Roblox Super MCP

Server Quality Checklist

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Latest release: v3.0.1
Disambiguation4/5
Most tools have distinct purposes, but find_system and get_related_code both rank files by match evidence across similar dimensions, which could cause confusion. The audit_* tools are differentiated by their target concern, so overall boundaries are mostly clear.
Naming Consistency5/5
All tool names follow a consistent snake_case verb_noun pattern (e.g., analyze_project, get_project_snapshot, audit_lifecycle), making the set highly predictable and easy to navigate. There are no mixed conventions or vague names.
Tool Count3/5
At 23 tools, the server is in the borderline heavy range, and the presence of near-duplicate tools like find_system and get_related_code suggests it could be slightly consolidated. The breadth of Roblox development is large, but the count feels marginally bloated rather than well-scoped.
Completeness4/5
The tool surface covers analysis, auditing, modification, verification, and rollback comprehensively for Roblox code. However, it lacks explicit instance deletion or standalone script creation (only via create_architecture), which are minor gaps agents could work around.
Average 3.6/5 across 23 of 23 tools scored. Lowest: 2.9/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 5 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
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```
{
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}
```
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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 provided, the description carries the full burden of behavioral disclosure. It mentions 'bounded' and 'paginated' output but does not reveal side effects, permissions, rate limits, or other behavioral traits. The lack of detail about the refresh parameter's side effects and cache behavior is a gap.
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, information-dense sentence with no fluff. It front-loads the core action ('Returns a bounded project overview') and lists components efficiently. Every phrase adds value, making it highly concise.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has 7 parameters, no output schema, and no annotations, the description is too brief to be fully contextual. It lists return components but does not explain what 'bounded' means, how pagination works, or how to interpret the components. The tool's complexity demands more detail.
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 baseline is 3. The description adds no extra semantic meaning beyond the schema; it does not explain parameter interactions or provide examples that would help select values.
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 tool returns a 'bounded project overview' with specific components (place identity, counts, remotes, modules, etc.). This distinguishes it from sibling tools like analyze_project or inspect_instance, though it does not explicitly differentiate by naming them.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives. The description implies an overview use case but does not mention scenarios where a sibling tool would be more appropriate, nor does it suggest exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must disclose side effects and operational traits. It mentions 'field-level match evidence, relationships, services, and remote participation,' but does not clarify whether it performs network calls, whether it is read-only, or what 'remote participation' means.
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, front-loaded sentence with no wasted words. It conveys the core action and key features, though it is somewhat terse.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With no output schema and no annotations, the description fails to explain the return value format or the meaning of 'remote participation.' The short description is insufficient for an agent to fully understand expected outputs and side effects.
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 covers all four parameters with descriptions, so the schema does the heavy lifting. The description adds minimal extra value beyond clarifying that 'task' is the ranking query, which is already implied by 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 uses a specific verb ('ranks') and resource ('indexed files') for a task, which distinguishes it from search/analysis tools. However, it does not explicitly contrast with sibling tools, so it falls short of a 5.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives like search_source or impact_analysis. The description only states what it does, leaving the agent to infer usage context.
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 carries the full burden. It states what the audit covers and that it is 'line-aware,' but it does not disclose whether the tool is read-only, whether it modifies the script, what the output looks like, or any side effects. For an audit tool, the lack of explicit read-only confirmation and return format is a significant transparency gap.
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, efficient sentence. It front-loads the purpose ('Performs a line-aware Luau audit') and then lists the audit categories in a compact enumeration. Every word contributes to conveying scope without unnecessary filler.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
This is a moderately complex audit tool with no output schema and no annotations. The description explains what aspects are audited but fails to describe the output format, how results are returned, or how to interpret findings. An agent would not know what to expect after invoking the tool, making the description incomplete for this complexity level.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema covers both parameters (script_name and script_content) with descriptions, achieving 100% schema description coverage. The tool description does not add any additional parameter-level meaning beyond the schema, so the baseline score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool performs a 'line-aware Luau audit' and enumerates the specific audit dimensions (strict typing, lifecycle, remote validation, etc.). It is a specific verb+resource combination, but it does not explicitly differentiate itself from sibling audit tools like audit_lifecycle or validate_remote_contract, which cover overlapping areas.
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 the more specialized audit tools listed among siblings. The description implies a comprehensive audit use case but does not state exclusions, alternatives, or prerequisites. An agent is left to infer when a broad audit is needed versus a targeted one.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden of behavioral disclosure. It mentions analytical categories (cycles, unresolved requirements, ambiguity) but does not state whether the operation is read-only, potential performance impact, authentication needs, or how results are returned.
