MoleCare MCP Server

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Server Quality Checklist

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Latest release: v1.0.2
Disambiguation2/5
Many tools have overlapping purposes, especially the health/status cluster: get_app_status vs get_all_apps_status, get_service_health vs get_system_health, and get_deployments vs get_deployment_status are easy to confuse. The medical and ML tools are more distinct, but the sheer number of similar status endpoints makes tool selection risky.
Naming Consistency4/5
Most tool names follow a get_<domain>_<resource> pattern and are consistently snake_case, so the naming style is recognizable. There are minor deviations like check_server_health, clear_cache, and get_ec2_instance vs get_ec2_instances, but the overall pattern stays readable.
Tool Count1/5
Fifty-3 tools is an extreme number for one MCP server, well beyond the 3-15 well-scoped range. Many tools are near-duplicates in areas like app status, database diagnostics, and EC2 health; the set creates a large navigation and prompt-scope burden.
Completeness2/5
The tool surface is almost entirely read-only. It can inspect moles, medical concepts, ML runs, databases, and infrastucture, but there are no core write operations for users, moles, risk assessments, or system actions. An agent cannot follow a lifecycle from action to completion, such as recording a new mole or remediating an unhealthy service.
Average 3.7/5 across 53 of 53 tools scored. Lowest: 2.9/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 7 commits in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI status not available
This repository is licensed under Apache 2.0.
This repository includes a README.md file.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
Tip: use the "Try in Browser" feature on the server page to seed initial usage.
Add a glama.json file to provide metadata about your server.
If you are the author, simply .
If the server belongs to an organization, first add glama.json to the root of your repository:
```
{
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}
```
Then . Browse examples.
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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?
With no annotations, the description must disclose behavior itself. It states the tool returns 'detailed information about skin conditions including severity and category,' but it does not explain what happens for non-skin concepts, invalid codes, or what 'detailed' means. The description also ambiguously blends 'medical concept' with 'skin conditions,' creating potential misdirection.
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?
Three short sentences, with the core value stated first. The final sentence adds marginal value and is slightly redundant, but the overall length is appropriate.
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?
For a tool with one required parameter and no output schema or annotations, the description gives a reasonable starting point but leaves key behaviors unclear: whether non-skin SNOMED codes work, what fields the return contains, and whether any errors or empty results can occur. An agent relying solely on this description could under- or over-expect its usefulness.
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 100%, and the example value ('372244006' for melanoma) adds helpful context. However, the description adds no further meaning beyond 'by SNOMED code,' so it does not compensate beyond the baseline.
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 identifies a clear action ('look up') and a specific resource ('medical concept by SNOMED CT code'). It differentiates from the search-like siblings by requiring an exact SNOMED code, though it narrows the results to skin conditions without explaining why, which weakens precision.
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 only usage guidance is the generic sentence 'Use this to provide accurate medical terminology,' which does not explain when to choose this tool over nearby alternatives like search_medical_concepts or map_snomed_to_icd10. No context, alternatives, or exclusions are provided.
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 exist on this tool, so the description carries the full burden of behavioral disclosure. Beyond name-dropping four metric types, it says nothing about output structure (time series vs. point estimates), aggregation (averages percentiles leads), or the application of the default/max window (periodHours behavior is only in the schema). The description neither confirms nor addresses this user's key uncertainty—whether active users for example may be sampled, deduplicated, or in real-time.
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?
A single sentence of fourteen words. It moves directly from verb to resource to the list of metrics, with no filler, no repetition of schema fields, and no boilerplate. Every word adds information.
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?
This is a low-complexity tool (2 params, 1 enum, no nested objects), and the description plus schema cover the inputs. The gap is that there is no output schema, and the description only lists metric families but does not state the shape—will the result be a time series per metric, a single scalar per metric, or a summary object—which an agent must infer when consuming the output.
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 good factual docs for both properties (app enum and periodHours default/max). The description does not add any extra interpretation to the parameters—it only lists result metric names, which overlap with what the period parameter expects—so it conforms to the baseline 3 where the schema already carries the semantic weight.
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 a specific verb 'Get' and a specific resource ('application metrics'), and it enumerates concrete metric families—request counts, error rates, response times, and active users—so an agent can tell what data it returns. Its clarity is marred only by not differentiating from nearby siblings such as get_app_errors or get_database_metrics, which could plausically overlap these metric families.
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 gives no conditional context about when to choose this tool over get_app_errors, get_app_status, get_database_metrics, get_ec2_metrics, or get_app_status. With more than a dozen sibling tools addressing apps, a boundary such as 'use this for aggregated application-level KPIs; use get_app_errors for detailed error lists' would significantly reduce ambiguity.
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 behavioral transparency, but it is nearly a restatement of the tool name. It does not disclose what the status represents, whether this is a read-only operation likely to be hit, or any other behavior beyond 'get status.' It only adds that the environments are staging and production.
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 one efficient sentence with no filler. It front-loads the verb and resource, and the target environments. Every word adds meaning, and it takes very little time to read.
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 definition is minimum for a trivial getter with a single enumerated parameter, but the lack of an output schema means the agent does not know what the response contains. There is also no guidance around environment defaulting or what a status includes. For a tool among many similarly-named getters, this is a barely adequate level of context.
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 100%, so the parameter 'environment' is fully documented with an enum and description. The tool description only adds that the environments are staging and production, but that is also in the enum. No additional semantic depth is provided, so the baseline 3 is appropriate.
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 a specific verb ('Get') and resource ('deployment status') and explicitly narrows scope to 'staging and production environments.' It is clear and actionable, but it does not explicitly distinguish itself from sibling tools like 'get_deployments', so it lacks some differentiation.
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 offers no when-to-use or when-not-to-use guidance. It does not name alternatives, mention context such as deployment-related checks, or suggest when to prefer another look like 'get_deployments'. The agent must infer usage from the name alone.
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 behavioral disclosure. It conveys that the tool is a read-like operation and names the returned data categories, but it omits any behavior around ordering, time ranges, pagination, performance cost, or authentication requirements. It is not misleading, but it is thin for a data retrieval tool.
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 one compact sentence with no filler. The action, resource, and expected result are front-loaded, making it easy to parse quickly.
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 definition is mostly sufficient for a simple filtered read tool: it names the target and the returned fields, and the schema covers all parameters. However, with no annotations, no output schema, and no sibling routing, it leaves the agent without clarity about sorting, default ordering, or when to prefer other run-related tools.
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%, so each parameter (limit, filter, experimentId) is already documented structurally. The description adds no new parameter-level meaning beyond the schema, so a baseline score of 3 is appropriate.
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 clearly identifies a specific verb ('Get') and resource ('MLflow runs for an experiment'), with output components named: metrics, parameters, and status. It does not explicitly name sibling tools for differentiation, but the MLflow-scoped phrasing makes the purpose distinct from generic pipeline/training run tools.
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?
There is no guidance about when to use this tool versus related alternatives like get_training_runs, get_pipeline_runs, or compare_model_runs. No context is provided about scenarios, exclusions, or prerequisites, so an agent must infer usage from the name and schema alone.
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 behavioral disclosure burden. It only states what information is returned and does not mention ordering, default behavior, read-only nature, failure modes, or any side effects. This minimal disclosure is insufficient for full behavioral transparency.
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?
One short, front-loaded sentence that immediately states the action and the key returned content. Every word contributes to meaning, and there is no redundant filler.
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 is simple with one optional parameter and no output schema, so a brief description is reasonable. However, it leaves out useful context such as ordering, pagination, and default behavior beyond the schema. It is adequate but has clear gaps.
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 only parameter, 'limit', is already fully described in the schema with its default value, so schema description coverage is 100%. The description adds the word 'recent' but provides no additional parameter semantics beyond the schema, meeting the baseline.
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 uses a specific verb ('Get') with a clear resource ('recent releases') and names the payload contents ('version info, release notes, and assets'). This makes the purpose easy to identify, though it does not explicitly differentiate the tool from potentially overlapping siblings like get_app_versions.
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 implies usage when one wants recent release information, but provides no explicit guidance on when to choose this tool over alternatives or any exclusions. The large sibling list includes version-related tools, but no distinguishing usage context is given.
