Skip to main content
Glama

get_genotox_details

Retrieve detailed genotoxicity study data including test methods, guidelines, species, exposure conditions, and results to assess DNA damage potential of substances.

Instructions

Get genotoxicity study details from GENOTOX table by GENOTOX_ID.

Retrieves detailed genotoxicity study records including test methods, guidelines,
and results. This tool provides the experimental data used to assess whether
substances have genotoxic or mutagenic properties that could cause DNA damage.

Args:
    genotox_id: Single GENOTOX_ID (int) or list of GENOTOX_IDs (list[int]) to query.
               Use search_substance tool first to find GENOTOX_IDs through the STUDY table.

Returns:
    JSON string containing a DataFrame with genotoxicity study records. Each record
    includes study category, test guidelines, species, exposure conditions, and
    genotoxicity results.

The returned data includes:
- Study category: STUDY_CATEGORY
- Test guidelines: GENOTOXGUIDELINE, GENOTOXGUIDELINEFULLTXT
- Test conditions: SPECIES, STRAIN, SEX, ROUTE, EXP_PERIOD, EXPPERIODUNIT
- Genotoxicity result: IS_GENOTOXIC
- Study quality: GLP_COMPL, DEVIATION
- Study details: NUMBER_INDIVIDUALS, CONTROL, MET_INDICATOR, REMARKS

Note: Multiple records may be returned if multiple GENOTOX_IDs are provided.
Genotoxicity studies are critical for assessing cancer risk and mutagenic potential.

<dictionary_descriptions>
<name>STUDY_CATEGORY</name>
<description>Mutagenicity or genotoxicity study</description>
<name>SPECIES</name>
<description>Description of the organism/cell culture tested</description>
<name>SEX</name>
<description>Sex of the tested animals in vivo genotoxicity study</description>
<name>ROUTE</name>
<description>Description of the route of administration</description>
<name>NUMBER_INDIVIDUALS</name>
<description>Number of organisms dosed at each dose level of the in vivo genotoxicty study</description>
<name>CONTROL</name>
<description>Indicates whether and what type of concurrent control groups were used in in vivo genotoxicity study</description>
<name>IS_GENOTOXIC</name>
<description>Positive or negative result</description>
<name>REMARKS</name>
<description>Remarks on genotoxicity study</description>
</dictionary_descriptions>

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
genotox_idYes

Implementation Reference

  • The handler function for the 'get_genotox_details' tool. It takes a genotox_id (int or list[int]), queries the database using query_by_id, and returns the results as JSON. Includes comprehensive docstring describing inputs, outputs, and data fields.
    def get_genotox_details(genotox_id: Union[int, list[int]]):
        """
        Get genotoxicity study details from GENOTOX table by GENOTOX_ID.
    
        Retrieves detailed genotoxicity study records including test methods, guidelines,
        and results. This tool provides the experimental data used to assess whether
        substances have genotoxic or mutagenic properties that could cause DNA damage.
    
        Args:
            genotox_id: Single GENOTOX_ID (int) or list of GENOTOX_IDs (list[int]) to query.
                       Use search_substance tool first to find GENOTOX_IDs through the STUDY table.
    
        Returns:
            JSON string containing a DataFrame with genotoxicity study records. Each record
            includes study category, test guidelines, species, exposure conditions, and
            genotoxicity results.
    
        The returned data includes:
        - Study category: STUDY_CATEGORY
        - Test guidelines: GENOTOXGUIDELINE, GENOTOXGUIDELINEFULLTXT
        - Test conditions: SPECIES, STRAIN, SEX, ROUTE, EXP_PERIOD, EXPPERIODUNIT
        - Genotoxicity result: IS_GENOTOXIC
        - Study quality: GLP_COMPL, DEVIATION
        - Study details: NUMBER_INDIVIDUALS, CONTROL, MET_INDICATOR, REMARKS
    
        Note: Multiple records may be returned if multiple GENOTOX_IDs are provided.
        Genotoxicity studies are critical for assessing cancer risk and mutagenic potential.
    
        <dictionary_descriptions>
        <name>STUDY_CATEGORY</name>
        <description>Mutagenicity or genotoxicity study</description>
        <name>SPECIES</name>
        <description>Description of the organism/cell culture tested</description>
        <name>SEX</name>
        <description>Sex of the tested animals in vivo genotoxicity study</description>
        <name>ROUTE</name>
        <description>Description of the route of administration</description>
        <name>NUMBER_INDIVIDUALS</name>
        <description>Number of organisms dosed at each dose level of the in vivo genotoxicty study</description>
        <name>CONTROL</name>
        <description>Indicates whether and what type of concurrent control groups were used in in vivo genotoxicity study</description>
        <name>IS_GENOTOXIC</name>
        <description>Positive or negative result</description>
        <name>REMARKS</name>
        <description>Remarks on genotoxicity study</description>
        </dictionary_descriptions>
        """
        df = query_by_id(genotox_id, "genotox")
        return df.to_json()
  • main.py:21-21 (registration)
    Registration of the get_genotox_details tool with the FastMCP server.
    mcp.add_tool(get_genotox_details)
  • main.py:5-5 (registration)
    Import of the get_genotox_details tool function in main.py for registration.
    from src.mcp_openfoodtox.tools.get_genotox_details import get_genotox_details
Behavior4/5

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 effectively describes the tool's behavior: it retrieves detailed records, can handle single or multiple IDs, returns JSON with a DataFrame structure, and notes that multiple records may be returned for multiple IDs. It lacks explicit details on error handling or rate limits, but covers core operational aspects well.

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 well-structured with clear sections (purpose, args, returns, data fields, notes) and uses bullet points for readability. It is appropriately sized for the complexity, though the dictionary descriptions section is somewhat redundant with the earlier bullet points, slightly reducing efficiency.

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 the tool's complexity (1 parameter with 0% schema coverage, no annotations, no output schema), the description is highly complete. It covers purpose, usage, parameters, return format, detailed field explanations, and operational notes. The dictionary descriptions provide additional semantic clarity, ensuring the agent has all necessary context to use the tool effectively.

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?

The schema description coverage is 0%, so the description must fully compensate. It provides detailed parameter semantics: explains that 'genotox_id' can be a single integer or list of integers, clarifies its purpose (querying by GENOTOX_ID), and includes usage context (finding IDs via search_substance). This adds substantial 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 tool 'retrieves detailed genotoxicity study records' from the GENOTOX table by GENOTOX_ID, specifying the exact resource and action. It distinguishes itself from sibling tools like 'search_substance' by focusing on detailed study data rather than substance searching or listing.

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?

The description explicitly provides usage guidance: it instructs to 'Use search_substance tool first to find GENOTOX_IDs through the STUDY table,' naming a specific alternative tool for prerequisite steps. This gives clear context on when and how to use this tool in relation to siblings.

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

Install Server

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/spyrosze/mcp-openfoodtox'

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