Sally Skills
Server Details
Plug Sally AI into your agent. The clinical-grade health intelligence behind A1C Insights, Sally AI, now exposed as an MCP endpoint and REST API to your agent — 64+ biomarkers across Western clinical and TCM preventive frameworks. Authenticate, call the skills you need, get billed per request. Built for agents who care about health and evidence.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
Glama MCP Gateway
Connect through Glama MCP Gateway for full control over tool access and complete visibility into every call.
Full call logging
Every tool call is logged with complete inputs and outputs, so you can debug issues and audit what your agents are doing.
Tool access control
Enable or disable individual tools per connector, so you decide what your agents can and cannot do.
Managed credentials
Glama handles OAuth flows, token storage, and automatic rotation, so credentials never expire on your clients.
Usage analytics
See which tools your agents call, how often, and when, so you can understand usage patterns and catch anomalies.
Tool Definition Quality
Average 2.4/5 across 10 of 10 tools scored. Lowest: 1.4/5.
Several tool names have overlapping or unclear purposes (e.g., 'health_insights' vs 'metabolic_overview', 'food_journal' vs 'lookup_food', 'supplement_grading' vs 'lookup_supplement_grade'), and most descriptions are generic ('Sally skill') without clarifying distinctions.
Tool names mix verb_noun patterns (analyze_lab_result, lookup_food, lookup_supplement_grade, search_health_knowledge) with noun_phrases (chat_with_sally, food_journal, health_insights, health_sync, metabolic_overview, supplement_grading), lacking a consistent convention.
The tool count of 10 is well within the typical 3-15 range and appears appropriate for a health assistant server covering nutrition, supplements, lab results, and general health queries.
The tool set covers nutrition, supplements, lab analysis, and health knowledge, but lacks obvious features like activity tracking, medication management, or appointment scheduling, leaving some gaps in a holistic health assistant.
Available Tools
10 toolsanalyze_lab_resultDInspect
Sally skill ($0.008/call)
| Name | Required | Description | Default |
|---|---|---|---|
| pdf_b64 | Yes | Base64-encoded PDF or image (≤10MB raw, ≤14MB base64). | |
| filename | No | Optional hint for mime detection (e.g. "panel.pdf"). Defaults to "document.pdf". | |
| llm_model | No | Override OpenRouter model id used for interpretation. Defaults to LAB_READING_MODEL on the OCR side. |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided; description only mentions a cost. Does not disclose that the tool processes base64-encoded PDFs, performs OCR, or interprets lab results. No behavioral traits beyond what's implied by the name.
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?
Extremely concise but at the expense of clarity. The description is a single short phrase that fails to convey the tool's purpose. Conciseness without meaningful content is under-specification.
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?
No output schema, no annotations, and complex functionality. Description does not mention return values, error handling, or processing steps. Incomplete for a tool that analyzes lab results from uploaded files.
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 100% as each parameter has a description. The tool description adds nothing beyond the schema, but the schema already provides sufficient parameter documentation. Baseline score maintained.
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?
Description only says 'Sally skill ($0.008/call)', which does not indicate what the tool does (e.g., analyze lab results from PDFs). Title is null, so no additional context. Sibling tools like 'chat_with_sally' are not differentiated.
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 guidance on when to use this tool versus alternatives. Missing any context about suitable scenarios or prerequisites.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
chat_with_sallyCInspect
Sally skill ($0.003/call)
| Name | Required | Description | Default |
|---|---|---|---|
| health | No | If true, the agent personalises with the calling user's mem0, lab results, CGM, sleep, vitals. Defaults to false (knowledge-only). | |
| message | Yes | ||
| language | No | BCP-47 short code (e.g. 'en', 'id'). Omit for auto-detect from the message. | |
| knowledge | No | Sally's knowledge brain — 'medical' for evidence-based clinical sources, 'tcm' for Traditional Chinese Medicine. | tcm |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
While no annotations exist, the description does not disclose behavioral traits such as side effects, permissions, or what happens when invoked. It only mentions cost, which is marginal for behavioral understanding.
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 too brief, missing essential information. It is not concisely informative but rather under-specified.
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?
For a tool with 4 parameters, no output schema, and no annotations, the description is severely incomplete. It fails to explain the tool's purpose or behavior, making it difficult for an agent to use correctly.
