fhir-synthetic-mcp

Server Quality Checklist

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Latest release: v2.0.0
Disambiguation5/5
Each tool targets a distinct action and resource: listing vs reading patients, searching guidelines vs trials, proposing vs approving/rejecting writes. No two tools appear to do the same thing.
Naming Consistency5/5
All tool names follow a consistent snake_case verb_noun pattern (list_patients, get_patient, search_guidelines, approve_write). The convention is uniform across the entire set.
Tool Count5/5
With 10 tools, the server is well-scoped for its purpose: patient data access, clinical searches, and a human-in-the-loop write workflow. Each tool serves a clear role without excessive overlap or bloat.
Completeness4/5
The core workflow is covered: list/get patients, list observations, propose and approve/reject observations, plus guideline and trial searches. Minor gaps exist (e.g., no update/delete for observations, no patient creation), but these are not critical for the synthetic FHIR use case.
Average 3.4/5 across 10 of 10 tools scored. Lowest: 2.3/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 41 commits in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is passing
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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?
No annotations are provided, so the description must carry the full burden. It only offers the vague label 'HUMAN-IN-THE-LOOP GATE' and the core action. It does not disclose consequences of rejection, permissions required, reversibility, or side effects.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness2/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is very brief, but this brevity is due to under-specification rather than economy. The 'HUMAN-IN-THE-LOOP GATE' label adds little concrete information, and the action sentence is minimal. Lacks essential details that should be included.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness1/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool is part of a write approval workflow, as evidenced by siblings approve_write and list_pending_writes. The description fails to explain this context, what a 'staged write' is, or the consequences of rejection. With three unexplained parameters and no annotations, the description is completely inadequate for a reliable tool invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has zero descriptions for the three required parameters (write_id, approver, reason). The description does not explain any of them, leaving their meaning to inference. With 0% schema coverage, the description was expected to compensate but did not.
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 states 'Reject a staged write' which is a specific action targeting a specific resource type (a staged write). It is clear and likely understood in the context of the sibling tool approve_write, though it does not explicitly name that alternative.
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 approve_write or other alternatives. The sibling tools list_pending_writes and approve_write suggest a workflow, but the description does not explain the decision criteria for rejecting.
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 must carry the full burden of disclosing behavior. It only states that the tool commits a staged write after human approval, but does not describe consequences (e.g., whether the write becomes irreversible, approval requirements, or what happens on failure).
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely brief and front-loaded with a clear purpose, but it is under-specified given the three required parameters and workflow context. It reads as concise without being sufficiently informative.
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?
The tool is part of a human-in-the-loop workflow (with propose_observation, list_pending_writes, reject_write), and has three required parameters plus an output schema. The description does not place approve_write in this workflow, explain where write_id comes from, or indicate what the output will contain. This leaves critical context missing.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0% and the description itself provides no information about write_id, approver, or reason. The description utterly fails to compensate for the lack of schema-level parameter documentation, leaving all three required parameters undefined.
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?
Description identifies a clear action ('Commit a staged write') and a specific resource, which distinguishes it from the sibling reject_write. It doesn't explicitly name alternatives or scope limitations, so it doesn't earn 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 Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The phrase 'HUMAN-IN-THE-LOOP GATE' implies this tool is used when human approval is required for a pending write. However, there is no explicit 'when to use vs alternatives', no mention of reject_write or list_pending_writes as counterparts, and no prerequisites for obtaining write_id.
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, the description must disclose behavioral traits. It implies a read-only operation but does not state rate limits, permissions, pagination, or the meaning of the required `reason` parameter. The purpose of `reason` and its potential effect on the response is unexplained, leaving the agent uncertain about side effects or constraints.
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 concise sentence plus a parenthetical parameter explanation. It front-loads the core purpose and wastes no words, earning a high score for conciseness and structure.
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?
Despite having an output schema, the tool lacks annotations and the description does not explain return behavior, the role of `reason`, or usage context relative to siblings. The presence of a required `reason` parameter is unusual and unexplained, creating a significant gap for a simple-looking tool. The description is minimally adequate but incomplete for a dependable agent decision.