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 full behavioral disclosure burden. It conveys a read-only status retrieval and lists important result areas, which is reasonable. However, it does not clarify how the optional namespace affects results or whether the status is cluster-wide by default.
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?
Single sentence, clear and front-loaded. Every phrase contributes: 'Kubernetes cluster status' gives the resource, and the list gives expected contents without 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?
For a one-parameter read tool, the description and schema are mostly sufficient. But with no output schema and a large cluster of sibling status tools, the lack of usage guidance and namespace-filtering clarity reduces completeness.
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% for the single optional parameter, and the schema already explains the namespace with examples. The tool description adds no additional parameter meaning, so the baseline of 3 applies.
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 a specific verb+resource: 'Get Kubernetes cluster status', with concrete sub-resources (nodes, namespaces, deployments, pod health). It clearly identifies the tool's domain, though it does not explicitly differentiate from closely related siblings like get_deployments or get_service_health.
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 offers no guidance about when to use this tool instead of the many sibling status/health tools, nor any exclusions or alternatives. The intended use is only implied by the Kubernetes terminology, leaving an agent to infer routing.
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 present, so the description carries the full behavioral transparency burden. 'Retrieve' safely implies a read operation, but the description does not disclose expected return shape, behavior for missing entities, errors, latency, or whether it uses the live online store.
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 entire definition is one concise sentence that front-loads the primary action and resource. Every held phrase adds identity, and there no redundant 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?
For a tool with only two fully documented parameters, this description is minimally workable: an agent can construct an invocation. However, without an output schema or annotations, it should describe the expected returned values and missing-entity behavior to be fully complete.
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 100% and the entityKey property already includes an example, so the baseline is 3. The description adds the relationship between entity and online features but does not add meaningful parameter-level meaning beyond the schema.
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 states a specific verb and resource: retrieving online features for an entity from Feast feature store. It is clear enough to distinguish this tool from siblings like get_feature_views, get_feature_view_details, and get_feature_freshness, which concern metadata or freshness rather than feature values.
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 usage guidance is provided about when to call this tool versus other Feast-specific siblings such as get_feature_freshness or get_feature_view_details. The appropriate context is only implied by the special term 'online features'.
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 suggests a read-only 'Get/Shows' behavior and summarizes the output fields. However, it does not disclose ordering, defaults beyond the schema, possible pagination, or side effects, so behavioral transparency is only basic.
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, focused sentences with no filler. The action and resource are front-loaded, and the second sentence concisely describes the result content.
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?
For a simple optional-parameter list retrieval, the description is mostly adequate, and the schema covers the input arguments. But because there is no output schema and no sibling differentiation, it leaves ambiguity about ordering, scoping, and how this tool compares with get_pipeline_runs or get_mlflow_runs.
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%, so the description does not need to explain the limit or flowName parameters. The description only adds general output context, not parameter-level meaning.
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 names a specific resource ('Metaflow ML training pipeline runs') and the informational fields returned ('training status, parameters, and duration'). It is clear, but it does not explicitly differentiate this tool from siblings like get_pipeline_runs or get_mlflow_runs.
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 usage guidance is given. The description does not say when to choose this tool over get_pipeline_runs, get_mlflow_runs, or get_pipeline_summary, and there are no exclusions or alternative references.
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 bears the full burden of behavioral disclosure, and it only states a 'get' plus the field list. It does not explain how 'calculated risk level' is derived or whether it is computed on demand vs precomputed, does not say what happens when the user's profile has missing fields (e.g., sun exposure, family history), and gives no caveats that would matter for a medical decision-support tool.
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?
Two tight sentences with zero wasted words: the first carries the technical contract (verb, resource, content), the second says when to use it. The 'including' wording also signals a non-closed factor list without bloating the text.
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?
With no output schema and no annotations, the description is the only shape for the return object; the field list is helpful but incomplete — the scale/format of 'calculated risk level' is undefined and edge-behaviors (unknown user, incomplete profile) are not addressed. For a single-parameter lookup the input contract is fully self explains, so it's invokable, but the output disposition is left to runtime discovery.
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?
Scalar schema description coverage is 100%: userId is already documented as 'the user's unique identifier,' so the description adds no param-level info and doesn't need to. Baseline 3 applies because the schema fully roughly covers the only parameter and there is no secondary param ambiguity.
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 has a specific verb resource pair — 'Get a user's skin cancer risk factors' — and enumerates the returned content (skin type, family history, sun exposure habits, calculated risk level), so the aim is unambiguous. The 'a user's' phrasing implies a per-user scope that differs from condition-level sibling tools (e.g., get_condition_risk_factors), but it never names/contrasts any sibling explicitly, so the distinction is left to inference.
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 last sentence 'Use this for personalized advice' offers a clear context where the tool fits — interactive user-advice flows — which is better than nothing. However, it names no alternatives (get_condition_risk_facters, assess_risk_from_factors, get_user_moles), no when-not-to-use conditions, and no decision rules among them.
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 behavioral burden, and it does indicate that this is a read-oriented metrics retrieval operation. However, it does not add behavioral nuance such as whether metrics are pre-aggregated across the full period, whether the call can be expensive, or whether it purely reads cached data. This is acceptable but not richly transparent.
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 definition is a single front-loaded sentence: 'Get database performance metrics' leads immediately with the core action, and the following list clarifies the return envelope. No filler, no repetition of the tool name, and no unnecessary ambiguity.
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 tool with one optional, schema-documented parameter and no nested structures or required fields, the description plus schema is enough to call it confidently. It lists enough metric categories so an agent can predict the shape of the result. The main gap is not positioning it among the many sibling monitoring tools, but that concern is captured under usage guidance.
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 coverage for periodHours is 100%, including default and maximum values, so the parameter is already well documented. The description adds no detail about how periodHours shapes the metrics or whether the period is applied uniformly to every returned metric. Baseline 3 is appropriate.
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 is clear about the action and resource: 'Get database performance metrics' followed by concrete categories like query stats, storage usage, cache hit ratios, and deadlocks. It stops short of distinguishing itself from nearby siblings such as get_database_status, get_slow_queries, or get_table_stats, so it is not a full 5.
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?
There is no guidance on when to call this tool versus alternatives like get_database_status, get_slow_queries, or get_connection_pools. The description omits exclusions, prerequisites, and any indication of which sibling covers narrow metrics vs overall performance.
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?
No annotations are provided, so the description carries the behavioral disclosure burden. It states that the tool returns possible conditions, risk level, and recommendations, which is meaningful, but it does not mention limitations, whether it is advisory-only, or how it behaves with missing inputs. For a classification tool with medical implications, more caveats would be helpful.
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 only three sentences, with no filler. It opens with the main action, then states the output, and ends with a usage hint, all efficiently.
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 no output schema and no annotations, so the description is responsible for explaining the outcome; it does name outputs. However, a medical risk classifier arguably should provide more context about how authoritative or complete the classifications are, and possible conditions/recommendations are not further described.
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%, so the parameters are already individually documented. The description's mention of 'ABCDE features' maps to the schema, but it adds no parameter-specific detail beyond the schema, earning the baseline.
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 clearly specifies a verb ('Classify') and a resource ('a lesion based on its ABCDE features'), and it states the output: possible conditions, risk level, and recommendations. It is distinct enough from siblings like get_mole_analysis, but it does not explicitly call out a sibling to distinguish itself.
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 sentence 'Use this to help interpret mole analysis results' gives useful context for when the tool applies, but it does not compare with alternatives or explain when not to use it. With several related medical tools nearby, explicit routing guidance would improve the description.
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 the full burden of behavioral disclosure. It tells the agent the core behavior is comparing metrics from MLflow runs, which suggests an analytic, non-mutating operation, but it does not disclose the output format, potential side effects, permission concerns, or error behavior around invalid run IDs.
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. Every phrase contributes meaning: the operation ('Compare'), the data ('metrics'), the objects ('multiple MLflow runs'), and the purpose ('evaluate model performance').
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 single parameter is fully covered by schema and the description provides a clear scenario for use, so an agent can call the tool without missing inputs. However, no output schema exists and the description does not specify what the comparison result will contain or how it will be presented, leaving an important knowledge gap for an agent that needs to interpret the response.
