Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@phi-mcp-server Analyze these biomarkers: HbA1c 6.4, LDL-C 145, triglycerides 190."
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
A Model Context Protocol (MCP) server that lets AI agents call Phi Longevity's PRISM clinical recommendation engine on synthetic biomarker panels — guideline-cited, evidence-tiered recommendations an agent can use when researching options for the person it's helping.
Built for chronic-condition work in particular: type-2 diabetes, lupus, and cancer survivorship each have their own condition lens, because the same panel means different things depending on what someone is managing.
🛡️ Synthetic / de-identified data only. Do not submit protected health information (PHI). The server is stateless and stores nothing. Real health files are analyzed only inside the authenticated Phi Longevity app, after consent, by the account owner.
Two surfaces, deliberately different. The npm package (stdio) has 4 tools; the remote endpoint has 6. The two report-generating tools are remote-only.
Tool | npm | remote | What it does |
| — | ✅ | Start here. Returns a complete, real PRISM report for a sample chronic-condition persona in one instant call. Free, no signup, no input required. The fastest way to see what the engine actually produces. |
| ✅ | ✅ | A fast read on a handful of values — the low-friction entry point before a full analysis. |
| ✅ | ✅ | Analyze a synthetic panel → tiered, guideline-cited recommendations, with a condition lens. |
| ✅ | ✅ | 51 scored biomarkers + units + reference ranges, by clinical pillar. |
| ✅ | ✅ | How the Phi Score works (5 pillars + weights) + link to the full methodology. |
| — | ✅ | A full report for a panel you supply. Paid — priced per call via x402; terms are returned with the offer. |
All tools except full_prism_report are free.
Related MCP server: GenomeMCP
Point any MCP client that supports Streamable HTTP at:
https://philongevity.com/mcpNothing to install, no key to manage, all 6 tools. Try sample_prism_report first — it
needs no arguments and returns a complete report immediately.
{
"mcpServers": {
"phi-longevity": {
"command": "npx",
"args": ["-y", "@phi-longevity/mcp-server"]
}
}
}A published, rate-limited access key is built in. If you have a dedicated key, set
PHI_MCP_KEY to override it. This path exposes the 4 tools marked ✅ above.
npm install && npm run build
node dist/index.js// sample_prism_report — no arguments, returns a complete sample report
{}
// analyze_biomarkers input (synthetic values only)
{
"biomarkers": { "HbA1c": 6.4, "LDL-C": 145, "Triglycerides": 190 },
"conditionFocus": "type2_diabetes" // or general_wellness | lupus | cancer_survivorship
}
// → tiered, guideline-cited recommendations + a `full_report` block
// pointing the user to a complete PRISM report at philongevity.comThe tools above analyze a handful of values at a time. A full PRISM report consolidates all of a person's lab reports, wearable data, and clinical notes into one integrated picture with a personal health score and progress over time — which is the actual problem for someone managing a chronic condition across several clinicians who don't talk to each other. Agent docs + signup: https://philongevity.com/for-agents
Var | Required | Notes |
| no | Overrides the built-in published key. Rate-limited + revocable. |
| no | Override the default Phi MCP gateway URL (rarely needed). |
Synthetic-only at the protocol boundary; stateless; aggregate-only telemetry (counts/timing to stderr, never values or recommendation text); zero access to Firestore / the HIPAA datastore / user accounts. GDPR: processes no personal data (synthetic only) → minimal exposure.
Agent docs: https://philongevity.com/for-agents
Methodology: https://philongevity.com/methodology
Phi Longevity: https://philongevity.com
Apache-2.0.
Analyze a SYNTHETIC biomarker panel with Phi Longevity's PRISM engine. Returns 3-tier, evidence-cited recommendations (THIS WEEK / CONSIDER / ASK YOUR CLINICIAN). For research/education with SYNTHETIC or de-identified data only. Do NOT submit protected health information (PHI). This endpoint is stateless and does not store inputs.
