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., "@Reactome MCP Server find pathways containing BRCA1"
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.
Model Context Protocol server for accessing Reactome pathway and systems biology data.
Developed by Augmented Nature - Advancing AI for Scientific Discovery
All 8 tools working with live Reactome API data:
🔍 Pathway Search - Search biological pathways by name, process, keywords
📊 Pathway Details - Comprehensive pathway information and components
🧬 Gene-to-Pathways - Find pathways containing specific genes/proteins
🦠 Disease Pathways - Disease-associated biological mechanisms
🌲 Pathway Hierarchy - Parent/child relationships and pathway structure
🧪 Pathway Participants - All molecules participating in pathways
⚗️ Biochemical Reactions - Detailed reaction information
🔗 Protein Interactions - Molecular interactions within pathways
Related MCP server: Reexpress MCP Server
# Install and build
npm install
npm run build
# Run the server
node build/index.js{
"mcpServers": {
"reactome-server": {
"command": "node",
"args": ["/path/to/reactome-server/build/index.js"]
}
}
}node /path/to/reactome-server/build/index.jssearch_pathwaysSearch for biological pathways by name, description, or keywords
{
"name": "search_pathways",
"arguments": {
"query": "cell cycle", // Pathway name, process, or keywords
"type": "pathway", // Optional: pathway, reaction, protein, complex, disease
"size": 20 // Optional: 1-100 results (default: 20)
}
}Example Results:
Cell Cycle (R-HSA-1640170) - Cell cycle progression and regulation
Cell Cycle Checkpoints (R-HSA-69620) - Quality control mechanisms
Mitotic G1-G1/S phases (R-HSA-453279) - G1 phase progression
get_pathway_detailsGet comprehensive information about a specific pathway
{
"name": "get_pathway_details",
"arguments": {
"id": "R-HSA-1640170" // Reactome pathway stable identifier
}
}find_pathways_by_geneFind all pathways containing a specific gene or protein
{
"name": "find_pathways_by_gene",
"arguments": {
"gene": "BRCA1", // Gene symbol or UniProt ID
"species": "Homo sapiens" // Optional: species (default: Homo sapiens)
}
}find_pathways_by_diseaseFind disease-associated pathways and mechanisms
{
"name": "find_pathways_by_disease",
"arguments": {
"disease": "cancer", // Disease name or DOID identifier
"size": 25 // Optional: 1-100 pathways (default: 25)
}
}get_pathway_hierarchyGet hierarchical structure and parent/child relationships
{
"name": "get_pathway_hierarchy",
"arguments": {
"id": "R-HSA-1640170" // Reactome pathway stable identifier
}
}get_pathway_participantsGet all molecules (proteins, genes, compounds) in a pathway
{
"name": "get_pathway_participants",
"arguments": {
"id": "R-HSA-1640170" // Reactome pathway stable identifier
}
}get_pathway_reactionsGet all biochemical reactions within a pathway
{
"name": "get_pathway_reactions",
"arguments": {
"id": "R-HSA-1640170" // Reactome pathway stable identifier
}
}get_protein_interactionsGet protein-protein interactions within pathways
{
"name": "get_protein_interactions",
"arguments": {
"pathwayId": "R-HSA-1640170", // Reactome pathway stable identifier
"interactionType": "all" // Optional: protein-protein, regulatory, catalysis, all
}
}Access Reactome data through standardized URIs:
reactome://pathway/{id} - Complete pathway information
reactome://reaction/{id} - Detailed reaction information
reactome://protein/{id} - Protein details and associations
reactome://disease/{id} - Disease-associated pathways
reactome://search/{query} - Search results
# 1. Search for DNA repair pathways
{"name": "search_pathways", "arguments": {"query": "DNA repair", "size": 10}}
# 2. Get detailed pathway information
{"name": "get_pathway_details", "arguments": {"id": "R-HSA-5696394"}}
# 3. Find all pathways containing BRCA1
{"name": "find_pathways_by_gene", "arguments": {"gene": "BRCA1"}}
# 4. Get pathway participants
{"name": "get_pathway_participants", "arguments": {"id": "R-HSA-5696394"}}# 1. Search for cancer-related pathways
{"name": "find_pathways_by_disease", "arguments": {"disease": "cancer", "size": 15}}
# 2. Get pathway hierarchy for oncogenic signaling
{"name": "get_pathway_hierarchy", "arguments": {"id": "R-HSA-5637815"}}
# 3. Analyze biochemical reactions
{"name": "get_pathway_reactions", "arguments": {"id": "R-HSA-5637815"}}# 1. Find pathways for drug target
{"name": "find_pathways_by_gene", "arguments": {"gene": "EGFR"}}
# 2. Get protein interactions in pathway
{"name": "get_protein_interactions", "arguments": {"pathwayId": "R-HSA-177929"}}
# 3. Analyze pathway participants
{"name": "get_pathway_participants", "arguments": {"id": "R-HSA-177929"}}Reactome provides curated data for:
25,000+ reactions across all major biological processes
14,000+ proteins with detailed functional annotations
2,500+ pathways covering cellular and molecular processes
20+ species including human, mouse, rat, and model organisms
Cross-references to UniProt, ChEMBL, Ensembl, and other databases
Key Biological Areas:
Signal transduction pathways
Metabolic processes and networks
Gene regulation and expression
Cell cycle and DNA repair
Immune system responses
Disease mechanisms and drug action
Developmental biology processes