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 already documents 'runIds' with the description 'Array of run IDs to compare', so the schema coverage is 100%. The tool description adds value by clarifying these are MLflow runs and that multiple runs are expected, but it does not specify ID format, limits, or how many runs are valid beyond the implied 'multiple'.
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 uses a specific verb ('Compare'), names a resource type ('metrics between multiple MLflow runs'), and gives an explicit purpose ('evaluate model performance'). This lets an agent distinguish it from retrieval-focused siblings like get_mlflow_runs, though it does not name a sibling explicitly.
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 'to evaluate model performance' clause gives a clear evaluative context, implying this tool is used for run comparison rather than listing or retrieving runs. However, it does not state explicit exclusions or reference alternative tools such as get_mlflow_runs, get_pipeline_runs, or compare_moles, so the agent must infer when the tool is the best choice.
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?
There are no annotations, so the description carries the transparency burden. It does disclose the read-only nature ('Get information', 'Shows') and the general output shape ('potential progression paths'), but it omits possible return details such as how paths are structured, what happens for unknown SNOMED codes, and any auth or failure considerations.
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 short and readable with a useful concrete example. There is a slight redundancy between 'how a skin condition can progress' and 'Shows potential progression paths', but overall it is efficient and front-loaded with the main purpose.
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?
This is a simple one-parameter getter with a fully documented input schema, so the description does not need to cover much. The 'potential progression paths' statement, plus the example, gives enough context for an agent to invoke it correctly, though a note about the expected return structure would make it marginally more complete.
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 already provides 100% description coverage for the only parameter, snomedCode, so the description does not need to add much. The description adds no new semantics about the parameter beyond the schema's clear declaration that it is a SNOMED CT code.
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 clearly states a specific verb and resource ('Get information about how a skin condition can progress') and gives an illustrative output example ('dysplastic nevus to melanoma'). It is distinct enough from nearby siblings like get_condition_risk_factors, though it does not explicitly name or contrast them.
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 should be used when someone asks how a condition can progress over time, rather than for risk factors, diagnoses, or general medical lookups. However, it gives no explicit when-to-use guidance, exclusions, or named alternatives among the sibling tools.
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?
No annotations are provided, so the description carries the behavioral disclosure burden. It does disclose that the tool returns factors and lists example types, but it does not mention behavior for invalid SNOMED codes, empty results, or the response format. 'Get' implies non-mutation but is not explicit.
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 and every sentence contributes meaningful information. It front-loads the purpose and then adds useful output content with examples, with no filler or redundancy.
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?
This is adequate for a one-parameter lookup tool, but there is no output schema and no guidance on edge cases like missing SNOMED codes or conditions without known risk factors. It gives the agent enough to make a baseline call but leaves response-shape expectations and failure behavior unspecified.
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 only parameter snomedCode is already documented with a clear schema description ('SNOMED CT code of the condition'), and schema description coverage is 100%. The description adds no additional parameter-level semantics beyond confirming the condition relationship.
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 verb and resource: 'Get risk factors associated with a specific condition.' It includes concrete example factors (family history, skin type, UV exposure), and it is distinguishable from sibling tools like get_user_risk_factors and assess_risk_from_factors.
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 gives no guidance on when to choose this tool over alternatives such as get_user_risk_factors or assess_risk_from_factors. It implies condition-centric use, but it does not state exclusions, preconditions, or preferred scenarios.
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?
No annotations are provided, so the description carries the burden. It does indicate the read-oriented nature via 'Get' and reveals what the tool cocts/returns (status, latency, connection pools). However, it does not disclose side effects, resource intensity of checking all databases, access premissions, or latency units.
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 one concise, well-formed sentence with no filler. It front-loads the primary purpose and then adds the most relevant details (PostgreSQL, Redis, connection pools, latency) in the right order.
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 operation is simple, requires no parameters, and the description states what will be returned in terms of covered databases and health dimensions (status, connection pools, latency). It could be slightly richer about the output format, but it is adequate for a zero-input monitoring endpoint.
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?
The tool has zero parameters, and the schema already says none are required. The description does not add parameter details, but with no parameters there is nothing meaningful to explain; baseline 4 is appropriate.
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 uses a specific verb and resouce ('Get status of all databases') and gives concrete examples of what is covered (PostgreSQL, Redis, connection pools, latency). It is clear, but it does not explicitly distinguish itself from siblings like get_database_metrics or get_connection_pools that overlap in content.
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 about when to choose this tool versus nearby alternatives (e.g., get_database_metrics, get_connection_pools, get_health). There is no explicit 'when to use' or 'preferer another tool if' statements.
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 the full behavioral disclosure burden. It does convey the scope ('across namespaces') and the output content, which implies a read-only list operation, but it does not mention default behavior when namespace is omitted, potential cluster-wide cost, or any errors that might occur.
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 that efficiently covers purpose, scope, and key returned fields. Every phrase contributes meaningful selection and invocation information without redundancy.
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 low-complexity tool with one optional parameter and no output schema, this description is nearly complete: it names the resource, the filtering capability, and the main output fields. The main missing piece is explicit guidance on how this differs from the similarly scoped sibling tool get_deployment_status.
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?
The single parameter is already well documented in the schema with 100% coverage. The description adds useful meaning by clarifying that the tool operates 'across namespaces' and shows status details, which helps an agent interpret the optional namespace filter as a scope restriction rather than a required selector.
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 clearly states the verb ('Get status') and resource ('Kubernetes deployments'), and specifies the type of returned data: replica counts, images, and health status. It is distinct enough from the broader sibling list, though it does not explicitly differentiate itself from the similarly named 'get_deployment_status'.
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 gives no guidance on when to prefer this tool over siblings such as get_deployment_status, get_kubernetes_status, or get_service_health. It implies a broad read across namespaces but provides no exclusions or selection criteria.
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?
There are no annotations, so the description carries the burden of behavioral disclosure. It does indicate the tool produces statistics and store types, implying a non-mutating informational call, but it does not explicitly state read-only semantics, data freshness, or potential costly aggregation behavior. This is adequate for a simple zero-parameter stats tool.
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 that names the resource, the operation, and the kind of information returned. There is no fluff or redundant repetition of the tool name.
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 zero-parameter, no-output-schema read tool, the description gives enough information to select it among many siblings: 'overall statistics', 'Feast feature store', and 'counts and store types'. It could explain the exact returned shape, but the tool's simplicity makes the current description nearly sufficient.
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?
With zero parameters and 100% schema description coverage, there are no parameter semantics to explain. The description correctly focuses on what the statistics include rather than any inputs.
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 a specific verb ('Get') and resource ('statistics about the Feast feature store') and mentions the content ('counts and store types'). It is distinct enough from sibling tools like get_feature_views and get_feature_freshness by describing overall store stats rather than individual feature views, though it does not explicitly name alternatives.
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 given about when to use this tool versus related tools such as get_feature_views, get_feature_freshness, or get_online_features. The description implies a broad statistics use case but neither states it nor names alternatives.
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 behavioral burden. It correctly implies a read-only status fetch and lists the returned dimensions, but it does not mention any side effects, dependencies, latency expectation, or what 'status' health may or may not cover.
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?
One compact sentence efficiently states what the tool returns and names the target resource. There is no filler or repeated structural information.
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 zero-input status-checking tool, the description gives enough information about the return values to guide an agent. The lack of an output schema means the description is the only source for expected content, and it covers health, version, response time, and uptime; still, behavior around failure/discovery is not addressed.
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?
The tool has zero parameters, so the description has no parameter semantics to explain. The schema is trivially covered, and no parameter ambiguity exists.
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 uses a specific verb and resource ('current status of the MoleCare web application') and lists the meaningful returned aspects: health, version, response time, and uptime. It distinguishes itself from database/mobile/Kubernetes health tools, though it does not explicitly set itself apart from get_app_status or get_system_health.
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 about when to prefer this tool over siblings like get_app_status, get_system_health, get_service_health, or get_obile_api_status. The usage context must be entirely inferred from the tool name and description.