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 75%, so the schema already defines most parameters. The description adds no additional meaning beyond the schema, meeting the baseline for a tool with high coverage.
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?
Description only says 'Sally skill ($0.003/call)', which is vague and essentially restates the name. It does not specify the verb or resource, nor does it distinguish from siblings like analyze_lab_result or food_journal.
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 guidance is provided on when to use this tool versus alternatives. The description lacks any context about use cases or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
food_journalDInspect
Sally skill ($0.004/call)
| Name | Required | Description | Default |
|---|---|---|---|
| mime | No | Optional mime override (e.g. "image/png"). Auto-detected from magic bytes if absent. | |
| image_b64 | Yes | Base64-encoded food image (≤10MB raw, ≤14MB base64). |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must disclose behavioral traits, but it only mentions a cost and "Sally skill." It does not indicate it processes images, is read-only, or what side effects occur, leaving the agent blind to critical behavior.
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?
While extremely short, the description is under-specified and lacks structure. Every sentence should add value, but this single phrase neither introduces the tool nor aids understanding.
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 absence of an output schema and annotations, the description fails to explain return values, prerequisites, or expected outcomes. For a tool with two parameters, this is critically incomplete.
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 coverage is 100%, and the parameter descriptions (image_b64, mime) are clear and self-contained. However, the tool description adds no additional meaning beyond what the schema provides, so a baseline of 3 is appropriate.
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 only states "Sally skill ($0.004/call)", which fails to specify what the tool does. It does not mention food journaling, image analysis, or any actionable verb, making it misleading and essentially missing.
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 guidance is provided about when to use this tool versus its siblings (e.g., lookup_food, analyze_lab_result). The description offers no context or alternatives, leaving the agent without any basis for selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
health_insightsDInspect
Sally skill ($0.003/call)
| Name | Required | Description | Default |
|---|---|---|---|
| date | No | Local date YYYY-MM-DD. Default: today in `timezone`. | |
| type | No | Which insight to generate. 'auto' resolves from current local hour: 05:00-11:59 morning, 12:00-17:59 afternoon, else evening (00:00-04:59 rolls back to evening). | auto |
| language | No | BCP-47 short code (e.g. 'en'). Default: 'en'. | |
| timezone | No | IANA tz (e.g. 'Asia/Jakarta'). Default: UTC. | UTC |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations and a minimal description, there is no disclosure of behavioral traits such as data access, side effects, or limitations. The description adds no value 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.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely short but omits essential information about the tool's purpose. It is under-specified rather than concise.
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 lack of output schema and minimal description, the description is completely inadequate. It does not explain what insights are generated, what the tool returns, or how it relates to sibling tools.
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 coverage is 100% with clear descriptions for all 4 parameters. The description adds no additional meaning beyond what the schema already provides, so baseline 3 is appropriate.
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 'Sally skill ($0.003/call)' does not state what the tool does; it is missing and misleading, providing only a cost and a vague skill reference, not a specific verb or resource.
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 guidance is provided on when to use this tool versus its siblings (e.g., analyze_lab_result, chat_with_sally). The description lacks any context for appropriate usage.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
health_syncDInspect
Sally skill ($0.001/call)
| Name | Required | Description | Default |
|---|---|---|---|
| date_to | No | UTC date YYYY-MM-DD. Default: today. | |
| include | No | Subset to return. Omit for the six daily-aggregate categories. `cgm_minute` (real-time, minute-base CGM samples) is opt-in only — it is NOT included in the default set because the table is large. | |
| max_days | No | Relative window size (days). Ignored if date_from is provided. Hard cap: 90. | |
| aggregate | No | When true, returns the same payload core-go sends to langchain for morning-insights + metabolic-overview. | |
| date_from | No | UTC date YYYY-MM-DD. Default: today - 7 days. | |
| cgm_minute_to | No | ISO datetime — end of the window. Default: now. | |
| cgm_minute_from | No | ISO datetime — start of the high-resolution CGM window. Default: now - 24h. | |
| cgm_minute_resolution | No | Sampling resolution for the cgm_minute series. `1m` returns raw rows (≤1440 = 1 day). Coarser buckets average value/roc inside each bucket — useful for longer windows. | 5m |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must disclose behavioral traits. It does not state whether the tool is read-only, modifies data, or requires permissions. The description gives no information beyond a cost note.