TypeScript implementation with robust type safety
Reactome Content Service API for efficient data retrieval
MCP Protocol compliant JSON-RPC communication
Error Handling with comprehensive validation
Production Ready with 30s timeouts and proper logging
Base URL: https://reactome.org/ContentService
Version: Reactome v79 (latest)
Rate Limits: Generous for research use
Authentication: None required
Format: REST API with JSON responses
Fork the repository
Make your changes
Submit a pull request
If you use this project in your research or publications, please cite it as follows:
author = {Moudather Chelbi},
title = {Reactome MCP Server},
year = {2025},
howpublished = {https://github.com/Augmented-Nature/Reactome-MCP-Server},
note = {Accessed: 2025-06-29}Find disease-associated pathways and mechanisms
| Name | Required | Description | Default |
|---|---|---|---|
| disease | Yes | Disease name or DOID identifier | |
| size | No | Number of pathways to return (1-100, default: 25) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden but offers minimal behavioral insight. It doesn't disclose whether this is a read-only operation, what data sources are used, potential rate limits, or what the output format looks like. 'Find' implies querying, but no further context is given.
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 and appropriately sized for a straightforward query tool.
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?
For a query tool with no annotations and no output schema, the description is insufficient. It doesn't explain what 'pathways and mechanisms' means in this context, what data is returned, or how results are structured, leaving significant gaps for agent understanding.
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 adds no additional parameter meaning beyond what's in the schema, maintaining the baseline score of 3 for adequate coverage through structured data alone.
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 'find' and the resource 'disease-associated pathways and mechanisms', making the purpose immediately understandable. It distinguishes from siblings like find_pathways_by_gene by specifying disease focus, though it doesn't explicitly contrast with other tools like search_pathways or get_pathway_hierarchy.
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 when to choose it over search_pathways or get_pathway_hierarchy, nor does it specify prerequisites like needing a valid disease identifier or typical use cases.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Find all pathways containing a specific gene or protein
| Name | Required | Description | Default |
|---|---|---|---|
| gene | Yes | Gene symbol or UniProt ID (e.g., BRCA1, P04637) | |
| species | No | Species name or taxon ID (default: Homo sapiens) |
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 what the tool does but doesn't describe how it behaves—e.g., whether it returns a list, how results are formatted, if there are rate limits, or error conditions. For a query tool with zero annotation coverage, this leaves significant gaps in understanding operational behavior.
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 function without redundancy or fluff. It's appropriately sized and front-loaded, making it easy for an agent 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.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (querying pathways with gene input), lack of annotations, and no output schema, the description is incomplete. It doesn't explain return values, result structure, or potential limitations. For a tool that likely returns structured data, this omission hinders the agent's ability to use it effectively without trial and error.
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 ('gene' and 'species') with examples. The description adds no additional parameter semantics beyond what's in the schema, such as format details or constraints. Baseline 3 is appropriate when the schema does the heavy lifting, but the description doesn't compensate or enhance parameter understanding.
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 as finding pathways containing a specific gene or protein, which is a specific verb+resource combination. However, it doesn't explicitly differentiate from sibling tools like 'search_pathways' or 'get_pathway_participants', which might have overlapping functionality. The description is accurate but lacks sibling distinction.
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 'find_pathways_by_disease' or 'search_pathways'. It doesn't mention prerequisites, exclusions, or comparative contexts. The agent must infer usage from the name alone, which is insufficient for optimal tool selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get comprehensive information about a specific pathway
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes | Reactome pathway stable identifier (e.g., R-HSA-68886) |
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 'comprehensive information' but doesn't specify what that includes, whether it's read-only, if there are rate limits, or how errors are handled. This leaves significant gaps in understanding the tool's behavior beyond basic purpose.
