STRING-db MCP Server

Server Quality Checklist

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Latest release: v1.0.0
Disambiguation4/5
Most tools have distinct purposes, but there is some potential confusion between get_interaction_network (analyzes networks for multiple proteins) and get_protein_interactions (gets direct partners for a specific protein), which could overlap in use cases. Otherwise, tools like find_homologs, get_functional_enrichment, and search_proteins are clearly differentiated.
Naming Consistency5/5
All tools follow a consistent verb_noun pattern with snake_case, such as find_homologs, get_functional_enrichment, and search_proteins. This uniformity makes the tool set predictable and easy to navigate for agents.
Tool Count5/5
With 6 tools, this server is well-scoped for a protein database domain, covering key operations like search, annotation, interaction analysis, and homology without being overwhelming. The count aligns well with typical MCP server ranges for focused functionality.
Completeness4/5
The tool set covers essential protein database operations, including search, annotation, interaction analysis, and functional enrichment, but lacks explicit CRUD operations like create, update, or delete proteins, which might be intentional for a read-only database. Minor gaps exist, but core workflows are well-supported.
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
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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?
With no annotations provided, the description carries the full burden of behavioral disclosure. It states what the tool does but reveals nothing about performance characteristics (e.g., speed, accuracy), data sources, limitations (e.g., coverage gaps), or what the output looks like (format, structure). For a bioinformatics tool with potentially complex behavior, 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that gets straight to the point with zero wasted words. It's appropriately sized for a tool with clear parameters documented elsewhere and follows good front-loading principles.
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 homology search (which involves algorithms, databases, and potential limitations) and the absence of both annotations and an output schema, the description is incomplete. It doesn't help an agent understand what to expect from the tool's behavior or results, which is particularly important for scientific tools where accuracy and methodology matter.
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 three parameters thoroughly. The description adds no additional parameter semantics beyond what's in the schema (e.g., it doesn't explain what constitutes a 'homolog', how homology is determined, or format examples). 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 action ('Find') and resource ('homologous proteins across different species'), making the purpose immediately understandable. It doesn't explicitly differentiate from sibling tools like 'search_proteins' or 'get_protein_annotations', but the focus on cross-species homology is specific enough for basic understanding.
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_proteins' or 'get_protein_annotations'. It doesn't mention prerequisites, limitations, or typical use cases, 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.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It states the action but lacks details on what 'functional enrichment analysis' entails (e.g., statistical methods, output format, computational cost, or potential side effects like data processing time). This is a significant gap for a tool with no structured safety or behavior hints.
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 any fluff or redundancy. It's 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.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of 'functional enrichment analysis' (a bioinformatics task with statistical implications), no annotations, and no output schema, the description is incomplete. It fails to explain what the analysis returns, how results are structured, or any prerequisites (e.g., valid protein ID formats), leaving critical gaps for effective tool 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?
Schema description coverage is 100%, so the schema already documents all parameters thoroughly. The description adds no additional meaning beyond what's in the schema (e.g., it doesn't explain what 'functional enrichment' means in terms of the parameters or how they interact). Baseline 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.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('perform functional enrichment analysis') and target ('on a set of proteins'), which is specific and unambiguous. However, it doesn't explicitly differentiate from sibling tools like 'get_protein_annotations' or 'search_proteins', which might also involve protein analysis but for different purposes.
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. It doesn't mention sibling tools like 'get_protein_annotations' for annotation retrieval or 'search_proteins' for querying, leaving the agent to infer usage based on context 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?
With no annotations provided, the description carries full burden for behavioral disclosure. It mentions 'build and analyze' which implies computational processing, but doesn't disclose important traits like computational intensity, timeout risks, data source limitations, or what 'analyze' specifically entails (e.g., returns network statistics, visualizations, or raw edges). For a network construction tool with 5 parameters, 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is perfectly concise - a single sentence that states the core purpose without any redundant words. It's front-loaded with the essential action ('build and analyze') and resource ('protein interaction network'). Every word earns its place, 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.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of network construction/analysis, 5 parameters, no annotations, and no output schema, the description is insufficiently complete. It doesn't explain what 'analyze' produces (network properties? visualization? edge list?), doesn't mention data sources or limitations, and provides no context about computational requirements. For a tool that likely returns complex network data, this leaves too many unknowns.