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 present, the description carries the behavioral disclosure burden. It does usefully reveal that output includes occurrence counts and error details. However, it doesn't explain how 'recent' is bounded, whether results are aggregated per endpoint, or any call-time defaults beyond those in the schema, leaving the behavior only partially transparent.
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?
A single, compact sentence front-loads the core purpose and lists the key data fields. Every phrase is useful, and there is no filler or repetition of schema content.
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 read-focused tool with two optional parameters and a fully documented schema, the description provides enough context to call the tool correctly and interpret its output. A minor gap is the lack of explanation about the format of 'occurrence counts' or any grouping behavior, but this doesn't block invocation.
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 100%, so the app enum and limit parameter are already well documented. The description does not add new meaning to the parameters, but the schema already fully explains them, so no compensation is needed.
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 states a clear action ('get') and resource ('recent application errors') and enumerates the returned fields ('error messages, endpoints, status codes, and occurrence counts'). This distinguishes it well from siblings like get_app_status or get_app_metrics, which focus on health and metrics rather than error details.
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 offers no guidance on when to prefer this tool over alternatives such as get_app_status, get_app_metrics, or get_service_health. There is no mention of when not to use it or what to do if errors span multiple apps, so the agent is left to infer the tool's role from its name.
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?
No annotations are provided, so the description carries the disclosure burden. The 'Get' verb strongly implies a read-only retrieval, and 'detailed information' suggests the kind of response expected. However, the description does not mention edge cases, permission requirements, or what happens when the named feature view does not exist.
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 or repetition. It puts the action and target immediately up front, which makes it easy for an agent to parse quickly.
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?
For a simple one-parameter getter, the description plus input schema is probably enough to invoke the tool correctly. Still, since there is no output schema and no annotation information, the description does not explain what the returned details will contain or how errors or missing references are handled.
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 input schema is fully documented at 100% coverage, and the 'name' parameter is already explained as 'Name of the feature view'. The description adds only 'specific feature view', which reinforces the parameter's role without adding meaning beyond the schema.
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 the action ('Get'), the resource ('feature view'), and the scope ('specific'), so the agent can identify this as a single-item detail retrieval rather than a listing tool. It does not explicitly name sibling get_feature_views, but the 'specific' wording is enough to distinguish it from plural/list-style siblings.
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 phrasing clearly implies the usage: use this when you need detailed information about one particular feature view, as opposed to listing all feature views with get_feature_views. It does not provide explicit when-not-to-use instructions or alternatives, but the context is reasonably clear.
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?
Because there are no annotations, the description carries the burden of behavioral disclosure. It does disclose sorting behavior by total execution time, but it does not mention read-only nature, response shape, limits, pagination, or thresholds beyond what the schema already captures.
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?
It is a single sentence with no filler. It is front-loaded with what the tool does and adds a useful sort detail without unnecessary branching or repetition.
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 two-optional-parameter read-style list tool, the description is adequate and the schema fully documents parameters. However, the return format is not specified, and because no output schema or annotations exist, slightly more detail about what each returned query looks like would make it more complete.
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 complete description for both parameters (limit and minDurationMs), so the description's additional value is naturally limited. The description does not explain parameter semantics beyond the schema, and the sort-order hint is not targeted at individual parameters.
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 names a specific resource and operation: getting slow database queries sorted by total execution time. This clearly describes the tool's result, but it does not explicitly contrast it with related database siblings such as get_database_metrics or get_table_stats.
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 'that may need optimization' implies this is used in performance tuning. However, it does not explicitly state when to pref on analytics or metrics. No exclusions or alternative-method guidance is provided.
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?
No annotations are provided, so the description carries the burden of explaining behavior. It does state what information is returned, and 'Get' implies read-only, but it does not explicitly disclose whether it reads system catalogs, whether permissions are needed, or whether it modifies or refreshes statistics.
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 one compact sentence that front-loads the core purpose and lists the specific statistic types without filler. Every word contributes useful information.
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 low-complexity tool with one optional parameter and a schema that documents the default, the description provides adequate context to invoke it correctly. It could be slightly richer about output shape or exact scope, but the essentials are present.
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%, and the single optional 'schema' parameter already documents its purpose and default. The tool description adds no extra meaning about the parameter beyond what the schema provides, 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 uses a specific verb ('Get') and resource ('database tables') and lists concrete kinds of information: row counts, sizes, and vacuum/analyze status. This clearly distinguishes it from broader database health/metrics sibling tools.
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 gives no guidance about when to use this tool versus alternatives like get_database_metrics or get_database_status. It also does not state any exclusions or prerequisites for table-level statistics.
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 present, so the description carries the full burden for behavioral disclosure. It states the action but does not explain that clearing the cache is destructive to cached data, may cause performance impact, or that partial clears via pattern are possible. This is a notable transparency gap for a mutation-like tool.
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 one concise sentence that states what the tool does and when to use it. It is front-loaded and contains no filler or redundant restatement of the tool name.
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 description is adequate for a simple optional-parameter tool with a single purpose. However, because it performs a destructive action on the cache, additional context about consequences, scoping, and whether this is safe in production would improve completeness.
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 already fully describes the optional pattern parameter with an example, so the description does not need to duplicate it. The description itself adds no parameter guidance, but schema coverage is 100%, 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 uses a specific verb (Clear) and a specific resource (the MCP server's internal cache), making the tool's function immediately obvious. The sibling list is dominated by getters and search tools, so the cache-clearing purpose is clearly differentiated.
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 use the tool: 'Use when you need fresh data after known changes.' It does not mention when not to use it or point to alternatives, but the core usage context is clear enough.
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?
No annotations are provided, so the description carries the behavioral transparency burden. It tells the agent the tool returns status across all listed application types, which is useful scope information. However, it does not disclose whether the operation is non-mutating, the exact meaning of 'status', or any authorization or aggregation behavior beyond the tool name and scope.
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 with no filler. It front-loads the core purpose, then adds a clear usage intent. Every clause earns its place.
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?
For a tool with only one optional schema-covered parameter, the description is mostly complete for selecting the call. However, with no output schema and no annotations, it does not define what status data is returned, and it could benefit from a note distinguishing it from the many similar status/health siblings in the tool list.
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 already 100%, and the single parameter has an enum with descriptions including the default. The description adds no additional parameter semantics beyond what the schema already provides, so the baseline 3 is appropriate.
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 names a concrete verb plus resource: 'Get status of all MoleCare applications' and enumerates the app types covered. It is clear at a glance, but it does not explicitly distinguish itself from sibling aggregate tools like get_all_apps_status or get_system_health, so full sibling differentiation is missing.
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 use it: 'Use this to check overall system health.' This gives clear intended context. It does not mention alternatives or exclusion scenarios, so it misses the highest level of usage guidance.
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 present, the description carries the full transparency burden. It correctly signals a read-only list operation and identifies the returned data as versions and stages, but it does not mention pagination, authentication needs, or empty/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?
One short sentence contains the action, resource, scope, and output data. It is front-loaded and free of filler or redundant phrasing.
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?
For a zero-parameter tool with no output schema, the description provides the core subject and key output fields, but not the exact result structure or edge-case behavior. It is adequate for selecting the tool but leaves some context gaps for an agent that must rely purely on the description.
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?
The input schema has zero parameters and an empty properties object, so there are no parameters for the description to explain. The baseline score for a no-parameter tool is appropriate here.
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 uses a specific verb ('List') and names the exact resource: registered ML models in the MLflow model registry, including versions and stages. This makes it clearly distinguishable from sibling tools like get_mlflow_experiments, which target a different MLflow resource.
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 such as get_mlflow_experiments. It states what the tool does but gives no context, exclusions, or alternative-selection criteria.
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 the full burden. It communicates that the tool performs a mapping/lookup, implying a non-mutating operation. Still, it does not describe behavior around invalid codes, missing mappings, or whether multiple ICD-10 codes are returned, which would be useful.
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?
Two short sentences with the core mapping statement first. The second sentence adds some context but is slightly vague and could be omitted. Overall it is concise and well-structured.
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 one-parameter mapping tool, the description captures the primary action and output type. There is no output schema, but the expected result (ICD-10 diagnosis codes) is directly in the first sentence. Some edge-case behavior is not covered, but the tool's simplicity makes the description reasonably complete.
