, second gives usage instructions and terminal statuses. Every word is purposeful, no redundancy.
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 absence of an output schema, the description should clarify the response format. It only mentions terminal statuses but not how they are returned (e.g., as a status field). Also missing details on error handling or what a non-terminal response looks like, which is important for a polling tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description names the parameters 'sessionId' and 'section' but adds no meaning beyond what the input schema provides. The schema has 0% description coverage, so the description should compensate, but it only restates the parameter names and the enum values implicitly. It does not explain what 'section' represents or provide usage context.
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 it "Return the latest writer-pipeline job for a (sessionId, section)." It specifies the verb 'Return', the resource 'latest writer-pipeline job', and the required parameters. This clearly distinguishes it from sibling tools like start_writer_pipeline and others.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly instructs to use after `start_writer_pipeline` and to poll every 5-10 seconds. It also lists terminal statuses, providing clear guidance on when to use and what to expect.
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 discloses that the tool is zero-LLM-cost, free, sub-second response, and backed by a local library. This gives the agent confidence that it is a safe read-only operation without side effects, though it does not explicitly state no mutations occur.
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: two sentences front-loading the purpose and usage. Every sentence adds value, with no redundant or vague phrasing.
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 complexity (5 parameters, no output schema, no annotations), the description covers the core functionality: what it returns (tier, field, confidence band) and when to use it. It lacks details on error handling or authentication, but for a simple scoring tool, it is adequately complete.
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?
All 5 parameters have descriptions in the schema (100% coverage), so the schema already provides parameter semantics. The description adds only general context (e.g., returns probabilities, fast) but does not elaborate on parameter-specific details 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's purpose: run a pre-submission scoring of an academic paper, returning predicted tier, field, and confidence band. It uses specific verbs and resources, and the use of 'pre-check' differentiates it from siblings like 'check_duplicate_publication' and 'recommend_journals'.
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 description explicitly tells when to use this tool: 'when the user wants a fast sanity-check on whether a paper is ready to submit, or which tier of journal to target.' It does not include when-not-to-use or alternatives, but the context is sufficient for an AI agent to decide.
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?
Discloses immediate return with jobId, ~5s claim time, default section, credit usage. Schema adds force behavior. No annotations exist so description carries full burden, and it does well.
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?
Four sentences, front-loaded with key action, no filler. Efficient and clear.
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?
Covers enqueue action, return type, timing, default, credits, and references sibling for polling. Lacks error handling details but sufficient for a simple tool with clear sibling.
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 covers 100% parameters with descriptions. Description adds minimal value: default section elaboration. Baseline 3 is appropriate.
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?
Clearly states the action (enqueue a writer-pipeline job) and resource (existing WriterSession). Distinguishes from sibling get_writer_pipeline_status by describing the polling pattern.
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 explicit context: requires existing WriterSession, default section, and references sibling for polling. Schema description adds prerequisite (create session via web UI). No explicit exclusion for concurrent jobs, but force parameter is described.
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. It discloses that it's free, runs locally via FTS5 + topic-RAG with no LLM call, and lists output fields. Missing details on error handling or side effects, but these are minor for a read-only tool.
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 with a clear front-loaded purpose and a concise list of output fields. Every word serves a purpose, no wasted text.
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?
Despite no output schema, the description details all output fields (grade, match %, tier, etc.). With 8 parameters including a nested filter, the description covers the tool's functionality comprehensively.
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 coverage is 100%, with each parameter having a description. The description adds no additional meaning beyond summarizing the schema. Baseline of 3 is appropriate as it repeats but does not enrich.
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 'Recommend ranked target journals for a paper from a ~1,200-venue index,' specifying the verb, resource, and scope. This directly differentiates it from sibling tools like check_duplicate_publication or find_research_gaps.
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?
Explicitly states usage context: 'Use when the user asks "where should I submit this paper" or wants to compare target venues before deciding.' This provides clear guidance, though it doesn't mention when not to use or alternatives, which is acceptable given distinct siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden. It discloses the tool's behavior: uses Science AI Journal credits, rate-limited to 10 requests/hour/IP, and describes the output composition comprehensively. This is thorough for a read-heavy research tool.