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 front-loads the core purpose ('Get comprehensive information about a specific pathway') with zero wasted words. It's appropriately sized for a simple tool with one parameter, making it easy to parse 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. It doesn't explain what 'comprehensive information' entails, such as data structure, fields, or potential errors, which is crucial for a tool with no structured output documentation. This leaves the agent with insufficient 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.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, clearly documenting the single required parameter 'id' as a Reactome pathway stable identifier. The description adds no additional parameter semantics beyond what the schema provides, such as examples of comprehensive information returned, 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 tool's purpose with a specific verb ('Get') and resource ('comprehensive information about a specific pathway'), making it easy to understand what the tool does. However, it doesn't explicitly differentiate from sibling tools like 'get_pathway_hierarchy' or 'get_pathway_participants', which might provide overlapping or related information about pathways.
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, such as needing a pathway ID, or compare it to siblings like 'find_pathways_by_disease' or 'search_pathways', leaving the agent to infer usage based on the name alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get hierarchical structure and parent/child relationships for a pathway
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes | Reactome pathway stable identifier |
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 hierarchical structure and relationships, implying a read-only operation, but doesn't specify if it's safe, has rate limits, requires authentication, or what the output format looks like. This is a significant gap for a tool with no annotation coverage.
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 unnecessary words. It's appropriately sized for a simple tool, though it could be slightly more front-loaded with additional context to improve clarity.
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. It doesn't explain what the hierarchical structure looks like, how relationships are represented, or any potential errors or limitations. For a tool that likely returns complex data, this leaves significant gaps for an AI agent to understand its behavior.
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 'id' parameter documented as a 'Reactome pathway stable identifier'. The description doesn't add any extra meaning beyond this, such as examples or constraints, so it meets the baseline score of 3 where 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 ('hierarchical structure and parent/child relationships for a pathway'), making it easy to understand what it does. However, it doesn't explicitly differentiate from sibling tools like 'get_pathway_details' or 'search_pathways', which might also provide structural information, so it doesn't reach the highest 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 prerequisites, context for usage, or compare it to sibling tools like 'get_pathway_details' or 'search_pathways', leaving the agent to infer usage based on the name alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get all molecules (proteins, genes, compounds) participating in a pathway
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes | Reactome pathway stable identifier |
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 'gets' data, implying a read-only operation, but doesn't clarify permissions, rate limits, pagination, or the format of returned data. For a tool with no annotations, this leaves significant behavioral gaps.
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 unnecessary words. It is appropriately sized and front-loaded, making it easy for an agent to parse 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 no annotations and no output schema, the description is incomplete. It doesn't explain what the return value looks like (e.g., list format, data structure) or address potential complexities like large result sets. For a tool with minimal structured data, 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?
Schema description coverage is 100%, with the single parameter 'id' documented as a 'Reactome pathway stable identifier'. The description adds no additional parameter semantics beyond this, such as examples or constraints, so it meets the baseline for high schema coverage without compensating value.
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 ('all molecules participating in a pathway'), and specifies the types of molecules included (proteins, genes, compounds). However, it doesn't explicitly differentiate from sibling tools like 'get_pathway_details' or 'get_pathway_reactions', which might also return pathway-related information.
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, exclusions, or compare it to sibling tools such as 'find_pathways_by_gene' or 'get_pathway_reactions', leaving the agent to infer usage context 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 all biochemical reactions within a pathway
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes | Reactome pathway stable identifier |
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 'Get' implies a read operation, the description doesn't address important behavioral aspects like whether this returns all reactions at once or uses pagination, what format the reactions are returned in, whether there are rate limits, or what happens with invalid pathway identifiers. For a read operation with zero annotation coverage, this is insufficient.
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 communicates the core purpose without unnecessary words. It's appropriately sized for a simple retrieval tool and front-loads the 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?
For a tool with no annotations and no output schema, the description is incomplete. It doesn't explain what format the reactions are returned in, whether there are limitations on the data returned, or what constitutes a 'biochemical reaction' in this context. The agent would need to guess about the output structure and behavioral characteristics.
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 'id' clearly documented as 'Reactome pathway stable identifier.' The description adds no additional parameter information beyond what's already in the schema. With complete schema coverage, the baseline score of 3 is appropriate.
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 all biochemical reactions') and target resource ('within a pathway'), providing a specific verb+resource combination. However, it doesn't differentiate this tool from potential sibling tools like 'get_pathway_details' or 'get_pathway_participants' that might also retrieve pathway-related information.