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 provides zero description coverage for the single parameter `reason`, so the description's phrase 'why you need this list' adds a minimal semantic: it is a justification string. However, it does not specify format, allowed values, length limits, or whether it affects the returned data, so compensation is partial.
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 'List available patient IDs' clearly states a specific verb+resource (list patients) and the output is patient IDs. It distinguishes somewhat from sibling get_patient by indicating a list operation, but does not clarify what 'available' means (all patients vs. accessible ones), which leaves some ambiguity.
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 like list_observations or get_patient. The sole mention of `reason` hints at a use case (why you need the list) but does not provide explicit context, prerequisites, or exclusions.
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 disclosing behavior. It only states 'List writes awaiting human approval,' which implies a read-only operation, but it does not clarify whether it has side effects, requires permissions, or how it handles the 'reason' parameter. This is too minimal for a tool with no annotation safety net.
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 concise sentence with no filler. It is front-loaded and to the point, but it sacrifices necessary details for brevity. Still, for what is included, it is efficient.
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 has one required parameter with no explanation and no annotations, the description is incomplete. The presence of an output schema may help with return values, but the description still does not explain the purpose of the 'reason' parameter or provide any behavioral context. The tool is simple, but the missing parameter semantics make it incomplete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0% and the description completely ignores the required 'reason' parameter. The schema provides only the parameter name and type, leaving the agent without any clue about what 'reason' means or how it filters results. The description fails to compensate for the missing parameter documentation.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('List') and the specific resource ('writes awaiting human approval'), making it distinct from sibling tools like list_patients or approve_write. It leaves no ambiguity about what the tool does.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies this tool is used to see pending write approvals, but it does not explicitly state when to use it over alternatives or mention any prerequisites. It is understandable in the context of sibling tools like approve_write and reject_write, but lacks explicit guidance.
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?
The description discloses that 'reason is audited', which is a useful behavioral trait. However, with no annotations provided, it fails to disclose other important behaviors such as read-only status, pagination, or error handling.
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 concise with two short sentences. It front-loads the primary purpose and adds the audit note as a caveat, with no wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool has an output schema and only two parameters, but the description lacks usage guidance and detailed parameter semantics. The audit note is valuable but insufficient for a complete picture.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate. It only mentions that 'reason is audited', which adds minimal context, but does not explain the purpose or format of either parameter, especially patient_id.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states 'List a patient's observations', specifying the verb (list), resource (observations), and scope (patient's). This distinguishes it from sibling tools like list_patients and parse_clinical_document.
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 when not to use it, or how it relates to related tools like search_guidelines or get_patient.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full responsibility for disclosing behavior. It mentions the hybrid retrieval method, which is a useful behavioral detail, but it does not explicitly state that this is a read-only operation, mention any authentication requirements, or describe rate limits or side effects. This is partial transparency but lacks a clear safety profile.
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, front-loaded sentence with no filler. It wastes no words and is easily scannable. This is ideal for conciseness.
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?
Despite having an output schema, the description is sparse for a tool with 4 parameters and no annotations. It does not differentiate the tool from sibling search_clinical_trials, explain parameter semantics, or provide usage context. The minimalism leaves the agent with significant gaps in understanding.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has 0% description coverage, and the tool description does not explain any parameters. Parameter names like 'k', 'reason', and 'loinc_codes' are ambiguous without additional context. The description fails to compensate for the lack of schema parameter descriptions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action (search) and the resource (clinical guidelines), and specifies the retrieval method (hybrid BM25 + semantic retrieval). It effectively distinguishes this tool from sibling 'search_clinical_trials' by focusing on guidelines. This is a specific verb+resource statement.
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_clinical_trials, nor does it mention any prerequisites or exclusions. It only states what the tool does, leaving the agent to infer usage from the name. This is a lack of explicit usage context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden of disclosing side effects. It explicitly states 'does NOT write' and 'stages for human approval', which are critical behavioral traits. It omits details like reversibility or error conditions, but the most important side-effect disclosure is present.
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 two short sentences, front-loaded with the main purpose, and every clause adds meaningful information. It is concise without losing clarity.
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?