| Name | Required | Description | Default |
|---|---|---|---|
| age | No | ||
| biomarkers | Yes | Map of biomarker name -> numeric value, e.g. { "Hemoglobin A1c": 5.4 }. SYNTHETIC ONLY. | |
| biologicalSex | No | ||
| conditionFocus | No | Optional condition track. Default general_wellness. | |
| include_partner_options | No | If true, include partner/product options. Default false (clean clinical output). |
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 clearly states the endpoint is stateless and does not store inputs, which is a key safety trait. It also specifies the output format (3-tier recommendations). This goes beyond basic disclosure, though it does not cover every possible side-effect or require permissions. For an analysis tool, this is strong transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is compact and front-loaded with the core purpose. Each sentence adds value: the action, the output, the data constraints, and the statelessness. There is no filler or redundancy. Despite being slightly longer than typical, it is appropriately detailed given the safety-critical data-handling instructions.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description covers the main purpose, output, and important data-handling behavior. It does not mention that users should consult list_supported_biomarkers to ensure valid biomarker names, nor does it explain how age or biologicalSex affect recommendations. Given the moderate complexity (nested object, 5 params) and the absence of an output schema, the description is mostly complete but has notable gaps in prerequisites and parameter influence.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 60%, so the description should compensate for the undocumented parameters (age, biologicalSex). The description only refers to 'biomarker panel' and the output tiers, adding little detail about how age, biologicalSex, conditionFocus, or include_partner_options affect results. The schema itself describes biomarkers, conditionFocus, and include_partner_options, but age and biologicalSex remain unexplained in both schema and description, leaving a gap.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's function: it analyzes a SYNTHETIC biomarker panel using the PRISM engine and returns 3-tier recommendations. This distinguishes it from siblings list_supported_biomarkers (which lists biomarkers) and get_methodology (which explains methodology). The verb 'analyze' plus the resource 'biomarker panel' makes the purpose unmistakable.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides strong context for when to use the tool: it is intended for research/education with synthetic or de-identified data, explicitly forbids PHI, and notes the stateless nature. However, it does not explicitly contrast this tool with its siblings or state conditions like 'use when you need recommendations' versus 'use list_supported_biomarkers when checking available biomarkers'. The usage is implied rather than fully explicit.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Summarize how the Phi Score works (5 clinical pillars + weights) and link to the full methodology.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations exist, so the description carries the full burden. It discloses the core behavior (summarize and provide a link), but omits details such as the output format, whether the summary is static or dynamic, and any access prerequisites. Adequate but not enriched.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
A single, front-loaded sentence conveys the purpose and key output elements without superfluous words. Every word earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the simple, parameterless nature and lack of output schema, the description adequately explains the tool's purpose and outputs (summary and link). It could specify the link format or include a note about the summary's granularity, but this is a minor gap.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters, and the schema is an empty object, making parameter semantics irrelevant. Per rubric, a zero-parameter tool earns a baseline of 4.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses the specific verb 'summarize' and identifies the exact resource (Phi Score with 5 clinical pillars and weights). It clearly distinguishes itself from sibling tools that list or analyze biomarkers.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No explicit when-to-use or alternative guidance is provided. The description only states what the tool does, leaving the agent to infer when it should be selected over siblings; this falls under 'no guidance'.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List the biomarkers PRISM scores, with units and reference ranges, grouped by clinical pillar. Reference data only — no patient data.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It discloses that the tool returns reference data only, not patient data, and reveals the structure (grouped by clinical pillar). This is sufficient for a simple list operation, though it does not detail any edge cases or side effects, which are unlikely here.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences, front-loaded with the core action and resource, and each sentence adds value: the first explains what is listed, the second clarifies the scope and data type. No wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given zero parameters and no output schema, the description is complete: it tells the user what the tool does, what information is returned, and what the result is grouped by. The distinction from patient-data tools is also covered, making it sufficient for a low-complexity tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters, so the baseline is 4. The description adds meaning by explaining what the list contains (biomarkers, units, reference ranges) and how it is organized, which is more than necessary for an empty schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a specific verb ('List') and resource ('biomarkers PRISM scores'), with added detail on units, reference ranges, and grouping by clinical pillar. It also distinguishes itself from siblings by clarifying 'Reference data only — no patient data', which separates it from tools like analyze_biomarkers.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The phrase 'Reference data only — no patient data' provides clear context on when to use this tool (for reference lookups, not patient-specific queries). While it does not explicitly name alternatives, the sibling context and this exclusion make the intended use clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
v0.6.3analyze_biomarkersget_methodologylist_supported_biomarkersEach tool targets a distinct concern: listing reference data, explaining methodology, and performing analysis. There is no overlap or ambiguity between them.
All tools follow a consistent verb_noun pattern (list_, get_, analyze_), making the API predictable and easy to navigate.
Three tools is well-scoped for a domain-specific server focused on biomarker scoring; each tool serves a clear and necessary purpose without bloat.
The server covers the full lifecycle of its purpose: reference data, methodology explanation, and actual analysis. No obvious gaps exist for a research/education tool.
Physician-reviewed medical opinions and prescriptions for AI agents.
Doctor-reviewed blood-test markers, conditions & symptoms as agent tools. EN/RU/HE. Hosted.
Find public biomarker education, available blood tests, panels, and budget-aware starting points.
Connect AI clients to biomedical data and tools.
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