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 about when to use this tool versus alternatives. With multiple sibling tools available (find_pathways_by_disease, get_pathway_details, get_pathway_hierarchy, etc.), there's no indication of when this specific reaction-focused query is appropriate versus other pathway-related queries.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get protein-protein interactions within pathways
| Name | Required | Description | Default |
|---|---|---|---|
| pathwayId | Yes | Reactome pathway stable identifier | |
| interactionType | No | Type of interactions to retrieve (default: all) |
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 what the tool does but doesn't cover critical aspects like whether this is a read-only operation, potential rate limits, error conditions, or the format of returned data. This is a significant gap for a tool with no annotation coverage.
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 waste. It's front-loaded with the core purpose and appropriately sized for the tool's complexity, making it easy for an agent to parse 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. It doesn't explain what the tool returns (e.g., list of interactions, structured data), potential limitations, or how it fits with sibling tools. For a tool with no structured behavioral hints, more context is needed to be fully helpful.
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%, so the schema already documents both parameters thoroughly. The description doesn't add any additional meaning beyond what's in the schema, such as explaining the significance of 'pathwayId' or 'interactionType' in biological terms. 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 verb 'Get' and the resource 'protein-protein interactions within pathways', making the purpose immediately understandable. However, it doesn't explicitly differentiate from sibling tools like 'get_pathway_participants' or 'get_pathway_reactions', which might also involve pathway components, so it doesn't reach the highest 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 prerequisites, context, or compare to sibling tools like 'get_pathway_participants' or 'search_pathways', leaving the agent to infer usage based on the name alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Search for biological pathways by name, description, or keywords
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | Search query (pathway name, process, keywords) | |
| type | No | Type of entity to search for (default: pathway) | |
| size | No | Number of results to return (1-100, 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 mentions what can be searched (name, description, keywords), it doesn't describe important behavioral aspects like whether this is a read-only operation, what format results are returned in, whether there are rate limits, authentication requirements, or how search results are ranked/ordered.
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 - a single sentence that directly states the tool's function without any unnecessary words. It's front-loaded with the core purpose and efficiently communicates the search scope in minimal text.
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?
For a search tool with 3 parameters, no annotations, and no output schema, the description is insufficient. It doesn't explain what kind of results to expect, how they're structured, whether there's pagination, or any error conditions. The description provides basic purpose but lacks the contextual information needed for effective tool 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?
With 100% schema description coverage, the input schema already documents all three parameters thoroughly. The description mentions searching by 'name, description, or keywords' which aligns with the 'query' parameter, but doesn't add meaningful semantic context beyond what's already in the schema descriptions for 'type' and 'size' parameters.
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 as searching for biological pathways using specific criteria (name, description, or keywords). It uses a specific verb ('search') and identifies the resource ('biological pathways'), but it doesn't explicitly differentiate from sibling tools like 'find_pathways_by_disease' or 'find_pathways_by_gene' which have more specialized search functions.
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 the available alternatives. With sibling tools like 'find_pathways_by_disease' and 'find_pathways_by_gene' that perform more targeted searches, the description fails to indicate whether this is a general-purpose search tool or how it differs from those specialized options.
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.
find_pathways_by_diseasefind_pathways_by_geneget_pathway_detailsget_pathway_hierarchyget_pathway_participantsget_pathway_reactionsget_protein_interactionssearch_pathwaysEvery tool has a clearly distinct purpose with no ambiguity. For example, find_pathways_by_disease and find_pathways_by_gene target different entry points, while get_pathway_details, get_pathway_hierarchy, get_pathway_participants, and get_pathway_reactions each focus on specific aspects of pathway information. The descriptions reinforce these distinctions, making misselection unlikely.
All tool names follow a consistent verb_noun pattern with snake_case, such as find_pathways_by_disease, get_pathway_details, and search_pathways. The naming is predictable and readable throughout the set, with no deviations in style or convention.
With 8 tools, the count is well-scoped for a biological pathway server. Each tool earns its place by covering distinct operations like searching, retrieving details, and analyzing components, without being overly sparse or bloated. This aligns with typical MCP server ranges of 3-15 tools.
The tool surface provides complete coverage for querying and exploring biological pathways. It includes search (search_pathways, find_pathways_by_disease/gene), retrieval (get_pathway_details, get_pathway_hierarchy), and analysis (get_pathway_participants, get_pathway_reactions, get_protein_interactions), with no obvious gaps that would cause agent failures in this domain.
Unclaimed servers have limited discoverability.
If you are the server author, to access and configure the admin panel.
Model Context Protocol server for Studex tools, notifications, and profile integrations
A Model Context Protocol (MCP) server for Selise Blocks Cloud integration
Reactome biological pathway database
WikiPathways MCP — open community pathway database.
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