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 core functionality without any 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 tool with no output schema, the description enumerates the key result categories but does not explain the return format or provide any usage context. It is adequate but not complete for a complex graph-building 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?
Both parameters have complete schema descriptions (100% coverage), so the baseline is 3. The description adds no additional parameter meaning beyond what the schema already provides.
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 tool returns dependency and dependent graphs with direct/transitive scope and includes cycles, unresolved requirements, ambiguity, and service usage. It is specific but does not explicitly differentiate from similar analysis tools like impact_analysis or trace_data_flow.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives. The description only states what it returns, leaving the agent to infer utility from the name and content.
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, but the verb 'Lists' strongly implies a read-only operation. The description discloses the categories of returned data, offering some transparency, yet it does not mention pagination, output format, or any side effects beyond the listing.
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, efficient sentence that front-loads the verb and lists key items. It avoids unnecessary words, though the long list of terms could benefit from slight reordering for readability.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With no output schema, the description must explain what the tool returns and how parameters affect results. It lists categories like 'missing peers' and 'evidence coverage' but does not define them or clarify the impact of filters like 'include_unused', leaving a significant context gap.
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 describes all four parameters (limit, offset, remote_name, include_unused) with explicit meanings, achieving 100% description coverage. The description adds no additional parameter context, so the baseline score 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 uses the specific verb 'Lists' and identifies the resource as the remote registry, enumerating key data categories. It is clear enough but does not explicitly differentiate from sibling tools like 'get_project_snapshot' or 'find_system'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives. The description only states what it lists, with no mention of prerequisites, use cases, or exclusions, leaving the agent to infer applicability.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It discloses atomicity, exact-text matching preconditions, dry-run capability, idempotency, verification, and rollback—far more than typical mutation tools. While it doesn't detail failure semantics or permissions, the disclosed traits give an agent a strong sense of the tool's safety profile and side effects.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence with dense, relevant information. Every word contributes: atomicity, exact-text edits, preconditions, and feature list. It is front-loaded with the core action and avoids redundant or filler text.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
This is a complex tool (6 parameters, nested objects, no output schema, no annotations), but the description only provides a high-level summary. It fails to mention critical operational details such as the requirement for an idempotency_key during live mutation, how dry_run interacts with other parameters, or what verification/rollback entail. An agent would need the full schema and inference to use this correctly, making the description incomplete for a tool of this complexity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 67%, so the schema does most of the work. The description adds high-level context by connecting source-hash, match-count, and dry-run to the respective parameters (expected_source_hash, expected_count, dry_run), which helps interpretation. However, it does not clarify the distinction between `path`+`edits` and the `changes` array, nor the behavior of `replace_all` vs `expected_count`. This is acceptable but not exceptional.
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 tool applies exact-text script edits with specific preconditions (source-hash, match-count) and features (dry-run, idempotency, verification, rollback). It uses a specific verb and resource, making the purpose unambiguous. However, it does not explicitly differentiate from sibling tools like generate_change_plan or verify_change, so it misses the top score.
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 use this tool versus alternatives such as generate_change_plan, verify_change, or rollback_change. The description implies usage through its feature list but does not state scenarios, prerequisites, or exclusions. This is a significant gap for a tool with many siblings.
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 does not state whether the audit is read-only, what side effects might occur, what the output format is, or any performance/rate-limit implications. The description is too terse to convey the tool's behavior beyond its stated purpose.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single concise sentence that front-loads the verb and immediately communicates scope. There is no redundancy or filler. Every word contributes to explaining the tool's focus.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool is an audit tool with no output schema, so the description should explain what the audit returns or produces. It does not. It also lacks guidance on how to use the parameters (e.g., what 'side' affects) or any disambiguation from sibling audit tools. This leaves significant gaps for an agent 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 input schema covers all four parameters with clear descriptions (side enum, query, offset, max_scripts), achieving 100% schema_description_coverage. The description adds no parameter-specific details, but since the schema fully documents them, the baseline score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'Audits' and specifies the exact resources (ProfileStore/ProfileService/DataStore, receipt flows) and the safety properties checked (session, pcall, persistence, yield, grant-order). This level of specificity distinguishes it from sibling audit tools like audit_lifecycle or audit_performance.
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 integrity checks on specific systems and flows, but does not explicitly state when to choose this tool over alternatives or provide exclusions. No mention of prerequisites or scenarios. This is implied rather than explicit guidance, so it only meets the baseline.