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 5 parameters thoroughly. The description adds no parameter-specific information beyond what's in the schema - it doesn't explain relationships between parameters (e.g., how 'add_nodes' interacts with 'required_score') or provide usage examples. With complete schema coverage, baseline 3 is appropriate as the description doesn't enhance parameter understanding.
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 with specific verbs ('build and analyze') and resource ('protein interaction network for multiple proteins'). It distinguishes from sibling tools like 'get_protein_interactions' by emphasizing network construction and analysis rather than just retrieving interactions. However, it doesn't explicitly differentiate from all siblings like 'get_functional_enrichment' which might also involve network analysis.
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. It doesn't mention sibling tools like 'get_protein_interactions' (which might retrieve individual interactions) or 'get_functional_enrichment' (which might analyze network properties). There's no context about prerequisites, typical use cases, or limitations that would help an agent choose between available options.
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 what the tool does but lacks details on traits like whether it's read-only, potential rate limits, authentication needs, or what 'detailed annotations' entail in terms of output format or data scope. This is inadequate 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.
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 is front-loaded and appropriately sized, making it easy to parse 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 protein annotation retrieval, no annotations, and no output schema, the description is insufficient. It does not explain what 'detailed annotations' include, how results are structured, or any limitations (e.g., maximum protein IDs). This leaves significant 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?
The schema description coverage is 100%, with clear descriptions for both parameters in the input schema. The description does not add any additional meaning beyond the schema, such as explaining the format of protein identifiers or species names. Baseline score of 3 is appropriate as the schema handles the parameter documentation adequately.
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 ('detailed annotations and functional information for proteins'), making the purpose understandable. However, it does not explicitly differentiate from sibling tools like 'search_proteins' or 'get_functional_enrichment', which might also involve protein information retrieval, so it 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.
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 such as 'search_proteins' or 'get_interaction_network'. It does not mention prerequisites, exclusions, or specific contexts for usage, leaving the agent to infer based on tool 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?
With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions 'direct interaction partners' but doesn't specify data sources, rate limits, authentication needs, error handling, or what constitutes a 'direct' interaction. For a tool with 4 parameters and no output schema, 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, focused sentence with zero wasted words. It's appropriately sized for a straightforward query tool and front-loads the core 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 4 parameters, no annotations, and no output schema, the description is insufficiently complete. It doesn't explain return values, data formats, error conditions, or how parameters interact (e.g., how 'species' affects 'protein_id' resolution). For a biological data query tool, more context is needed.
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 schema. The description adds no additional parameter semantics beyond implying the tool focuses on 'direct' interactions, which might relate to the 'required_score' parameter. 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 the resource 'direct interaction partners for a specific protein', making the purpose unambiguous. However, it doesn't explicitly differentiate from sibling tools like 'get_interaction_network' or 'search_proteins', which might offer overlapping functionality.
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_interaction_network' or 'search_proteins'. It lacks context about use cases, prerequisites, or exclusions, leaving the agent to infer usage based on tool 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?
With no annotations provided, the description carries the full burden of behavioral disclosure. It states the search functionality but lacks details on permissions, rate limits, pagination, or what the results look like (e.g., format, fields). For a search tool with zero annotation coverage, 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 purpose without unnecessary words. It is front-loaded and appropriately sized, making it easy to parse 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 search tool with 3 parameters, no annotations, and no output schema, the description is incomplete. It doesn't explain result formats, error handling, or behavioral traits, leaving gaps that could hinder effective tool invocation by an AI agent.
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, species, limit) with descriptions and constraints. The description adds minimal value by implying the search scope ('across species') but doesn't provide additional syntax or usage details beyond what the schema offers, aligning with the baseline for high 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 action ('Search for proteins') and the resource ('by name or identifier across species'), making the purpose evident. However, it doesn't explicitly differentiate this tool from sibling tools like 'find_homologs' or 'get_protein_annotations', which might also involve protein-related searches or queries, so it doesn't fully achieve sibling distinction.
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 'find_homologs' or 'get_protein_annotations'. It mentions searching 'across species', which implies a broad scope, but doesn't specify exclusions or recommend other tools for specific scenarios, leaving the agent with little context for selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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