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% because the schema already contains 'SNOMED CT code to map'. The description adds no additional meaning beyond restating 'SNOMED CT code'. Baseline 3 is appropriate when the schema fully documents the parameter.
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 states a specific verb ('Map') and resource ('SNOMED CT code to ICD-10 diagnosis codes'). This clearly distinguishes the tool from sibling medical lookup and search tools, as none of the siblings perform this code-mapping function.
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 'Useful for understanding official diagnosis classifications' gives a general use context. However, it does not explicitly say when to use this tool instead of alternatives like search_medical_concepts or lookup_medical_concept, and there are no exclusions or prerequisites.
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 behavioral disclosure burden. It does state the search behavior and return type ('Returns matching SNOMED CT concepts'), which is helpful. However, it does not explicitly say the operation is read-only or describe any limitations, pagination, or match semantics.
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?
A single, front-loaded sentence conveys the search action, the fields searched, and the expected result type with no wasted words. It gives the agent necessary context quickly.
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 tool with one parameter and no output schema, the description is quite complete: it names searchable fields, the result target (SNOMED CT concepts), and the domain restriction. A minor gap is not describing the exact structure of the returned concepts, but the tool complexity is low.
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?
The schema already documents the one parameter with examples, and schema description coverage is 100%, so the baseline is 3. The description adds meaningful beyond-schema nuance by stating the query matches both 'name or description', clarifying how the search behaves.
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?
States a clear action ('Search for medical concepts') and resource, with a specific scope ('SNOMED CT concepts for dermatology conditions'). It is distinct in function, but does not explicitly differentiate from sibling tools like search_medical_info or lookup_medical_concept, so it misses the top tier.
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 when to use the tool: when you need to find medical concepts by name or description. It does not mention exclusions or alternative tools, so an agent is left to infer when this is preferred over the many sibling search/lookup tools.
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?
No annotations are provided, so the description carries the full burden. It clearly states that the tool calculates and returns results, implying a non-mutating analysis operation, but it does not explicitly say whether it is read-only, what side effects or prerequisites exist, or how errors are surfaced. This is adequate but not deeply transparent.
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 with no filler. The main action is front-loaded, and the return value is stated immediately. Every phrase contributes useful information.
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 tool with one simple required parameter and no output schema, the description is mostly complete: it names the input concept and the two key output components. It could go slightly further by mentioning where risk factor IDs come from or what the recommendations look like, but the current level is sufficient for straightforward invocation.
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%, and the schema already explains the 'riskFactorIds' array with an example. The description adds little beyond restating the concept of 'a list of risk factors,' so it does not meaningfully extend the parameter meaning beyond the schema.
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 uses a specific verb ('Calculate') and a clear resource ('risk assessment based on a list of risk factors'), and states the output ('combined relative risk and recommendations'). This clearly distinguishes it from retrieval-style siblings like get_user_risk_factors or get_condition_risk_factors because it performs a calculation rather than a lookup.
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 the tool should be used when you have risk factor IDs and want a combined risk assessment, but it does not explicitly state when to use it versus alternatives, nor does it mention any exclusions or complementary tools. Usage is inferable from the name and input schema, but not directly guided.
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 full burden of behavioral disclosure. It states that the tool retrieves health check status and includes instance and system checks, indicating a read-only operation. However, it does not describe return format, error behavior, or how the optional instanceId affects output 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 one concise sentence that leads with the action and resource, and immediately clarifies the two health check types. No filler or redundant content is present.
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, single-parameter read-only tool, the description covers core context: what is retrieved and the scope. It could additionally describe the expected response shape, but the absence is not critical given the tool's low complexity and the schema already documents the default behavior.
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 input schema documents the single optional parameter completely, including the default behavior of checking all MoleCare instances. With 100% schema description coverage, the description does not need to add parameter details, but it also does not provide additional semantic context beyond the schema.
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 uses a specific verb ('Get') and resource ('AWS health check status for EC2 instances') and further specifies the scope ('including instance and system status checks'). This clearly distinguishes it from siblings like get_ec2_instances, get_ec2_instance, and get_ec2_metrics.
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 usage for retrieving EC2 health check statuses, but it does not explicitly state when to use this tool over alternatives or provide exclusions. While the resource and purpose are clear, there is no direct guidance on selecting this tool versus get_ec2_metrics or get_ec2_instance.
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 behavioral burden. 'Get' and 'see' clearly signal a read-only operation, and 'current state' indicates what the agent will receive. It does not discuss authorization, rate limits, or pagination, but for a straightforward list tool the lack of mutation is reasonably clear.
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 tight, front-loaded sentences. The first gives the action and scope; the second gives a direct usage hint. There is no filler, repetition, or unnecessary qualifier.
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 description gives the output fields, the scope ('all'), and a concrete usage scenario, while the only optional parameter is fully handled by the input schema. It could better route the agent to siblings for single-instance or health-specific needs, but the gap is minor given the simplicity of this tool.
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 fully covers the only parameter with an enum and a documented default ('all'), so schema coverage is 100%. The description adds no parameter-level detail, but the baseline 3 is appropriate because the schema already does the heavy lifting.
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 specific verb 'get', a clear resource ('MoleCare EC2 instances'), and expected outputs (current state, IP addresses, instance types). The plural 'all' distinguishes it from the singular sibling get_ec2_instance, though it does not name that sibling explicitly.
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?
It gives a clear use case ('Use this to see all running servers') and the plural scope suggests it is for a broad list rather than a single instance. However, it does not explicitly say when not to use it or mention alternatives such as get_ec2_instance or get_ec2_health.
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 behavioral burden. It conveys a read-only retrieval from CloudWatch and correctly indicates the supported resource and time scope. It does not go into permissions, pagination, rate limits, or exact output shape, so it is adequate but not fully transparent.
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?
One clean sentence that front-loader the result type and resource before the time detail. There is no filler or unnecessary abstraction.
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, two-parameter read-only tool with 100% schema coverage, the description is nearly complete. An agent can select and invoke it with confidence. Minor gaps are the lack of explicit sibling differentiation and no mention of response structure, but these are not critical given the clarity of the metric names.
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 input schema already documents both parameters completely, including default and max for periodMinutes. The description contributes contextual grouping of metrics (CPU, network) and the notion of a time window, but does not add parameter-level detail beyond the schema.
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 uses a specific verb, resource, and boundary: 'Get CloudWatch metrics (CPU, network) for an EC2 instance over a specified time period.' It clearly identifies what is returned and distinguishes it from sibling tools like get_ec2_health, get_ec2_instances, and get_app_metrics, even though no alternatives are named.
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 intended use is implied by the wording: when CloudWatch CPU/network metrics for a single EC2 instance over a time window are needed. However, it does not explicitly state when to prefer this over related siblings such as get_ec2_health, get_app_etrics, or get_database_metrics.
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?
The verb 'List' reasonably implies a non-mutating read operation, and the description states what is returned. However, with no annotations provided, the description carries the full burden and does not disclose potential aspects like result size, ordering, or whether all views must be readable by the caller.
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 one short, front-loaded sentence that packs the resource, the scope ('all'), and the response contents without any filler. Every phrase earns its place.
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 zero-parameter list endpoint, the description is nearly complete: an agent knows exactly what will be listed and must only invoke the tool. Minor ambiguity remains around response shape, but the described output clauses make the expected result clear enough.
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?
The input schema has zero parameters and 100% schema description coverage, so there is no parameter behavior to document. The description focuses on what the call returns, which is appropriately sufficient here.
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 uses a specific verb ('List all') with a specific resource ('Feast feature views') and clarifies the contents of the response ('entities, features, and data sources'). It clearly distinguishes the tool as an aggregate listing tool rather than a detail/freshness tool.
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 gives clear evidence of what the tool does but offers no guidance on when to prefer it over sibling tools like get_feature_view_details or get_feature_freshness. No when-to-use or alternates are mentioned.
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?
No annotations are provided, so the description must carry the behavioral disclosure burden. It clearly describes what the tool returns (health, version, response time) and the verb 'get' implies a read operation, but it does not explicitly state whether the operation is read-only, whether it can fail, or whether it accesses live infrastructure.