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 disclose behavioral traits. It only lists what the tool detects without stating whether it is read-only, what output it produces, or any limitations. This leaves significant behavioral ambiguity for an agent.
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 efficiently captures the tool's purpose without filler. It is front-loaded with the verb 'Finds' and enumerates hazard types in a compact list.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the absence of an output schema and annotations, the description is too sparse. It does not describe the return format, whether the tool is a safe read-only analysis, or how to interpret the results, leaving an agent uncertain about the tool's 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 already provides 100% documentation for all four parameters, so the baseline is 3. The description adds no parameter semantics beyond what the schema states, relying on existing 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?
The description uses the specific verb 'Finds' and clearly identifies the resource (race conditions) with four concrete hazard categories. This distinguishes it from sibling audit tools like audit_performance and audit_data_integrity which target different aspects of code quality.
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 does not explicitly state when to use this tool versus alternatives such as trace_data_flow or sanity_check_script. Usage is only implied by the tool's name and the list of hazard types; no exclusions or alternative recommendations 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?
With no annotations provided, the description must fully disclose behavioral traits. It does enumerate what is audited, but omits critical details: whether the audit is read-only or modifies anything, potential performance impact on live hot paths, or what the output/report format looks like. This leaves significant ambiguity for an agent deciding whether to invoke the 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, front-loaded sentence that directly states the action and scope. Every phrase carries meaning, listing specific audit areas without redundancy or filler. It is optimally concise for its complexity level.
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?
All parameters are documented and the purpose is clear, but the absence of an output schema and any mention of return values means agents don't know what to expect from the tool's response. For an audit tool that likely produces a report, this is a noticeable gap, though the simplicity of the input schema partially compensates.
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 each parameter (side, query, offset, max_scripts) having a clear description. The tool description itself adds no extra parameter meaning, so the baseline of 3 applies. It neither enhances nor undermines the schema's clarity.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's function with a specific verb ('Audits') and a defined resource ('live hot paths'), and lists concrete audit targets like per-frame allocation, cloning, and raycast pressure. This distinguishes it from sibling audit tools (e.g., audit_lifecycle, audit_data_integrity) which focus on different concerns.
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 performance hot-path analysis but offers no explicit guidance on when to choose this tool over alternatives like audit_lifecycle or detect_race_conditions. There are no stated exclusions or use-case comparisons, leaving the agent to infer applicability 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, the description carries the burden of behavioral disclosure. It does reveal that the tool returns certain data and that script source is bounded (via optional source_limit), but it does not explicitly state whether the operation is read-only, how errors are handled, or any side effects. This is partial disclosure but lacks a full safety profile.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence, front-loaded with the action and outcome. It includes no filler or redundant information, making it highly concise and well-structured.
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 tool's complexity (6 parameters, no output schema), the description gives a fair high-level overview of inputs and outputs but omits details like return format, error behavior, and relationship to sibling tools. It is adequate for a basic understanding but not fully complete for a tool with this many parameters and no output schema.
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 of the 6 parameters already has a description in the input schema. The tool description adds minimal semantic value beyond naming the returned data; for example, it mentions 'bounded script source' which loosely maps to include_source and source_limit but does not clarify usage details. This meets the baseline for full schema coverage.
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 ('batch-inspects') and resource ('exact Studio paths'), and explicitly lists the returned data (properties, attributes, tags, children, optional bounded script source). This clearly distinguishes it from sibling tools like analyze_project or search_source, which focus on different operations.
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. It does not mention scenarios such as 'use this when you know exact paths' or direct users to other tools for fuzzy searching or analysis. This leaves usage context 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 full burden of behavioral disclosure. It does reveal an important behavioral trait: it 'clearly separates observation from inferred flow,' indicating the output distinguishes empirical data from inference. However, it does not mention side effects, permissions, failure modes, return structure, or caveats, so transparency is only partial.
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-structured sentence. It front-loads the core verb and target ('Traces observed remote and persistence participants') and adds a meaningful nuance in the subordinate clause ('clearly separating observation from inferred flow'). Every word earns its place, with no fluff 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?
For a tool with three parameters and no output schema, the description gives a reasonable overview but leaves key invocation details unresolved. It does not explain what the trace output looks like, how 'limit' caps results, what 'include_related' changes, or what constitutes 'flow.' This is adequate but leaves clear gaps for an agent that must invoke the tool correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema already describes all three parameters with 100% coverage. The description aligns with 'variable_name' by saying 'for a state name' but adds no extra meaning for 'limit' or 'include_related.' Per the rubric, baseline 3 is appropriate given high schema coverage.
