Patient Safety AI MCP
Server Quality Checklist
Latest release: v1.0.0
- Disambiguation5/5
Each tool targets a distinct aspect of patient safety: risk assessment, allergy conflicts, drug interactions, dosage validation, and alert generation. There is no functional overlap, and descriptions clearly differentiate their purposes.
Naming Consistency4/5All tool names use snake_case and follow a verb_noun pattern. Two tools start with 'check', while the others use different verbs (assess, generate, validate), which is mostly consistent but not perfectly uniform.
Tool Count5/5With 5 tools, the server covers essential patient safety checks without being bloated or too sparse. Each tool serves a clear purpose within the domain.
Completeness4/5The toolset covers core safety checks (risk, allergies, interactions, dosage) and includes alert generation. Minor gaps exist, such as no dedicated contraindication check, but overall it addresses the main clinical safety concerns.
Average 4.1/5 across 5 of 5 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 14 commits in the last 12 weeks
- Last stable release on
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is passing
This repository is licensed under MIT License.
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.
This repository includes a glama.json configuration file.
This server has been verified by its author.
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
- Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Comprehensive behavioral transparency section covering side effects, authentication, rate limits, error handling, idempotency, and data privacy. No annotations exist, so description carries full burden and exceeds expectations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5Is the description appropriately sized, front-loaded, and free of redundancy?
Well-structured with sections but repetitive (allergies comma-separated mentioned twice). Behavioral transparency is verbose but informative. Could be more concise.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With no schema descriptions and 3 parameters, the description should cover output format and api_key meaning. It lacks explanation of what the output looks like and does not provide examples. Output schema exists but description still incomplete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Description adds little meaning beyond schema: medication and allergies are described generically, api_key has a poor description 'to analyze or process'. Only the comma-separated hint for allergies is useful. Schema coverage 0% makes this inadequate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
Clearly states it cross-references a medication against patient allergies including cross-reactivity. However, it does not explicitly differentiate from the sibling tool 'check_drug_interactions', which might be similar.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
Includes 'When to use' and 'When NOT to use' sections, providing context for appropriate usage. However, the when-to-use is generic and does not explicitly compare to siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description fully discloses behavior: read-only, stateless, idempotent, rate limits (free 10/day, pro unlimited), authentication (none for basic usage), error handling (structured errors), and data privacy (no storage). This is comprehensive and addresses all key behavioral traits.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with sections but contains redundancy. The 'Behavioral Transparency' section repeats information from the initial 'Behavior' lines. It could be more concise while retaining key details.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the output schema exists, the description does not need to cover return values. It adequately covers inputs, behavior, side effects, rate limits, error handling, and privacy. It is complete for the tool's complexity, though a note about not being medical advice could add completeness.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, so description must compensate. The 'Args' section only repeats parameter names and types ('drugs (str)', 'api_key (str)') with vague descriptions ('the drugs to analyze or process'). It does not explain the expected format for drugs beyond the earlier mention of comma-separated, nor the role of api_key beyond what is in the authentication section. This adds little value over the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Check known drug interaction databases for a list of medications. Pass drugs as comma-separated string.' The verb 'check' and resource 'drug interaction databases' are specific, and the comma-separated format is explicitly noted. This distinguishes it from sibling tools like check_allergy_conflicts or validate_dosage.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit 'When to use' and 'When NOT to use' sections. It advises using the tool for structured analysis against frameworks and warns against real-time production use without human review. However, it does not directly compare with sibling tools, which would enhance clarity.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so the description carries full burden. It explicitly states read-only, stateless, idempotent behavior, authentication requirements, rate limits, error handling, and data privacy. This is comprehensive and exceeds expectations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with clear sections, but it is verbose and contains repetition (e.g., the Behavior section and Behavioral Transparency section overlap). Some sentences could be combined or trimmed without losing value.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (6 parameters, no output schema provided), the description covers behavioral aspects thoroughly, usage guidelines, and error handling. However, it lacks parameter-specific details and does not describe the output structure, which the output schema might address.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, but the description's 'Args' section merely repeats parameter names with 'to analyze or process,' adding no meaning beyond the schema. For example, 'drug (str): The drug to analyze or process' is tautological. Critical details like units, formats, or constraints are missing.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description starts with 'Check dosage against known safe ranges for a medication,' which is a specific verb and resource. It clearly identifies the tool's purpose and distinguishes it from sibling tools like assess_patient_risk and check_drug_interactions.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
