Provides a custom node for n8n workflows that enables access to FishBase marine biology data, allowing users to search for species information, get ecological and morphological data, and validate species names within their automation workflows.
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@MCP FishBase Server what's the scientific name for Atlantic cod?"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
A Model Context Protocol (MCP) server that provides access to FishBase marine biology data, with n8n integration.
Access to FishBase species data
Ecological information
Distribution/occurrence data
Morphological data
Species name validation
Common name to scientific name conversion
Related MCP server: ERDDAP MCP Server
cd mcp_fishbase
npm install
npm run build
npm run startcd n8n-integration
npm install
npm run buildThen install the node package in your n8n instance:
npm install /path/to/n8n-integrationget_species: Get detailed species information
search_species: Search for species by name
get_ecology: Get ecological data for a species
get_distribution: Get distribution/occurrence data
get_morphology: Get morphological data
validate_species_name: Validate and suggest corrections for species names
common_to_scientific: Convert common names to scientific names
list_tables: List all available FishBase tables
Add the FishBase node to your workflow
Configure the operation (e.g., "Get Species Info")
Provide the species name (e.g., "Salmo trutta")
Connect to downstream nodes for data processing
{
"SpecCode": 1,
"Genus": "Salmo",
"Species": "trutta",
"FBname": "Brown trout",
"Length": 100,
"CommonLength": 30,
"Weight": 17000
}The MCP server uses mock data for development. In production, you would integrate with the actual FishBase HuggingFace datasets using a proper Parquet parser.
MIT
Convert common name to scientific name
| Name | Required | Description | Default |
|---|---|---|---|
| common_name | Yes | Common name of the fish |
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 states the conversion action but doesn't describe key behaviors: whether it's case-sensitive, handles partial matches, returns errors for invalid names, or provides additional metadata. For a tool with zero annotation coverage, this leaves significant gaps in understanding how it operates.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise with a single, clear sentence that directly states the tool's purpose. There is zero wasted language or redundancy, and it's front-loaded with the core functionality. Every word earns its place in conveying the essential action.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (a conversion function with no annotations and no output schema), the description is incomplete. It doesn't explain what the output looks like (e.g., scientific name format, error responses), behavioral traits, or how it interacts with sibling tools. For a tool that performs a specific transformation, more context is needed to guide effective use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 100%, with the single parameter 'common_name' fully documented in the schema as 'Common name of the fish'. The description adds no additional parameter semantics beyond what the schema provides, such as format examples or constraints. Given the high schema coverage, the baseline score of 3 is appropriate as the description doesn't compensate but also doesn't detract.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's function as converting common names to scientific names, using specific verbs ('convert') and resources ('common name', 'scientific name'). It distinguishes itself from siblings like 'validate_species_name' or 'search_species' by focusing on name conversion rather than validation or broader searching. However, it doesn't explicitly mention the biological domain (fish) that the input schema implies.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
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 sibling tools like 'validate_species_name' (which might check name validity) or 'search_species' (which might return broader information), nor does it specify prerequisites such as needing a valid common name. Usage is implied but not explicitly defined.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get distribution/occurrence information for a species
| Name | Required | Description | Default |
|---|---|---|---|
| species_name | Yes | Scientific name of the species |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden for behavioral disclosure. While 'Get' implies a read operation, the description doesn't specify whether this requires authentication, has rate limits, returns structured or unstructured data, provides pagination for large result sets, or indicates what happens with invalid species names. For a tool with zero annotation coverage, this leaves significant behavioral questions unanswered.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is perfectly concise - a single sentence that directly states the tool's purpose with zero wasted words. It's front-loaded with the core functionality and doesn't include unnecessary elaboration. Every word earns its place in this minimal but complete statement of purpose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the lack of annotations and output schema, the description is insufficiently complete. For a tool that presumably returns distribution data (which could be complex - geographic ranges, occurrence points, temporal patterns), the description doesn't indicate what format the information comes in, whether it includes metadata, or how comprehensive the results are. The agent has no guidance on what to expect from this tool's output.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description doesn't add any parameter-specific information beyond what's already in the schema. With 100% schema description coverage (the single parameter 'species_name' has a clear description in the schema), the baseline score of 3 is appropriate. The description doesn't elaborate on format expectations (e.g., 'Homo sapiens' vs 'homo sapiens'), validation behavior, or provide usage examples.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Get distribution/occurrence information for a species' - a specific verb ('Get') and resource ('distribution/occurrence information') with a clear target ('for a species'). It distinguishes from some siblings like 'get_ecology' or 'get_morphology' by focusing on distribution rather than other biological aspects. However, it doesn't explicitly differentiate from 'get_species' or 'search_species' which might also provide distribution data.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
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. With siblings like 'get_species', 'search_species', and 'get_ecology' available, there's no indication whether this tool provides more detailed distribution data, different data formats, or serves a distinct use case. The agent must infer usage from the tool name alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get ecological information for a species
| Name | Required | Description | Default |
|---|---|---|---|