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 'Traces' and names the exact resource: 'observed remote and persistence participants for a state name.' The clause 'clearly separating observation from inferred flow' adds a distinctive functional scope, differentiating it from broader dependency/tracing sibling tools. It is neither tautological nor vague.
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 that the tool is used for tracing state-related data flow, but it provides no explicit when-to-use guidance, no comparison with alternatives like find_dependencies or impact_analysis, and no exclusions. In the presence of many siblings, this lack of context leaves the agent to guess when this tool is preferred.
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 disclosing behavior. It does list the types of checks performed (runtime validation, rate limiting, etc.), which conveys the scope, but it does not state whether the operation is read-only, how results are returned, or any side effects. 'Audits' implies non-destructive, but this 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 a single, well-structured sentence that front-loads the action and packs in six specific validation areas without unnecessary words. It is highly efficient.
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?
Contextual completeness is moderate: the description names the key validation areas but does not describe the output format, pagination behavior, or how the optional parameters influence the audit. However, the schema covers parameters, and the tool is relatively straightforward as a 'validate' operation, so it is minimally adequate but with 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 input schema already provides descriptions for all four parameters (limit, offset, max_scripts, remote_name), so schema coverage is 100%. The tool description adds no additional information about parameter semantics, so the baseline score 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 uses the specific verb 'audits' and clearly identifies the resource ('remote contracts') along with six concrete validation dimensions. This distinguishes it from sibling audit tools like 'audit_lifecycle' or 'audit_performance' by focusing on remote contract specifics.
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 use this tool versus siblings such as 'sanity_check_script' or 'audit_lifecycle'. The description states what it does but does not mention any prerequisites, exclusions, or alternative 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 present, so the description must carry the burden. It discloses the scope of the audit (what issues it checks) but does not explicitly state whether the operation is read-only, if it requires special permissions, or whether it has side effects. The term 'Audits' implies non-modifying behavior, but this is not made 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 a single, front-loaded sentence with a clear verb and resource. It lists specific issue categories without fluff, making every word informative and the structure highly concise.
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 covers the core purpose but omits information about expected outputs or behavior beyond the audit categories. Since there is no output schema and no annotations, the description could be more complete by mentioning the result format or any side effects, but it remains adequate for the tool's main intent.
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 descriptions already cover 100% of the parameters, providing clear explanations for side, query, offset, and max_scripts. The tool description adds no additional context about these parameters, so baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states a specific verb ('Audits') and resource ('live scripts'), enumerating precise audit categories (unowned connections, uncancellable threads, unbounded yields, stale references, missing cleanup). This distinguishes it from sibling audit tools like audit_performance and audit_data_integrity.
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 lifecycle-related audits but provides no explicit when-to-use guidance or mentions of alternatives. Sibling tools exist but are not referenced, so an agent must infer when this tool is appropriate.
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 does disclose idempotency and the transaction marker for rollback identity, which are valuable traits. However, it omits potential destructive effects of updates, permission requirements, and failure modes, so transparency is only partial.
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-structured sentence that front-loads the action ('Idempotently creates or updates') and then lists resources. It contains no fluff and efficiently communicates both purpose and a key behavioral trait, earning a perfect score.
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 complex tool with multiple entity types and four parameters, the description covers high-level purpose and one behavioral trait. It does not mention when to use dry_run vs. live mutation (though schema covers that) and lacks return-value context since no output schema is provided. The description is adequate but not fully complete for this complexity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 75% (dry_run, idempotency_key, create_missing_folders have descriptions). The description adds contextual meaning by enumerating the entity types that nodes likely represent, but it does not explain the nodes structure or how parameters relate. This aligns with the baseline 3 where schema does the heavy 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 states a specific action ('Idempotently creates or updates') with a comprehensive list of resources (folders, scripts, remotes, attributes, tags, scalar/typed properties), clearly distinguishing it from the read-oriented sibling tools. It also adds the unique behavioral detail of a transaction marker for rollback identity, making the purpose unmistakable.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies that this tool is for creating/updating architecture elements, but it does not explicitly state when to use it versus alternatives like apply_script_patch or rollback_change. There are no exclusions or alternative tool recommendations, so the usage guidance is implicit 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?