Includes 'When to use' and 'When NOT to use' sections, though the when-to-use description is somewhat generic. It provides a clear exclusion for real-time decision-making without human review. Implicitly differentiates from siblings through the dedicated use case.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description comprehensively discloses behavioral traits: read-only, stateless, idempotent, no side effects, authentication needs (none basic, API key for pro), rate limits (10/day free, unlimited pro), structured error handling, and data privacy. Since no annotations were provided, the description fully carries the burden and does so thoroughly.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with sections (Behavior, When to use, When NOT to use, Args, Behavioral Transparency), but it is verbose. The 'Behavioral Transparency' section largely repeats the earlier behavior bullet points. Parameter descriptions are padded without added value. Could be more concise.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has 6 parameters (3 required) and an output schema, the description covers behavioral context, usage guidelines, error handling, rate limits, and privacy. Parameter descriptions are weak, but the overall guidance is sufficient for an agent to use the tool effectively. Minor gap in parameter semantics prevents a perfect score.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Despite 0% schema description coverage, the description's 'Args' section adds almost no meaning beyond parameter names. For each parameter it only says 'The [name] to analyze or process,' which is tautological. Only conditions and medications get the useful hint of being comma-separated strings. Other parameters (age, bmi, smoker, api_key) lack any semantic guidance.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool is for 'Risk scoring based on patient conditions, age, and comorbidities.' This specifies the verb (scoring) and resource (patient risk), and distinguishes it from sibling tools like check_drug_interactions or validate_dosage, which target different clinical tasks.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description includes explicit 'When to use' and 'When NOT to use' sections, guiding the agent to use it for structured analysis/classification and cautioning against real-time production decisions without human review. This differentiation from siblings is effective.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Despite no annotations, the description extensively covers side effects (read-only, no side effects), authentication (no auth for basic, API key for pro), rate limits (free: 10/day, pro: unlimited), error handling (structured errors), idempotency, and data privacy. This fully compensates for missing annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5Is the description appropriately sized, front-loaded, and free of redundancy?
Well-organized with sections, but somewhat verbose as behavioral transparency details are repeated under both 'Behavior:' and 'Behavioral Transparency:'. Could be more concise without losing clarity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Covers behavior, usage, transparency thoroughly. Has output schema, so return format not needed. However, lacks integration context with sibling tools (e.g., whether this tool is meant to be used after specific checks). Still, fairly complete for a generation tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must add meaning. However, the 'Args' section only provides generic phrases like 'The drug to analyze or process' for each parameter, adding little beyond the parameter name. Missing specifics like valid alert types or severity levels.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool creates a formatted clinical safety alert and lists specific alert types (interaction, allergy, dosage, contraindication). It distinguishes from sibling tools like check_allergy_conflicts and check_drug_interactions by focusing on generating a formatted alert rather than just checking.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicit 'When to use' and 'When NOT to use' sections provide clear guidance. It recommends use for structured analysis/classification and warns against real-time production use without human review. This helps an AI agent decide when to invoke this tool.
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:
Score Badge
Copy to your README.md:
Latest Blog Posts
- Who's Calling? MCP Hosts Are an Identity Blind Spot (And the Spec Knows It)By Om-Shree-0709 on .mcpAgent IdentityOAuth 2.1
- Your AI Chatbot Just Exposed Your CEO's Salary to an InternBy Om-Shree-0709 on .Agent IdentityMCP SecurityOAuth Delegation
- Why MCP Servers Need Execution Sandboxing (And Why Your Current Stack Isn't Enough)By Om-Shree-0709 on .Agentic AiPrompt InjectionWebAssembly
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/CSOAI-ORG/patient-safety-ai-mcp'
If you have feedback or need assistance with the MCP directory API, please join our Discord server