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 at four sentences, each carrying important information: purpose, outputs, usage constraints, and recommended use cases. No fluff or redundancy.
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?
Despite lacking an output schema, the description thoroughly explains what the tool returns: cross-paper synthesis gaps, single-paper gaps, field overview, up to 50 top-cited and 50 most-recent papers. This is complete for a research gap tool.
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?
The input schema already provides descriptions for all three parameters (100% coverage), so the baseline is 3. The description does not add additional semantic details beyond what the schema offers; it simply references the query and field without elaborating.
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 purpose: surfacing research gaps along with top-cited and most-recent papers. It details the outputs (cross-paper synthesis gaps, single-paper gaps, field overview, top papers) which distinguishes it from sibling tools that focus on different aspects like publication checking or writing pipeline.
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 description provides explicit use cases: 'early-stage research discovery, literature gap identification, and proposal scoping.' It also mentions rate limits and credit usage, giving clear context for when to use. It does not explicitly state when not to use or compare to alternatives, but the context is sufficient.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Despite no annotations, the description discloses that the server wraps the prompt in <manuscript> tags, prepends RAG examples when agentType is supplied, runs the Claude model, consumes Science AI Journal credits, and returns a detailed structured output. This gives the agent a clear understanding of the tool's behavior.
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 dense but not overly long; it contains no fluff. However, it could be slightly restructured for easier scanning, e.g., by separating output fields into a list. Still, every sentence earns its place.
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 the output fields and server behavior well, given the tool's complexity and lack of output schema. It might benefit from mentioning authentication requirements or potential error conditions, but it is sufficiently complete for correct agent 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?
Schema coverage is 100% with descriptions for both parameters. The description adds valuable context: it specifies that the prompt should include manuscript text plus agent-specific framing, explains the server-side handling, and clarifies the meaning of agentType and its 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 that the tool runs a HAKEM specialist agent on a prepared prompt and returns a structured editorial decision with specific fields. It names the five agent types and explicitly contrasts with the full 5-agent flow available on the web UI, distinguishing it from sibling tools.
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 description explains that this tool is for single-agent review and directs users to the web UI for the full multi-agent flow, providing a clear alternative. However, it does not outline when to use this tool versus other siblings like pre_check_paper, but the context is sufficient.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Despite no annotations, the description fully discloses behavior: it cross-references title+abstract against six sources, returns specific fields (status, confidence, message), takes ~30 seconds, and is free. No destructive actions implied, and no contradictions.
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 concise sentences with no wasted words. The first sentence states purpose and sources, the second provides output and usage advice. Front-loaded with key information.
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?
Given no output schema and no annotations, the description completely covers purpose, input significance, sources, timing, cost, return format, and usage context. No gaps identified.
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?
Parameter schema has 100% coverage with descriptions. The description adds value by explaining that abstract improves match accuracy and DOI is optional, going beyond the schema's basic field definitions.
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 purpose: a pre-submission duplicate-publication check. It specifies the verb 'check' and the resource 'duplicate publication', and distinguishes it from siblings like find_research_gaps or hakem_review_paper by focusing on cross-referencing against multiple databases.
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?
Explicitly says 'Use before submission to catch accidental duplicates or to confirm a preprint hasn't been formally published yet.' This provides clear when-to-use guidance. It does not mention when not to use or alternatives, but sibling tools do not overlap significantly.
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

Copy to your README.md:

[![science-ai-mcp-server MCP server](https://glama.ai/mcp/servers/SelfPy/science-ai-mcp-server/badges/card.svg)](https://glama.ai/mcp/servers/SelfPy/science-ai-mcp-server)

Score Badge

Copy to your README.md:

[![science-ai-mcp-server MCP server](https://glama.ai/mcp/servers/SelfPy/science-ai-mcp-server/badges/score.svg)](https://glama.ai/mcp/servers/SelfPy/science-ai-mcp-server)

Latest Blog Posts

Who's Calling? MCP Hosts Are an Identity Blind Spot (And the Spec Knows It)
By Om-Shree-0709 on July 25, 2026.
mcp
Agent Identity
OAuth 2.1
Your AI Chatbot Just Exposed Your CEO's Salary to an Intern
By Om-Shree-0709 on July 2, 2026.
Agent Identity
MCP Security
OAuth Delegation
Why MCP Servers Need Execution Sandboxing (And Why Your Current Stack Isn't Enough)
By Om-Shree-0709 on June 30, 2026.
Agentic Ai
Prompt Injection
WebAssembly

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/SelfPy/science-ai-mcp-server'

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