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 extremely short (a 3-word phrase) but fails to convey the tool's purpose. It is under-specified rather than effectively concise, providing no actionable information.
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?
With 8 parameters, no output schema, and no annotations, the description is completely inadequate. It does not explain what the tool returns, the relationships between parameters, or the overall goal, leaving the agent unable to use it effectively.
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 100%, and each parameter is clearly described in the input schema. The description adds no additional meaning, but the schema already handles parameter semantics, meeting the baseline for high coverage.
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 'Sally skill ($0.001/call)' is vague and does not specify what the tool does. It fails to state a clear verb and resource, and does not distinguish it from sibling tools.
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 guidance is provided on when to use this tool versus alternatives like 'health_insights' or 'metabolic_overview'. The description lacks any context for usage.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
lookup_foodARead-onlyInspect
Look up nutrition for a named food or dish: macronutrients for the given portion plus Sally's Smart, Trap or Balance-with-Activity classification and its reasoning. Figures come from Open Food Facts and are estimates for a typical product of that name, not a specific branded item.
| Name | Required | Description | Default |
|---|---|---|---|
| food | Yes | Food or dish name, e.g. 'nasi goreng' or 'Greek yogurt, plain'. | |
| grams | No | Portion size in grams. Macros are scaled from per-100g values. |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate read-only, non-destructive behavior. The description adds important context: data source (Open Food Facts), that figures are estimates for typical products (not branded), and that it returns a classification with reasoning. This goes beyond annotations and helps the agent understand limitations.
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 purpose and returns, and concise with no wasted words. It could be slightly more structured but is effective.
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?
For a simple two-parameter tool with no output schema, the description adequately covers purpose, returns, data source, and caveats. It gives enough context for an agent to understand what the tool provides, though it does not detail output format.
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 coverage is 100% with descriptions for both parameters. The tool description does not add any additional semantic information beyond what the schema provides. Baseline 3 is appropriate.
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 verb 'Look up' and resource 'nutrition for a named food or dish', specifies what is returned (macros, classification, reasoning), and distinguishes it from siblings like lookup_supplement_grade. It also includes data source and limitation, making purpose very clear.
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 implies usage when nutrition info is needed, but does not explicitly state when to use this tool versus alternatives like analyze_lab_result or food_journal. No exclusions or alternative suggestions are provided, so usage guidance is minimal.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
lookup_supplement_gradeARead-onlyInspect
Look up a supplement by brand and product name in Sally's pre-graded corpus from the NIH Dietary Supplement Label Database, and return the stored grade, score and classification. If nothing matches closely, returns the nearest similar products with exact_match false. Cannot grade from a photo.
| Name | Required | Description | Default |
|---|---|---|---|
| top_k | No | How many corpus matches to return, nearest first. | |
| product | Yes | Brand and product name as printed on the label, e.g. 'Nature Made Vitamin D3 2000 IU'. Include the brand — matching is much weaker without it. |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations declare readOnlyHint and non-destructive. Description adds beyond: 'Cannot grade from a photo' and return behavior for non-exact matches. 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.
Is the description appropriately sized, front-loaded, and free of redundancy?
Three sentences, front-loaded with purpose. No unnecessary words. Every sentence adds value.
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?
No output schema, but description mentions return fields. Covers return behavior and key limitation. Adequately complete for a lookup tool with rich annotations.
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 coverage is 100%. Description adds meaningful constraints: product must be 'as printed on the label', brand inclusion advised, top_k returns nearest first. Adds value over 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?
Clearly specifies action 'look up', resource 'pre-graded corpus from NIH Dietary Supplement Label Database', and return values 'stored grade, score and classification'. Distinguishes from sibling 'supplement_grading'.
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?
Provides context: 'Cannot grade from a photo' and describes fallback behavior. Implicitly guides to use supplement_grading for new grades, but lacks explicit when/alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
metabolic_overviewDInspect
Sally skill ($0.005/call)
| Name | Required | Description | Default |
|---|---|---|---|
| date | No | Target date YYYY-MM-DD (defaults to today UTC). All other CGM data is fetched from the DB. | |
| timezone | No | IANA timezone for postprandial timing context (e.g. "America/New_York"). Defaults to UTC. |
Tool Definition Quality
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 only states a cost and offers no behavioral traits such as side effects, permissions, rate limits, or response characteristics. The tool remains a black box.