With no annotations, the description discloses a key behavioral trait: it does not authorize deletion. It also indicates results are confidence-scored. However, it does not mention permissions, rate limits, or return format details, leaving some transparency gaps.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, concise sentence that front-loads the main action. It contains no fluff and every word adds meaning.
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 gives the core behavior and safety guarantee, but lacks details about result structure (e.g., confidence score format) and any preconditions. Given no output schema and no annotations, a bit more context would be helpful, but it is minimally adequate.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 67%, and the description compensates for the undocumented 'kind' parameter by listing the four candidate types. Limit and offset are already described in the schema, so the description adds value by clarifying the enum 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 clearly states the tool's purpose: returning confidence-scored unused candidates across four specific types (module, function, remote, attribute). It uses a specific verb ('Returns') and resource, and the 'without authorizing deletion' clause distinguishes it from mutation 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 does not provide explicit guidance on when to use this tool versus alternatives. It implies use for dead code analysis but does not name alternative tools or exclusion criteria. Sibling tools like analyze_project and find_dependencies are not referenced.
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 of behavioral disclosure. It adds context by mentioning ranked results and match evidence, which gives insight into output characteristics. However, it does not explicitly state whether the operation is read-only, describe side effects, or clarify pagination behavior beyond what the schema already provides. This gap is notable but not severe for a discovery 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, concise sentence that front-loads the key purpose ("Ranked system discovery") and includes a useful status marker ("(RECOMMENDED)"). Every phrase adds value, and it avoids unnecessary details.
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 four parameters and no output schema, so the description needs to convey sufficient context. It provides a high-level summary of purpose and result characteristics (ranked, match evidence) but omits behavioral details such as explicit read-only guarantees or output structure. Given the moderate complexity, the description is adequate but has clear gaps for full autonomy.
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 per the baseline rule, a score of 3 is appropriate. The description's list of search domains (paths, functions, etc.) partially overlaps with the schema's descriptions for system_name and categories, but does not add substantial new meaning. It does not explain parameter syntax or constraints 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 clearly states the tool's function as "Ranked system discovery with match evidence," indicating a specific verb ('discovery') and resource ('systems'). It distinguishes from sibling tools by specifying the search scope across paths, functions, attributes, dependencies, services, and remotes, which is unique among the listed 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 usage for finding systems, with the "(RECOMMENDED)" prefix suggesting it is the preferred choice. However, it does not explicitly state when to use it over alternatives like search_source or find_dependencies, nor does it mention any exclusions or conditions. Usage guidance is thus implied but not 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 must carry the burden. It adds useful context like 'live' source and 'line-numbered snippets', but does not disclose whether the operation is read-only or any side effects. As a search tool, this is moderately transparent but not comprehensive.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, front-loaded sentence with no redundant words. It packs essential information about the tool's purpose and modes efficiently.
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?
While there is no output schema, the description mentions 'line-numbered snippets' which covers return format partially. The schema fills in parameter details, and the description provides enough context for a search tool. It could mention limitations like search scope, but is adequate overall.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is high (90%), so baseline is 3. The description adds meaningful semantics by explaining the mode types (literal text, Luau pattern, exact symbol frontier, method call) which directly maps to the 'mode' enum, and 'line-numbered snippets' relates to context_lines. This adds value 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 clearly states the tool's function: 'Searches live Luau source' with four specific modes (literal, pattern, symbol, method). It is specific and distinct from sibling analysis tools, 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 Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for searching source code, and the various modes give some context, but there are no explicit when-to-use vs alternatives or exclusions. Sibling tools are analysis-oriented, so the intent is somewhat clear, but not explicitly 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?
No annotations are provided, so the description must carry the behavioral disclosure burden. It implies a read-only analysis ('Calculates change risk') and gives a usage warning, but it does not explicitly state that it makes no changes, nor does it describe return format or potential side effects. It adds some behavioral context through the listed risk factors but lacks completeness.
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 immediately signals the tool's purpose with '(RECOMMENDED BEFORE EDITING)' followed by a concise definition. There is zero waste, and every phrase earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Despite having 100% schema coverage for parameters, the lack of an output schema means the description should explain what the tool returns (e.g., a risk score, a report, a list of affected elements). It does not, leaving a significant gap for agents that need to interpret results. The description also omits any details about output structure or how risk is quantified, making it incomplete for a complex risk-analysis 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?
Schema coverage is 100%, so the baseline is 3. The description adds contextual meaning by linking 'direct/transitive dependents' to the max_depth parameter and indicating that 'change' is the target of risk evaluation. This enriches but does not significantly exceed 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?