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, focused sentence that immediately states the purpose and the specific data included. No filler or redundant content is present.
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 zero-parameter, low-complexity read-only status tool, the description provides the essential resources and the included information. It does not describe the return format or potential failure modes, but given the simple nature of the operation, this is not a major gap.
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?
The input schema has zero parameters, and the description adds no parameter details. This is unnecessary, so the baseline of 4 applies because there is nothing parameter-related to document.
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 states a specific action ('Get the current status'), a specific resource ('MoleCare mobile API backend'), and the information returned (health, version, response time). This clearly distinguishes it from sibling status tools that target other resources like the web app, database, or Kubernetes.
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?
There is no explicit guidance on when to use this tool versus alternatives such as get_web_app_status, get_app_status, or get_service_health. The resource name is somewhat disambiguating, but the description does not mention any exclusions or direct comparisons to other status tools.
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?
There are no annotations, so the description carries the full burden. The description states what the tool returns (success rate, average duration, failure count), and the verb 'Get' implies a non-mutating read operation. It does not disclose other behavioral traits such as authentication requirements, rate limits, or whether results are computed live or from cached data.
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, focused sentence that front-loads the tool's purpose and enumerates the key statistics it returns. There is no repetitive or extraneous content, so every word earns its place.
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?
This is a simple tool with one optional parameter, no required parameters, no output schema, and no nested objects. The description fully covers what the tool returns, and the schema handles the 'days' parameter. The only minor gap is the lack of explicit guidance about when to use it relative to raw-run siblings.
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 describes the only parameter 'days' with a default value, and schema description coverage is 100%. The description adds no additional meaning to the parameter, but because the schema already fully documents it, a baseline score of 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 identifies a specific verb ('Get'), a specific resource ('CI/CD pipelines'), and the exact output ('summary statistics including success rate, average duration, and failure count'). This clearly distinguishes it from sibling tools like get_pipeline_runs, which would provide raw run details rather than a summary.
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 term 'summary statistics' implies that this tool is for aggregate insight rather than detailed run-level data, so usage is implied. However, the description does not explicitly state when to use this tool over alternatives like get_pipeline_runs or get_deployments, nor does it list any exclusions.
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?
No annotations are provided, so the description carries the behavioral burden. The 'Get' and 'check' wording reasonably imply a read-only, non-destructive operation, and 'comprehensive health status' communicates scope. However, it does not say what the response looks like or what happens when services are unhealthy.
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?
Two sentence: a plain scope statement then a direct use. No extraneous words, usable for instant scanning, and the actionable guidance is front-loaded.
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?
With no params and no output schema, the description explains ha and when to call, but leaves important return semantics unspecified (what the health result contains and how availability is signalled). It's adequate for a simple health check, but not richly 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?
anceParameter count is 0 and schema_description_coverage is 100%, so the schema is complete and parameter semantics are triv. The description adds no parameter details needed because no parameters exist.
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?
States a specific verb (get), resource (health status of all MCP backend services), and scope (comprehensive,all). It does not explicitly distinguish itself from sibling tools like get_service_health or get_all_apps_status, so the name alone leaves some ambiguity, but the description is still clear.
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 to use this tool before making other calls to check availability, which gives a clear temporal/orchestration context. It doesn't explain when we'd use a sibling health/status tool instead, but the core use case is clear.
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 present, the description must carry the burden for behavioral context. The word 'search' and the phrase 'educational content' imply a read-only, informative operation, and the description adds topical scope. However, it does not mention what the tool returns, whether results are ranked, or any limitations, so it is only partially transparent.
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?
Two short, front-loaded sentences cover action, topic, and intended use with no filler. Every sentence adds value, making it a compact and effective description.
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 one-parameter search tool with no output schema, the description gives the agent sufficient information to know what to query and why to ask. The main missing piece is clarification of how this differs from sibling search/lookup tools, but the core invocation need is met.
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 100%: the single required 'query' parameter is already described with examples like 'asymmetry', 'melanoma', and 'sunscreen'. The tool description contributes little beyond reinforcing that the query is for skin-health education.
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 uses a distinct verb and resource ('Search the medical knowledge base') and specifies the topic ('skin health information', 'ABCDE criteria', 'prevention tips'). This clearly states the tool's core purpose, though it does not explicitly reference the equally similar sibling name like 'search_medical_concepts'.
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 says 'Use this to provide accurate educational content', which gives a clear situational context for invoking the tool. It does not name alternatives or include when-not-to-use guidance, so it stops short of a full 5.
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 the burden of behavioral disclosure. It does reveal the output's key fields (build status, duration, triggered by) and implies read-only via the word 'Get'. However, it omits any mention of expected ordering, pagination behavior, or whether it relies on a configured repository context, leaving a significant portion of the operational behavior under-described.
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?
Two short, focused sentences with the operative verb and resource front-loaded. Every word contributes: the first declares what is retrieved, the second states the critical output fields. No generic marketing fluff or repetition of schema parameters. Exceptional structural efficiency.
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?
Given the low complexity, the schema covers the known parameters, and the description specifies return fields, the missing text is modest but still annoying. The description does not state the default scope of 'recent' (e.g., default sort order, repository context, or whether it hits GitHub Actions directly). A short note such as 'sorted by newest first' or 'for the configured repo' would complete the picture. As is, it's adequate but has clear gaps for an agent that must traverse the whole state.
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 input schema already documents both parameters ('limit' and 'workflow') with 100% coverage. The description only states the general fields that will be returned, and provides no additional meaning about how workflow filtering or the limit parameter behaves. The schema handles the grounding parameter semantics, so a 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 identifies the verb ('Get'), the resource ('CI/CD pipeline runs'), and the source ('GitHub Actions'). It also specifies the key fields exposed (build status, duration, triggered by), which distinguishes it from siblings like get_mlflow_runs or get_pipeline_summary, removing any 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 Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description makes the context of use clear: an agent should call this when the intent is to retrieve recent CI/CD pipeline runs from GitHub Actions. While it does not explicitly enumerate alternatives or exclusions, the phrase 'recent CI/CD pipeline runs from GitHub Actions' is specific enough. It lacks a 'use get_pipeline_summary instead when...' note, but the context is not vague.
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?
There are no annotations, so the description carries the burden of behavioral disclosure. It clearly states the tool performs a health-endpoint request, implying a read-only diagnostic operation, and it specifies the scope. Still, the description does not disclose the response shape or potential error behavior, but this is a simple health check and enough context is present for safe use.
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?
A single, focused sentence identifies the target, the environment, and the method. No extraneous words, and all important details are 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?
For a tool with a single required parameter, no annotations, and no output schema, the description is mostly adequate: it clarifies what the tool does and what the input means. The only real gap is that the agent isn't told what the return value looks like, but 'responding' and 'health' are enough for the common use case.
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 description already covers instanceId at 100% clarity, and the tool description repeats this context without adding more meaning. Since the schema carries the parameter-meaning weight, the baseline of 3 applies.
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 ('Check') and resource ('molecare-server application') along with the mechanism ('hitting its health endpoint') and the location ('on an EC2 instance'). This is specific enough to distinguish it from broader EC2 health checks and service status tools.
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 when to use it — whenever an agent needs to verify that the molecare-server application is responsive on a specific EC2 instance — but does not explicitly mention alternatives or exclusions. Given the many health/status sibling tools, a note about not being equivalent to get_ec2_health or get_service_health would make it clearer.
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?
No annotations are provided, so the description carries the burden. It discloses that the core behavior is identifying differences between the two photos, which is meaningful, but it doesn't clarify the form of the result, whether the operation is read-only beyond its name, or what kind of image constraints apply.
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?
Two sentences, no repeated or irrelevant words. The primary action is front-loaded and the usage trigger follows naturally.
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 simple three-required-parameter tool is fully invocable with the schema plus description. The main gap: no output schema exists and the description doesn't formally state the result object, though the intended response kind (identified changes) is understandable.
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%, so all three parameters are already clearly described without any extra input documentation. The tool description adds little beyond image-related context, so the schema does the important lifting.