| species_name | Yes | Scientific name of the species |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden for behavioral disclosure. It states a read operation ('Get'), implying it's likely non-destructive, but doesn't address critical aspects like authentication needs, rate limits, error conditions, or what 'ecological information' specifically entails (e.g., habitat, diet, conservation status). This leaves significant gaps for safe and effective use.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence with zero wasted words. It's front-loaded with the core purpose ('Get ecological information'), making it immediately scannable and appropriately sized for a simple tool with one parameter.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the lack of annotations and output schema, the description is incomplete for effective use. It doesn't clarify what 'ecological information' includes (e.g., structured data vs. text), how it relates to sibling tools, or behavioral constraints. For a tool in a rich sibling context with no structured safety or output guidance, this leaves too many open questions.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with the single parameter 'species_name' clearly documented in the schema as 'Scientific name of the species'. The description adds no additional parameter semantics beyond implying the tool operates on a species, which the schema already covers. Baseline 3 is appropriate when the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with a specific verb ('Get') and resource ('ecological information for a species'), making it immediately understandable. However, it doesn't explicitly differentiate from sibling tools like 'get_distribution' or 'get_morphology', which likely provide related but different ecological data subsets.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
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. With siblings like 'get_distribution' (likely for geographic data) and 'get_morphology' (likely for physical traits), there's clear potential for overlap or confusion, but the description offers no explicit when/when-not instructions or named alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get morphological and physiological data for a species
| Name | Required | Description | Default |
|---|---|---|---|
| species_name | Yes | Scientific name of the species |
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 retrieves data but does not specify whether it's a read-only operation, if it requires authentication, potential rate limits, or what the output format might be (e.g., structured data, text). This leaves key behavioral traits unclear for a tool with no 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.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, clear sentence that directly states the tool's purpose without unnecessary words. It is front-loaded and efficiently conveys the core functionality, making it easy to parse and understand quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the lack of annotations and output schema, the description is incomplete for a data retrieval tool. It does not explain what type of data is returned (e.g., JSON structure, text summary), potential errors, or how it differs from sibling tools, leaving gaps that could hinder an agent's ability to use it effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 100%, with the single parameter 'species_name' fully documented in the schema as 'Scientific name of the species'. The description adds no additional meaning or context beyond this, such as examples or constraints, so it meets the baseline for high schema coverage without compensating further.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Get') and the resource ('morphological and physiological data for a species'), making the purpose understandable. However, it does not explicitly differentiate this tool from sibling tools like 'get_species' or 'get_ecology', which might also retrieve species-related data, leaving some ambiguity about its specific scope.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
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, such as 'get_species' or 'get_ecology', nor does it mention any prerequisites or exclusions. Without such context, an agent must infer usage based on the tool name alone, which is insufficient for optimal selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get species information from FishBase
| Name | Required | Description | Default |
|---|---|---|---|
| species_name | Yes | Scientific name of the species (e.g., 'Salmo trutta') | |
| fields | No | Optional list of specific fields to return |
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 but offers minimal information. It states what the tool does but doesn't describe response format, error handling, rate limits, authentication needs, or whether it's a read-only operation. For a tool with zero annotation coverage, this is inadequate.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence with zero wasted words. It's appropriately sized for a simple lookup tool and front-loads the core purpose immediately.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (2 parameters, no output schema, no annotations), the description is incomplete. It doesn't explain what 'species information' includes, how results are structured, or address behavioral aspects like error cases. Without annotations or output schema, the description should provide more context about the operation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents both parameters thoroughly. The description doesn't add any parameter-specific information beyond what's in the schema, such as explaining field options or providing examples beyond the schema's 'species_name' example. This meets the baseline for high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Get') and resource ('species information from FishBase'), providing a specific purpose. However, it doesn't differentiate this tool from sibling tools like 'search_species' or 'get_ecology', which would require explicit comparison to achieve a perfect score.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
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 like 'search_species' or other 'get_' siblings. It lacks any context about use cases, prerequisites, or exclusions, leaving the agent to infer usage from the tool name alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List all available FishBase tables
