OpenTargets MCP Server

Server Quality Checklist

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

Latest release: v1.0.0
Disambiguation5/5
Each tool has a clearly distinct purpose: two are for retrieving details (disease and target), two for retrieving associations/summaries (target-disease and disease-targets), and two for searching (diseases and targets). There is no overlap in functionality, making it easy for an agent to select the right tool.
Naming Consistency5/5
All tool names follow a consistent verb_noun pattern with 'get_' or 'search_' prefixes, followed by descriptive nouns (e.g., get_disease_details, search_targets). The naming is uniform and predictable across all six tools.
Tool Count5/5
With 6 tools, this server is well-scoped for its purpose of querying disease and target data in the OpenTargets domain. Each tool serves a specific, non-redundant function, and the count is appropriate for covering core operations without being overwhelming.
Completeness4/5
The tool set provides comprehensive coverage for querying and retrieving disease and target information, including details, associations, and search capabilities. A minor gap is the lack of update or creation tools, but this is reasonable for a read-only data query server focused on biomedical research.
Average 2.9/5 across 6 of 6 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 0 commits in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI status not available
Add a LICENSE file by following GitHub's guide. Once GitHub recognizes the license, the system will automatically detect it within a few hours.
If the license does not appear after some time, you can manually trigger a new scan using the MCP server admin interface.
MCP servers without a LICENSE cannot be installed.
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

