nefesh-mcp-server

Server Quality Checklist

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Latest release: v4.0.1
Disambiguation4/5
Tools occupy distinct functional roles: ingest is the sole write path for biometric data, while get_human_state (current), get_session_history (temporal), and get_trigger_memory (cross-session psychological) provide clearly differentiated read paths. Minor overlap exists since ingest returns the same state fields as get_human_state, but input requirements prevent misselection.
Naming Consistency3/5
Three tools follow a consistent get_<resource> pattern (get_human_state, get_session_history, get_trigger_memory), but ingest breaks convention by using a bare verb without a noun object and lacking the get_ prefix. This creates an asymmetry between read operations (get_*) and the single write operation.
Tool Count4/5
Four tools is well-suited for this narrow biometric monitoring domain, covering data ingestion, current state retrieval, historical trend analysis, and persistent trigger profiling without unnecessary bloat. The surface is minimal but sufficient for the stated conversational adaptation purpose.
Completeness3/5
The core monitoring loop is functional (ingest → read states), but notable gaps exist in lifecycle management: no tool to manually mark triggers as resolved/unresolved (despite the API tracking this distinction), delete session data, or explicitly initialize sessions. Agents can observe psychological states but cannot administratively manage the underlying profiles.
Average 4.4/5 across 4 of 4 tools scored. Lowest: 3.9/5.
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
- Last stable release on April 2, 2026
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI status not available
This repository is licensed under MIT License.
This repository includes a README.md file.
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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

Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. Describes return format and trend types but omits behavioral details like rate limits, data freshness, or side effects. Adequate for a read-like tool 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?
Four sentences, each essential. Front-loaded with purpose, no redundant phrasing. Efficient and easy to parse.
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?
Given only 2 parameters and no output schema, description covers key aspects: returns, parameters, use case, and disclaimer. Could improve by explaining trend values or handling of missing sessions, but sufficiently complete for a tool with low complexity.
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 coverage is 0%, so description must compensate. It adds meaning to 'minutes' (lookback window, default 5, max 60) but does not elaborate on 'session_id' beyond being required. Partially fills the 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?
Clearly states the tool gets state history for a session over time, listing specific returned fields (stress_score, state, heart_rate, trend). Differentiates from siblings like get_human_state by focusing on session context.
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?
Provides explicit context: minutes parameter controls lookback window with default and max, useful for detecting stress patterns during conversation. Includes disclaimer 'Not a medical device.' However, does not explicitly mention alternative tools or contradictory usage.
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 full burden. It declares the tool is read-only by nature, describes the returned data structure (active vs resolved triggers with field details), and includes a disclaimer ('Not a medical device'). This is thorough, though it does not explicitly state side effects (none expected).
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 and well-structured: a clear first sentence, then bullet points explaining trigger types and fields. Every sentence adds value, with no unnecessary repetition or fluff.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with one parameter and no output schema, the description covers purpose, return values (including fields), prerequisites, and a disclaimer. It is sufficient for an AI agent to understand and use the tool correctly.
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 has only 'subject_id' with 0% description coverage. The description implies the parameter identifies the subject via the prerequisite statement, but provides no additional details on format, source, or constraints. This adds some context beyond the schema, but not enough to fully compensate for the lack of schema 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 tool retrieves a 'psychological trigger profile' and explains it returns active and resolved triggers. It differentiates from siblings like ingest and get_human_state by focusing on trigger memory.
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 mentions a prerequisite ('requires prior ingest calls') but does not provide explicit guidance on when to use this tool versus alternatives. No exclusions or relative advantages are stated.
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 must cover behavioral traits. It explains signal fusion, return format (same as get_human_state plus signals_received and topics_detected), and that source_device improves confidence. However, it omits details like idempotency, persistence, or error behavior.
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 well-structured: a one-line summary, required fields, common signals, cross-session use case, return format, and a note about source_device. Every sentence adds value relative to the 37-parameter complexity.
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?
Despite no output schema and 37 parameters, the description covers the core functionality, required inputs, and most impactful signals. It references get_human_state for return fields, which may suffice if that tool is documented. Less common parameters are not explained, but the description is reasonably complete for its purpose.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, but the description adds significant meaning: it lists common signals with ranges (e.g., heart_rate 30-220), groups them by type (cardiovascular, vocal, etc.), and explains the purpose of subject_id, user_message, and ai_response for trigger memory. This fully compensates for the missing schema 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 tool's action ('Send biometric signals') and outcome ('get unified state back'), distinguishing it from sibling tools that retrieve or query data. The verb and resource are specific.
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 specifies required fields (session_id, timestamp, at least one signal) and provides guidance for cross-session tracking (include subject_id, user_message, ai_response). It implies when to use this tool vs. siblings (others read, this writes), but does not explicitly state when not to use it.
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, description carries full burden and discloses polling behavior, status transition from 'pending' to 'ready', and that response includes API key when ready. Missing error handling details (e.g., invalid email), but adds significant behavioral context.
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?
Three sentences with purpose first, followed by usage guidelines and behavioral details. Every sentence contributes meaning, no redundant or missing parts.
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?
For a simple polling tool with one parameter and no output schema, description covers essential flow: polling interval, status transition, and key retrieval. Lacks error handling for invalid requests, but overall adequate.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Only one parameter 'email' with 0% schema description coverage. Description adds value by stating to use the same email as in request_api_key, which is crucial for correct usage.
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?
Description states 'Check the status of a pending API key request' with a specific verb and resource. It explicitly references the sibling tool 'request_api_key', distinguishing its role as a polling companion for status checking.
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?
Clear context provided: use same email from request_api_key, poll every 10 seconds, and expect status change after user clicks verification link. No explicit exclusions, but the usage flow is well-defined.
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, the description fully carries the burden. It discloses the tool is not a medical device, requires prior ingest, and explains the adaptation_effectiveness field on 2nd+ calls. Returns fields are described in detail, including scale ranges (stress_score 0-100, confidence 0.0-1.0) and meanings of suggested_action values. No contradiction.
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?
Description is informative and front-loaded with purpose. It lists return fields with explanations. A few extra sentences (e.g., adaptation_effectiveness details) add value but increase length. Could be slightly trimmed, but overall structured well.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (1 param, no output schema, no annotations), the description is comprehensive: covers return values, usage guidance, adaptation feedback, prerequisite, and disclaimer. Leaves no major gaps for an agent to understand when and how to use it.
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?
Only one parameter (session_id) with 0% schema description coverage. The description does not explicitly describe session_id, but the context (session state) makes it clear. Since schema offers no help, description should ideally add format or source, but the parameter name and tool purpose are self-explanatory. Adequate but not enriched.
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?
Description clearly states it gets the unified human state for a session, specifying when to call it ('before generating important responses'). It lists return fields (state, stress_score, etc.) and differentiates from sibling tools like get_session_history or get_trigger_memory by focusing on human state.
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?
Explicitly says 'Call this before generating important responses.' Provides guidance on how to use suggested_action to adapt response complexity based on state. Mentions requirement for prior ingest call. Could be clearer on when not to use (e.g., for trivial responses), but context is sufficient.
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?
Discloses blocking of disposable emails, need for user to click verification link, polling pattern, and rate limit. No annotations exist, so description fully carries the burden.
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?
Well-organized with warnings, example, and flow. Every sentence adds value; no redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Covers purpose, usage, behavioral quirks, and post-call steps. Simple tool with no output schema; description is fully complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, but description explains the single parameter 'email' well: must be real user email, cannot be disposable. Lacks validation details but sufficient for usage.
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?
Clear verb+resource ('Request ... API key') and distinguishes from sibling tools like check_api_key_status. States no existing key needed.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicit instructions: ask user for real email, do not invent, provide example prompt, describe verification link and polling workflow. Covers when and how to use.
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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