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It states the action but lacks behavioral details such as whether this is a read-only operation, if it requires authentication, what the output format looks like (e.g., list of table names), or any rate limits. This leaves significant gaps for an agent to understand how to handle the tool effectively.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, clear sentence with zero waste—it directly states the tool's purpose without fluff. It is front-loaded and appropriately sized for a simple, parameterless tool, making it highly efficient.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (0 parameters, no output schema, no annotations), the description is minimally adequate. It explains what the tool does but lacks context on output format or integration with sibling tools. For a metadata-listing tool, this is a basic but functional description that could be more complete by addressing its role in the broader toolset.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has 0 parameters, and schema description coverage is 100%, so no parameter documentation is needed. The description appropriately avoids redundant parameter info, earning a baseline score of 4 for not adding unnecessary details beyond the empty schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb ('List') and resource ('all available FishBase tables'), making the purpose unambiguous. However, it does not differentiate from sibling tools, which are all about species data rather than table metadata, so it misses an opportunity to clarify its distinct role in the toolset.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
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. Given the sibling tools focus on species-related queries (e.g., get_species, search_species), it's unclear if this tool is for metadata exploration or a prerequisite for other operations, leaving usage context implied at best.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Search for species by common name or partial scientific name
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | Search term (common name or partial scientific name) | |
| limit | No | Maximum number of results to return (default: 20) |
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. While it indicates this is a search operation, it lacks details on permissions, rate limits, pagination, error handling, or the format of results. For a search tool with zero annotation coverage, this leaves significant gaps in understanding how the tool behaves beyond its basic function.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
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 any redundant or unnecessary words. It is front-loaded with the core action and resource, making it easy to parse quickly. Every part of the sentence earns its place by conveying essential information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the lack of annotations and output schema, the description is incomplete for a search tool. It doesn't explain what the results look like (e.g., list of species with fields), how to handle large result sets, or any behavioral constraints. While the purpose is clear, the overall context for effective use by an AI agent is insufficient.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 100%, meaning the input schema fully documents both parameters ('query' and 'limit') with clear descriptions and defaults. The description adds no additional parameter semantics beyond what's in the schema, such as search syntax or result ordering. According to the rules, with high schema coverage (>80%), the baseline score is 3.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: searching for species using either common names or partial scientific names. It specifies the verb 'search' and the resource 'species', making the intent unambiguous. However, it doesn't explicitly differentiate this from sibling tools like 'get_species' or 'validate_species_name', which prevents a perfect score.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
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 sibling tools like 'get_species' (which might retrieve specific species) or 'validate_species_name' (which might check name validity), nor does it specify any prerequisites, exclusions, or contextual cues for choosing this search function.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Validate and correct species scientific names
| Name | Required | Description | Default |
|---|---|---|---|
| species_name | Yes | Scientific name to validate |
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 states the tool 'validate[s] and correct[s]' but doesn't explain what validation entails (e.g., checks for taxonomic rules, format), how corrections are applied (e.g., suggests alternatives, auto-fixes), or behavioral traits like error handling, rate limits, or permissions required. This leaves significant gaps for a tool that modifies input.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence with zero wasted words. It is front-loaded with the core purpose ('Validate and correct species scientific names'), making it easy to parse. Every word earns its place by directly contributing to understanding the tool's function.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (involving validation and correction of scientific names) and lack of annotations and output schema, the description is incomplete. It doesn't cover what validation checks are performed, how corrections are returned, error cases, or output format. For a tool that likely returns corrected names or validation results, this omission is significant.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, with the single parameter 'species_name' documented as 'Scientific name to validate'. The description adds no additional meaning beyond this, such as format examples (e.g., binomial nomenclature) or correction specifics. Since schema coverage is high, the baseline score of 3 is appropriate, as the description doesn't compensate but doesn't detract either.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with specific verbs ('validate and correct') and identifies the resource ('species scientific names'). It distinguishes itself from siblings like 'common_to_scientific' (conversion) or 'get_species' (retrieval) by focusing on validation/correction. However, it doesn't explicitly differentiate from all siblings (e.g., 'search_species' might involve validation).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
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 prerequisites (e.g., input format expectations), exclusions, or comparisons to siblings like 'common_to_scientific' for name conversion or 'search_species' for validation during search. Usage is implied from the purpose but not explicitly stated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
common_to_scientificget_distributionget_ecologyget_morphologyget_specieslist_tablessearch_speciesvalidate_species_nameEach tool has a clearly distinct purpose with no overlap: conversion, distribution, ecology, morphology, species retrieval, table listing, species search, and name validation. The descriptions make it easy for an agent to select the right tool for each task without confusion.
All tools follow a consistent verb_noun pattern (e.g., get_distribution, search_species, validate_species_name), with clear and predictable naming. There are no deviations in style or convention across the set.
With 8 tools, the server is well-scoped for its FishBase domain, covering key operations like retrieval, search, validation, and data access. Each tool serves a specific function without redundancy, making the count appropriate and manageable.
The tool set provides comprehensive coverage for querying and validating fish species data, including retrieval, search, and various data types. A minor gap might be the lack of tools for updating or managing data, but for a read-only FishBase interface, it is largely complete.
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