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 names a specific verb and action on a specific resource: compare two mole images to identify changes. The mole-image resource also clearly distinguishes it from the only similar sibling, compare_model_runs, so an agent can select the correct tool by description alone.
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?
It says explicitly Use this when a user asks about changes between photos, which is a clear usage trigger. It does not provide when-not-to-use guidance or a named alternative, so it does not reach the eplicit exclusions level.
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?
No annotations are provided, so the description must carry the behavioral burden. It clearly indicates a read-only 'get' operation and discloses the data scope (ratings, reviews, live versions), but it does not mention whether this fetches live external data, potential failure modes, or other side effects. This is acceptable but not rich.
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, well-formed sentence that starts with the action and resource and immediately lists the key result fields. Every word contributes to the meaning; there is no redundancy or filler.
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 the tool has no parameters and no output schema, the description is almost fully sufficient. It lids what the tool returns and which platforms it covers. A slightly more explicit statement about what 'status' means might improve it, but the main use case is covered adequately.
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?
The tool has zero params, so the parameter semantics are inherently not an issue. The description adds no parameter details, but none are needed; the baseline score of 4 for zero-parameter tools applies.
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 specific verb 'Get' and the resource: iOS App Store and Android Play Store status, with concrete outputs like ratings, reviews, and current live versions. This differentiates the tool from siblings such as get_app_status or get_all_apps_status, which are broader or focused on general app health.
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 the tool is for checking store-specific listing data rather than general app or infrastructure status. However, it does not explicitly mention when to use this tool over alternatives like get_app_status or get_app_versions, and there are many status-related sibling tools.
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?
There are no annotations, so the description carries the burden. It communicates the global scope, mentioning all MoleCare applications and both production and staging, which is useful behavioral context. However, it does not disclose return format, whether the call queries live deployment state, or whether any special auth is needed, leaving useful behavior partially implicit.
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, focused sentence with no filler. The resource and scope are front-loaded, and it wastes no tokens restating the tool name or schema.
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 zero-parameter, no-output-schema tool, the description provides adequate selection context: it says exactly what data is returned and for which environments. It is not fully complete because it does not describe the shape of deployment version results, but this is a minor gap for such a simple operation.
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?
The input schema is empty, so there are zero parameters to document. The description's mention of 'all MoleCare applications' and the environment list is consistent with a parameter-free API and adds useful scope detail.
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 (get), the exact resource (deployed versions of all MoleCare applications), and the scope (production, staging). This is specific enough to distinguish it from sibling tools like get_app_status, get_releases, or get_app_metrics, which concern different or broader data.
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 the appropriate context: use this when you need deployed version information for applications across environments. However, it does not explicitly state when to prefer this over closely related alternatives such as get_releases or get_deployments, and it gives no exclusions.
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?
No annotations are provided, so the description carries the full behavioral burden. It is transparent about what is returned (active, idle, and waiting connections) but does not disclose failure modes, whether this is strictly read-only, or how 'all database clients' is determined. This is adequate but not rich.
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 that efficiently conveys the action, scope, and key output fields. There is no redundant information or filler.
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 zero-parameter status read tool without an output schema, the description provides the essential context: what is being fetched, for which scope, and what connection states are reported. It could name the response format or note error behavior, but it is adequate for straightforward invocation.
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?
The tool has zero parameters, so there is no parameter semantics needed. The baseline of 4 applies, and the description does not need to compensate for any parameter documentation gaps.
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 states a specific verb ('Get'), a clear resource ('connection pools'), the scope ('all database clients'), and the data returned (active, idle, and waiting connections). This distinguishes it from generic siblings like get_database_status or get_database_metrics.
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 conveys that this is the tool to use for connection-pool-level status across all database clients, which provides clear context. However, it does not explicitly mention when to prefer this over related database/service status tools or any conditions for choosing an alternative.
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 present, so the description alone must carry the burden. 'Get' strongly implies a read-only operation, and the attribute of the resource as a specific EC2 instance sets a clear boundary. It doesn't detail error behavior or the exact returned fields, but this is minor for such a straightforward retrieval tool.
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?
A single sentence, front-loaded with the action and containing zero filler words. Every part of it earns its place.
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 tool with one required parameter and no output schema, the description is largely complete: the agent knows the exact input and the general outcome. It doesn't explicitly state that the operation is read-only or list the return fields, but neither gap severely undermines an agent's ability to invoke it 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 already documents the single parameter with a clear description and an example, giving 100% parameter coverage. The description repeats that the instance ID is the lookup key but adds no new semantics beyond the schema. Baseline 3 is appropriate.
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 identifies a specific verb ('Get'), a concrete resource ('detailed information about a specific EC2 instance'), and the selection criterion (instance ID). It does not explicitly name a sibling like get_ec2_instances, but the word 'specific' against the plural sibling still supplies a strong distinction.
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 'by its instance ID' makes the usage context clear: use this when you already have a known instance ID and need details for that one instance. It does not explicitly list alternatives or when-not-to-use, but for a one-parameter read tool this is adequate.
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?
No annotations are provided, so the description carries the full burden. The verb 'Get' and 'showing last materialization time' imply a non-mutating, read-only operation, and the output fields are stated. But the description does not disclose possible failures, time/format caveats, or whether the data may be cached or delayed.
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 one compact sentence with no filler. The action, scope, and the two key output fields are all front-loaded and each part earns its place.
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 zero-parameter read-only getter, the description is mostly complete: it names what is retrieved and what is returned. It leaves unstated exactly how staleness is represented and what happens when no feature views exist, but these are minor in light of the tool's simplicity.
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?
There are zero parameters, and the schema coverage is 100%, so no parameter documentation is needed. The description still adds semantic value by defining the scope ('all feature views') and the relevant output dimensions ('materialization time and staleness').
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 uses a specific verb and resource: it says it 'get[s] freshness status of all feature views' and explicitly names the output ('last materialization time and staleness'). This clearly separates it from sibling tools like get_feature_views, get_feature_view_details, and get_feature_store_stats.
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 intended use is implied: call this when you need freshness/staleness across all feature views. However, it does not explicitly state when to avoid it or name alternatives such as get_feature_view_details for a single feature view, so the usage guidance is not fully developed.
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 must carry behavioral disclosure. It does indicate this is a listing operation for educational purposes, suggesting a read-only, non-clinical context, but it does not describe return format, ordering, or whether the list is exhaustive beyond 'all.' For a zero-argument, non-destructive list tool, this is acceptable but not deeply transparent.
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 composed of two short sentences with no filler. It front-loading the core function and adds one purpose statement, which is 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?
For a zero-parameter list operation, the description sufficiently captures the scope, nature, and intended context. It could improve by referencing relevant sibling tools or mentioning whether output is a user-visible list or plain ontology terms, but the minimal complexity justifies a high but not perfect score.
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?
The tool takes no parameters, and the schema is already 100% covered with no properties. The baseline for zero-parameter tools is 4, and the description introduces no unnecessary parameter details. Guidance is unnecessary here.
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 states a specific action and resource: 'Get a list of all malignant skin conditions in the ontology.' It clearly differentiates itself from sibling tools that search, compare, or assess risk by emphasizing an exhaustive list of conditions. The educational caveat clarifies application context without muddying the purpose.
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 'Use for educational purposes about skin cancers' gives a clean context and intended use. However, it does not explicitly say when not to use it or point to alternatives such as lookup_medical_concept or search_medical_concepts for more specific queries. The usage guidance is present but rather implicit.
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 behavioral burden. 'Get' strongly implies a read-only lookup, and the description also disloses what the response covers: stage, metrics, and run info. It does not discuss error or auth behavior, but this is acceptable for a low-risk retrieval.
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?
A single concise sentence that front-loads the action and resource. Every phrase earns its place, listing the retured detail categories without filler or repetition.
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 two-parameter retrieval, the description is sufficient to orient an agent and the schema covers the required arguments. Since there is no output schema, the mention of stage, metrics, and run info provides a useful minimal indicator of the response contents.
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 input schema already provides 100% coverage for modelName and version, so the description needs to add little for parameters. It does not add syntax hints or clarify the relationship between the two parameters beyond the schema definitions.
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?