Behavior2/5
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' information, implying a read-only operation, but doesn't clarify aspects like whether it requires authentication, has rate limits, returns structured data, or handles errors. The description adds minimal behavioral context beyond the basic action.
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 a single, efficient sentence with no wasted words. It's front-loaded with the core action, making it easy to parse. However, it could be more structured by explicitly mentioning the parameter or differentiating from siblings, but it earns high marks for brevity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (a read operation with one parameter) and lack of annotations and output schema, the description is incomplete. It doesn't explain what 'comprehensive information' includes, how results are formatted, or error handling, leaving gaps for the agent to navigate without sufficient context.
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 has 100% description coverage, with the 'id' parameter documented as 'Target Ensembl gene ID'. The description adds no additional meaning about parameters, such as format examples or constraints. With high schema coverage, the baseline score of 3 is appropriate, as the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Get comprehensive target information' states a clear verb ('Get') and resource ('target information'), but it's vague about what constitutes 'comprehensive' and doesn't distinguish this tool from siblings like 'get_target_disease_associations' or 'search_targets'. It provides a basic purpose but lacks specificity.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description offers no guidance on when to use this tool versus alternatives. Given siblings like 'search_targets' (likely for broader queries) and 'get_target_disease_associations' (for related data), there's no indication that this tool is for detailed information on a specific target by ID, leaving the agent to infer usage from the parameter schema alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It states it 'gets' information, implying a read-only operation, but doesn't disclose behavioral traits such as authentication needs, rate limits, error handling, or what 'comprehensive' entails (e.g., format, depth). This leaves significant gaps 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.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence with no wasted words. It is appropriately sized for a simple tool, though it could be more front-loaded with key details like the required EFO ID.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
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 disease information' includes (e.g., fields, structure), nor does it address behavioral aspects like permissions or limitations, leaving the agent with insufficient context for reliable use.
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 schema description coverage is 100%, with the single parameter 'id' documented as 'Disease EFO ID'. The description adds no additional parameter semantics beyond this, so it meets the baseline 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.
Purpose4/5
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 ('comprehensive disease information'), making the purpose understandable. However, it doesn't differentiate from sibling tools like 'search_diseases' or 'get_disease_targets_summary' beyond the general scope of disease information.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., needing an EFO ID), exclusions, or comparisons to siblings like 'search_diseases' for broader queries or 'get_disease_targets_summary' for target-related details.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
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 doesn't indicate whether this is a read-only operation, what format the 'overview' returns, whether there are rate limits, or authentication requirements. The description doesn't contradict annotations (none exist), but provides inadequate behavioral context for a tool with 3 parameters.
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 a single, efficient sentence that states the core purpose without unnecessary words. It's appropriately sized for a tool with 3 parameters and gets straight to the point. Every word earns its place in conveying the essential function.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with 3 parameters, no annotations, and no output schema, the description is insufficiently complete. It doesn't explain what an 'overview' contains versus detailed associations, doesn't mention return format or structure, and provides no context about the data source or limitations. The agent lacks critical information to use this tool effectively despite the good parameter documentation.
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 description coverage is 100%, so all parameters are documented in the input schema. The description adds no additional parameter semantics beyond what's already in the schema - it doesn't explain what 'overview' means in relation to the minScore or size parameters, or provide context about disease EFO IDs. 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.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'Get' and resource 'overview of all targets associated with a disease', making the purpose immediately understandable. It distinguishes this from sibling tools like get_disease_details or get_target_details by focusing on target-disease associations rather than individual entity details. However, it doesn't specify what constitutes an 'overview' versus the more detailed get_target_disease_associations tool.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
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 sibling tools like get_target_disease_associations and search_targets available, there's no indication whether this tool provides summarized data, filtered results, or serves a different purpose. The agent must infer usage from the tool name and parameter set alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
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 mentions 'evidence scores' but doesn't explain what these scores represent, how they are calculated, or any limitations like rate limits, permissions, or data freshness. For a tool with 4 parameters and no annotations, this is a significant gap in transparency.
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 a single, efficient sentence that directly states the tool's function without any unnecessary words. It is front-loaded with the core action and resource, 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.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of a tool with 4 parameters, no annotations, and no output schema, the description is insufficient. It doesn't cover behavioral aspects like what the evidence scores mean, how results are structured, or any error conditions. This leaves critical gaps for an AI agent to use the tool effectively.
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 description coverage is 100%, with each parameter well-documented in the input schema (e.g., 'Target Ensembl gene ID', 'Minimum association score (0-1)'). The description adds no additional meaning beyond the schema, such as explaining relationships between parameters or typical use cases, so it meets the baseline score of 3.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
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 'target-disease associations with evidence scores', making the purpose specific and understandable. However, it doesn't explicitly differentiate from sibling tools like 'get_disease_targets_summary' or 'search_targets', which might also retrieve similar data, so it misses the top score for sibling differentiation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
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 'get_disease_targets_summary' or 'search_targets'. It lacks any context about prerequisites, such as needing specific IDs, or exclusions, leaving the agent to infer usage based on parameter names alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
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 mentions search functionality but doesn't disclose behavioral traits such as pagination, rate limits, authentication needs, or what the search returns (e.g., list of IDs, full details). This leaves significant gaps for a search 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 a single, efficient sentence with no wasted words. It's appropriately sized and front-loaded, directly stating the tool's purpose without unnecessary elaboration.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of a search tool with no annotations and no output schema, the description is incomplete. It doesn't explain what the search returns (e.g., partial matches, relevance scoring), behavioral aspects, or how it differs from siblings, leaving the agent with insufficient context.
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 description coverage is 100%, so the schema already documents all parameters (query, size, format) with details like defaults and constraints. The description adds minimal value by listing search fields (name, synonym, description), but this is largely redundant with the schema's query description.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'search' and the resource 'diseases', specifying it can search by name, synonym, or description. However, it doesn't explicitly differentiate from sibling tools like 'search_targets' or 'get_disease_details', which would require a 5.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
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_targets' or 'get_disease_details'. It lacks context about use cases, prerequisites, or exclusions, offering only a basic functional statement.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
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 search functionality but lacks critical details: whether this is a read-only operation, if it requires authentication, rate limits, pagination behavior, or what the response structure looks like. For a search 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.
Conciseness5/5
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 and front-loaded, with every element contributing value.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
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 insufficient for a search tool with three parameters. It doesn't explain return values, error conditions, or behavioral constraints, leaving significant gaps in understanding how to use the tool effectively.
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 description coverage is 100%, so the input schema fully documents all three parameters. The description mentions the searchable fields (gene symbol, name, description) which aligns with the 'query' parameter but doesn't add meaningful semantics beyond what the schema already provides. 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.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: searching for therapeutic targets using specific search criteria (gene symbol, name, or description). It uses a specific verb ('search') and resource ('therapeutic targets'), but doesn't explicitly differentiate from sibling tools like 'search_diseases' or 'get_target_details', 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.
Usage Guidelines2/5
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_diseases' or 'get_target_details'. It doesn't mention prerequisites, context, or exclusions, leaving the agent to infer usage based solely on the tool name and parameters.
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:

[![OpenTargets-MCP-Server MCP server](https://glama.ai/mcp/servers/Augmented-Nature/OpenTargets-MCP-Server/badges/card.svg)](https://glama.ai/mcp/servers/Augmented-Nature/OpenTargets-MCP-Server)

Score Badge

Copy to your README.md:

[![OpenTargets-MCP-Server MCP server](https://glama.ai/mcp/servers/Augmented-Nature/OpenTargets-MCP-Server/badges/score.svg)](https://glama.ai/mcp/servers/Augmented-Nature/OpenTargets-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/Augmented-Nature/OpenTargets-MCP-Server'

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