Skip to main content
Glama

get_introduction_section_prompt

Draft a manuscript Introduction section covering disease burden, treatment landscape, study rationale, and objective statement in AMA style using specified drug, indication, and journal.

Instructions

[PRO] Draft a manuscript Introduction/Background section. Covers disease burden, treatment landscape, study rationale, and objective statement. AMA style. No references included.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
drug_or_topicYes
indicationYes
target_journalYes
word_lengthNo400-500 words

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Implementation Reference

  • The `@mcp.tool()` decorated function that implements the `get_introduction_section_prompt` tool. It takes drug/topic, indication, target journal, and word length as parameters and returns a formatted prompt string for drafting an Introduction/Background section of a clinical manuscript.
    @mcp.tool()
    def get_introduction_section_prompt(
        drug_or_topic: str,
        indication: str,
        target_journal: str,
        word_length: str = "400-500 words"
    ) -> str:
        """
        [PRO] Draft a manuscript Introduction/Background section.
        Covers disease burden, treatment landscape, study rationale, and objective statement.
        AMA style. No references included.
        """
        return f"""Write an Introduction section for a clinical manuscript on {drug_or_topic} in {indication}.
    
    Structure:
    1. Disease burden paragraph: prevalence, unmet need, patient impact
    2. Current treatment landscape: standard of care, limitations
    3. Rationale for this study: mechanism of action, preclinical/early clinical data
    4. Study objective statement: what this paper reports
    
    Target journal: {target_journal}
    Length: {word_length}
    Do not include references.
    
    Pro tip: Add 'write in active voice where appropriate per AMA style' for cleaner prose."""
  • server.py:980-980 (registration)
    Tool listed in the pro_tools list for documentation/registration purposes with description 'Draft manuscript Introduction/Background section'.
    ("get_introduction_section_prompt", "Draft manuscript Introduction/Background section"),
  • server.py:246-246 (registration)
    Registration via the @mcp.tool() decorator from FastMCP, which registers this function as an MCP tool automatically.
    @mcp.tool()
  • Type annotations define the input schema: drug_or_topic (str), indication (str), target_journal (str), word_length (str, default '400-500 words'). Return type is str.
    def get_introduction_section_prompt(
        drug_or_topic: str,
        indication: str,
        target_journal: str,
        word_length: str = "400-500 words"
    ) -> str:
Behavior4/5

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

No annotations exist, so description carries full burden. It states the tool covers disease burden, treatment landscape, study rationale, objective statement, AMA style, and explicitly notes 'No references included'. This provides key behavioral context, though it does not clarify that the output is a prompt (rather than a drafted section).

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 (5 lines) with front-loaded main action. Every sentence adds value: purpose, content coverage, style, and an exclusion note. No superfluous 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 4 parameters (3 required), output schema present, and siblings indicating a family of prompt generators, the description covers core intent and style but lacks parameter explanations and usage context, making it minimally complete.

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

Parameters1/5

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

Schema description coverage is 0% and the description gives no information about the 4 parameters (drug_or_topic, indication, target_journal, word_length). The description must compensate for the lack of schema descriptions but does not, leaving the agent with no parameter guidance.

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 verb 'Draft' and resource 'manuscript Introduction/Background section'. The description distinguishes from siblings like get_discussion_section_prompt by specifying content coverage (disease burden, treatment landscape, etc.) and style (AMA, no 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?

Usage is implied by the name and context (sibling tools are all section prompts), but there is no explicit guidance on when to use vs. alternatives, nor any exclusion criteria or prerequisites.

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

Other Tools

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/pubspro/medwriter-mcp'

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