States a precise verb and resource: 'Get details of a specific model version', and enumerates the key content attributes (stage, metrics, and run info). It clearly distinguishes this from list-style sibling tools like get_registered_models and get_mlflow_runs.
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 should be used when retrieving a single model version's details, but it does not explicitly state when not to use it or name alternatives for listing versions or comparing runs. The guidance is implied rather than explicit.
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?
No annotations are present, so the description carries the transparency burden. The verb 'Get' implies a read operation, but the description does not explicitly state that it has no side effects, nor does it disclose output shape, aggregate size, potential slowness, or failure behavior across all services.
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. Every part adds meaning, especially the list of covered service categories, making it easy to parse quickly.
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 zero-parameter read-only status check, the description conveys enough for an agent to select the tool correctly. It does not define response formatting, but with no output schema and a simple goal, that is not a significant completeness gap.
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?
The tool accepts zero parameters, so the schema already requires no clarification. The description still adds value by explaining the scope of the request, matching the baseline for a no-parameter tool.
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 names the specific action ('Get') and the resource ('health status of all backend services') and enumerates covered categories (databases, cache, storage, auth, external dependencies). This clearly separates it from single-service sibling tools like get_database_status or get_web_app_status.
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 'all backend services' implies this is the broad health-overview tool, but the description never names sibling alternatives or says when to prefer get_system_health, get_database_status, or get_ec2_health instead. The routing guidance is implied rather than explicit.
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?
No annotations are provided, so the description carries the full burden. It implies read-only behavior through 'Get' and lists the principal output fields (risk levels and analysis dates), but it does not mention possible limitations, pagination, authentication needs, or behavior when a user has no moles.
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?
Two short sentences, no fluff, with the core action front-loaded and the usage context in the second sentence. Every sentence is relevant and readable.
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 with only one parameter and no output schema. The description explains both what it returns and how to use the result in context, which is reasonably complete. Slightly more detail about the structure or granularity of the returned risk fields would make it fully complete.
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 already fully documents userId with a description, and the schema coverage is 100%. The description only adds the broad context 'for a user', which does not materially augment what the schema already provides.
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 names the verb 'Get', the resource 'all moles', and the relevant data ('current risk levels and last analysis dates'), making the tool's purpose concrete. It is easily distinguishable from sibling tools like get_mole_analysis or get_mole_changes because it explicitly says all moles for a user.
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 states when to use the tool: 'Use this to understand a user's overall skin health status.' This provides useful context for selecting the tool, though it does not explicitly name alternatives or 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.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description itself must convey behavioral expectations. 'List' clearly indicates a read operation and mentions the returned fields, but it does not disclose potential details such as pagination, sorting, authentication needs, or failure behavior. This is adequate but not rich.
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 one focused sentence, immediately front-loaded with the verb and resource, and every phrase adds useful information. No filler or repetition.
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 zero-parameter listing tool, the description is complete: it states what is listed, the scope ('all'), and the included output details. There is no output schema, but the description still tells an agent what to expect from the response at a useful level.
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?
The tool takes zero parameters, so there are no param semantics to clarify. The description has nothing to add beyond the already complete 100% schema coverage, so the zero-parameter baseline of 4 applies.
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 names an action ('List'), a specific resource ('all MLflow experiments'), and the included output fields ('status and last update time'). This distinguishes it from nearby siblings like get_mlflow_runs, which operate on runs rather than experiments.
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 usage context is implied: use this tool when you need a list of MLflow experiments. However, it does not explicitly state when not to use it or name an alternative such as get_mlflow_runs, so guidance is only implicit.
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 the burden of explaining behavior. It communicates a read-only, batch operation, but it does not clarify what the status payload contains, whether partial failures can occur, or what the response shape looks like.
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 with no extraneous content. It clearly communicates the purpose and scope in a front-loaded, efficient way.
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 zero-parameter read-only tool, the description covers the most important context: what it reports on and that it is a single aggregated call. A brief note on the output/status format would make it fully complete, but nothing critical is missing.
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?
The tool has zero parameters and the schema is already complete, so no parameter documentation is needed. The description adds no parameter semantics, which is acceptable for an empty input schema.
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 specific verb ('Get'), the resource ('status of all MoleCare applications'), and the exact scope (web, mobile API, iOS app, Android app). The phrase 'in one call' also helps distinguish it from individual application status tools.
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 gives clear context: it is the one-call option for checking all application statuses. It does not explicitly name alternatives like get_web_app_status or say when an agent should prefer the individual status tools, but the intent is reasonably clear.
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 itself conveys the core behavioral expectation: retrieval of backup history including automated/manual backups and their status and sizes. It does not explicitly mention ordering, pagination, or the effect of the limit parameter, but the overall non-destructive retrieval behavior is clearly implied by the word 'Get' and the factual output description.
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, informative sentence. It immediately identifies the action, the resource, the inclusion of automated and manual backups, and the output criteria (status and sizes) without redundancy or filler.
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 read-only tool with one optional parameter and no output schema, the description gives a complete overview of what it retrieves and what output details are exposed (status and sizes). It doesn't list the exact response structure or timestamp fields, but the description is sufficient to understand the tool's result.
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 100% because the only parameter, 'limit', has a full description including its default value. The tool description adds no additional parameter-related context, but the baseline of 3 is appropriate when the schema already fully documents the parameter.
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 specifies a distinct verb ('Get') and resource ('database backup history') and additionally scopes the operation to automated and manual backups with statuses and sizes. This unambiguously separates it from related sibling tools like database status or metrics tools.
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 specific resource ('backup history') provides clear context for when this tool should be used, and it is distinct from all 50 sibling tools, so no alternative is explicitly named. It stops short of explicitly stating when not to use it or naming a specific alternative, but the resource scoping is clear enough.
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 burden of expressing behavior. It clearly presents this as a read-only get operation that retrieves stored analytical results, and adds the useful detail that the results include ABCDE scores. It does not warn about missing moles or auth constraints, but nothing in the description suggests destructive or side-effectful 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?
Two short sentences, front-loaded with the action and resource, followed by the concrete score list and an intended usage. Every part contributes useful information and there is no filler.
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 one fully described parameter, and no output schema. The description tells the agent what data will be returned (ABCDE scores) and what to use it for (explaining results to users). It is nearly complete, though it does not enumerate any additional return fields beyond ABCDE scores.
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 input schema has a single parameter, moleId, with a 100% schema description coverage: 'The mole's unique identifier.' The description reaffirms the parameter is a specific mole but adds little beyond the schema. Baseline 3 applies because the schema already covers the necessary semantics.
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 uses a specific verb ('Get') with a clear resource ('detailed ML analysis results for a specific mole') and spells out the ABCDE score components. This distinguishes it from sibling tools like get_user_moles, get_mole_changes, and compare_moles without needing to open the input schema.
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 provides a clear intended context: 'Use this to explain analysis results to users.' It does not explicitly contrast this tool with siblings or state when not to use it, but the stated purpose is enough for an agent to select it for user-facing explanations of mole analysis.
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 provided, the description carries the full burden of behavioral disclosure. It clearly conveys that this is a read-only retrieval operation returning historical change data, including size/color changes and trend analysis. It does not mention response ordering or error conditions, but for a simple history getter this is adequate.
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?
Two short sentences with no filler. The main action and resource are front-loaded, followed by purpose, and the usage guidance is folded cleanly into the second sentence.
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 has one required param, no output schema, and a simple read operation. The description sufficiently explains what the user gets, including key change categories and the intended use case. It could mention ordering or available time ranges, but these are not essential for accurate invocation.
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 already covers the single parameter moleId with a clear description ('The mole's unique identifier'), so the description does not need to add more. Baseline 3 applies; the description adds no new parameter-level detail, but none is required.
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 names a specific verb ('Get'), a clear resource ('change history for a mole'), and lists concrete contents ('size changes, color changes, and trend analysis'). It clearly differs from siblings like get_mole_analysis or get_condition_progression, so an agent can distinguish it without opening schemas.
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 gives an explicit usage context: 'Use this to explain how a mole has evolved.' It clearly indicates when this tool is appropriate, but it does not explicitly mention when not to use it or compare against alternatives such as get_mole_analysis.
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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