Despite having 7 required parameters with zero schema descriptions and no annotations, the description provides no parameter context or workflow guidance. The presence of an output schema covers return values, but the description still leaves major gaps in how to construct a valid proposal and what happens after staging.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate by explaining the 7 required parameters. It does not mention any parameter, leaving the agent to infer meanings from names alone. No format, code system, or relationship guidance is provided.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly identifies the action ('Propose a new observation') and the resource. The added clause 'Stages it for human approval; does NOT write' distinguishes it from direct write tools like approve_write and from read-only list_observations.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies a staging workflow that precedes approval, signaling when to use this tool versus approve_write. It does not explicitly name sibling tools or state exclusions, but the context is clear enough for an agent to infer the intended workflow.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the burden. It mentions using NVIDIA NIM, and the source parameter notes PHI handling via a self-hosted endpoint, implying potential external data transmission. However, it does not disclose side effects, permissions, rate limits, or what happens to the document. Partial disclosure but not comprehensive.
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 sentence of 10 words, directly stating the tool's purpose without any extraneous information. It is efficiently front-loaded.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The full tool definition benefits from a fully described input schema and an output schema, which cover return values and parameter constraints. The description itself is terse but combined with schema it provides adequate context, though it could better summarize the overall behavior and side effects at a high level.
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 detailed parameter descriptions (source types, PHI guidance, document_type options). The tool description itself adds no parameter-level detail, so baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states a specific action ('Parse') on a distinct resource ('a clinical document') and specifies the technology ('Nemotron Parse (NVIDIA NIM)'). This differentiates it from sibling tools that handle patients, observations, guidelines, and writes.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description itself gives a clear context for use (parsing clinical documents). The parameter description for 'source' provides practical guidance for PHI documents (using a self-hosted NIM endpoint). No explicit alternatives or exclusions are mentioned, but the use case is unambiguous relative to siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
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 adds a crucial safety disclosure: 'PHI-safe: only condition strings transmitted externally.' This tells the agent that the tool does not send protected health information beyond the condition, which is critical in a medical context. It also implies a read-only search. However, it does not elaborate on other behaviors like rate limits or error handling, so it gets a 4.
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 two sentences that front-load the primary purpose and then add usage and safety context. There is no fluff or redundant content; every clause adds value.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The output schema covers the return structure, so the description needn't explain return values. It covers the primary trigger and safety. However, it omits explanations for three of four parameters and does not describe the relationship with search_guidelines beyond the trigger. This is a clear gap, making the description adequate but not comprehensive.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 0%, meaning all four parameters (condition, reason, loinc_codes, max_results) lack descriptions. The description only mentions 'condition', which maps to one parameter. The remaining parameters are non-obvious, and the schema only provides defaults and types, not semantics. Thus the description fails to compensate for the coverage gap.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description starts with a specific action verb 'Search' and clearly identifies the resource (ClinicalTrials.gov) and the scope (recruiting trials matching a condition). This distinguishes it from sibling tools like search_guidelines or list_observations, which are about different resources or actions.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly states when to call the tool: 'Call when search_guidelines returns validation_warnings (flagged observations) to surface trials the patient may qualify for.' This gives clear situational context, but it does not explicitly mention when not to use it or present alternatives, so it falls just short of a 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
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 uses 'Read' to signal read-only behavior and explicitly discloses that `reason` is recorded in the audit trail, a non-obvious side effect. This adds meaningful context beyond the schema.
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 sentence, front-loaded with the action, and includes only essential information. Every word contributes to understanding the tool's purpose and behavior.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool is simple, has an output schema, and the description covers the key behavioral nuance (audit trail). It omits details like error conditions or access requirements, but for a straightforward read operation with structured output, this is adequate.
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 0%, and the description only explains `reason` (as being audited). `patient_id` is left to inference from its name, so the description partially compensates for the absent schema descriptions but does not fully clarify both parameters.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description starts with 'Read one patient's demographics' – a specific verb and resource that clearly distinguishes it from sibling tools like list_patients. The additional audit trail note further clarifies its scope.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The phrase 'one patient' provides clear context for when to use this tool – when you need a single patient's demographics. It does not explicitly name alternatives or exclusions, but the distinction from list_patients is evident from the wording.
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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clinical-ai-governance-platform MCP server

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