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 extremely short (one sentence) but severely under-specified. It does not earn its place by providing essential information; rather, it omits critical details.
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 no output schema, no annotations, and a minimal description, the tool is incomplete for an agent to use effectively. The purpose is unclear, and key context about behavior and results is missing.
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 100% with clear descriptions for date and timezone. The tool description adds no extra meaning beyond the schema, so the baseline score of 3 is appropriate.
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 'Sally skill ($0.005/call)' is a tautology and provides no information about what the tool does. It does not mention metabolism or overview, and fails to distinguish from sibling tools like health_insights or analyze_lab_result.
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 guidance is given on when to use this tool versus alternatives. There is no mention of context, prerequisites, or exclusions, leaving the agent without any usage framing.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
search_health_knowledgeARead-onlyInspect
Search Sally's preventive and metabolic health library and return matching source passages with citations. Covers clinical articles published by A1C plus NCCIH supplement monographs. Returns retrieved text for the caller to reason over; it does not write an answer or give medical advice.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | The health question to find evidence for, in natural language. Pass the substantive question only — do not include conversation history or anything about who is asking. | |
| top_k | No | Maximum number of passages to return, ranked by semantic similarity. |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and destructiveHint=false, so the tool is clearly safe. The description adds behavioral context by specifying that it returns passages with citations and does not generate answers, and it details the sources covered (A1C articles and NCCIH monographs), which goes beyond the annotations.
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 long, front-loads the core purpose, and adds clarifying scope and behavioral notes without any extraneous words. Every sentence 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 there is no output schema, the description adequately explains that the tool returns 'matching source passages with citations' and 'retrieved text for the caller to reason over'. This is sufficient for an agent to understand the tool's output without additional detail.
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 100%, so both parameters are already documented. The description adds extra guidance for the 'query' parameter: 'Pass the substantive question only — do not include conversation history or anything about who is asking'. This improves parameter usage beyond the schema's basic description.
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 verb 'search', the resource 'Sally's preventive and metabolic health library', and what is returned: 'matching source passages with citations'. It distinguishes the tool from siblings by specifying the library's scope (clinical articles and NCCIH monographs), which differentiates it from other health tools like 'lookup_food' or 'analyze_lab_result'.
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 explicitly states that the tool returns text for the caller to reason over and does not write an answer or give medical advice, which guides appropriate usage. However, it does not explicitly list when not to use it or suggest alternative tools, though the sibling list provides implicit context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
supplement_gradingDInspect
Sally skill ($0.008/call)
| Name | Required | Description | Default |
|---|---|---|---|
| language | No | BCP-47 short code (e.g. 'en', 'id'). Omit for auto-detect from the stack text. | |
| use_labs | No | If true (default), the agent may read the calling user's lab results to surface biomarker-relevant gaps. Set false for a stack-only audit with no PHI. | |
| supplements | Yes | The supplement stack to audit — names + doses as free text or a list (e.g. "vitamin D 5000IU, magnesium glycinate 400mg, fish oil 1g"). |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, and the description only mentions cost. There is no disclosure of behavioral traits such as side effects, permissions, or data handling. The description fails to inform the agent about what happens when the tool is invoked.
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 extremely short but fails to convey necessary information. Under-specification is not conciseness; the single sentence does not earn its place because it lacks substantive content.
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 tool has 3 parameters, one required, and lacks an output schema, the description is grossly incomplete. It does not explain the tool's behavior, return values, or how it relates to sibling tools, providing no actionable context for an AI agent.
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 100%, so the input schema already documents all parameters. The description adds no additional meaning beyond the schema, meeting the baseline for this dimension.
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 "Sally skill ($0.008/call)" does not state what the tool does. It provides a cost and a name but lacks a verb or resource, making it impossible to infer the tool's purpose from the description alone.
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?
There is no guidance on when to use this tool versus alternatives like lookup_supplement_grade. The description offers no context for selection, leaving the agent without any usage criteria.
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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