The description clearly states the tool calculates change risk and specifies the dimensions it considers (direct/transitive dependents, persistence, remotes, shared state, ambiguity). This distinguishes it from sibling tools like find_dependencies or generate_change_plan, which serve different purposes.
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 includes '(RECOMMENDED BEFORE EDITING)', providing clear context for when to use the tool. It does not explicitly exclude any scenarios or name alternatives, but the recommendation before editing is a strong trigger. This is clear context with no exclusions, meriting a 4.
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 explicitly states the operation is a 'read' (implying non-mutating) and lists the broad set of information retrieved, giving agents a clear expectation of what the tool accesses. It could mention limitations or side effects, but the verb and scope are clear enough for a read-only 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, front-loaded sentence that lists the tool's capabilities without redundancy. Every item in the list earns its place, and there is no filler or extraneous detail.
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 complex with 4 parameters and no output schema, but the description enumerates all the components of context it reads (source window, dependencies, dependents, remotes, functions, attributes, source hash). This provides a solid high-level understanding, though it does not specify output structure or parameter combinations, which would be helpful but not essential.
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?
All parameters have detailed descriptions in the schema (100% coverage), so the baseline is 3. The description mentions 'source window', which aligns with start_line and line_count, but it adds no additional parameter-specific meaning beyond 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?
The description uses the specific verb 'Reads' and identifies a distinct resource ('live source window') along with a comprehensive list of contextual elements (dependencies, dependents, remotes, functions, attributes, source hash). This clearly distinguishes it from sibling tools like search_source or get_related_code, which focus on narrower aspects.
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. It does not mention specific use cases, exclusions, or when another sibling tool would be more appropriate. It is purely a functional description without selection advice.
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 explicitly discloses the critical non-mutating behavior and indicates the output is evidence-backed, providing important safety and input context. However, it does not elaborate on other behaviors like read access patterns or the structure of the generated plan, so it is 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?
The description is a single sentence that is front-loaded with the core verb and resource, avoiding any filler or repetition. It is concise and easy to parse.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simple nature and full schema coverage, the description adequately communicates the plan-generating function. The lack of an output schema and absence of details about the plan's format are minor gaps, but the core behavior is sufficiently clear for an agent to select and invoke the 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 input schema fully describes all four parameters, so the description does not need to repeat them. The mention of 'a requested feature or change' maps to the 'request' parameter but adds no new semantic detail beyond 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?
The description uses the specific verb 'Produces' and identifies a distinct resource ('implementation and verification plan'), clearly distinguishing this planning tool from mutating siblings like apply_script_patch and verify_change. The 'non-mutating' qualifier further sharpens 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 description implies usage for planning phases by calling itself 'non-mutating' and targeting 'a requested feature or change', but it does not explicitly name alternatives or state when not to use this tool. 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.
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 goes beyond a simple recitation by explaining what is verified ('live Studio state'), what is rerun ('source sanity audits'), and what is reported ('index drift'). While it does not explicitly state that it is read-only or describe error conditions, the verb 'Verifies' and the action 'reports' strongly imply a non-mutating operation, providing meaningful behavioral context.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence with three concise clauses, all of which earn their place. It is front-loaded with the main verb and resource, and there is no redundant or filler 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?
Given that this tool has only one parameter, no output schema, and no annotations, the description is quite complete. It explains the core purpose and the key behaviors (verification, sanity audits, index drift reporting). It could mention what the output looks like or any prerequisites, but for a tool of this simplicity, the description adequately covers the essential 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?
The input schema covers 100% of the parameter (transaction_id) with a description that explains it is 'Transaction ID returned by a mutation tool.' The tool description adds no additional semantic detail about the parameter beyond what the schema already provides. Baseline of 3 is appropriate because the schema does the heavy 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 uses a specific verb ('Verifies') and clearly identifies the resource ('recorded transaction against live Studio state'), along with additional actions ('reruns source sanity audits' and 'reports index drift'). It is specific enough to distinguish from sibling tools like sanity_check_script or audit_* tools, which focus on other aspects.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies a clear usage context: after a mutation tool returns a transaction_id, this tool verifies the transaction against live state. It does not explicitly name alternatives or exclusions, but the context is sufficiently clear for an agent to know when to invoke it. It lacks explicit 'use this instead of X' guidance, but that is not essential for this tool.
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 and does disclose a key behavioral trait: 'safely retains the last valid cache on failure'. It also specifies the data source ('live Studio evidence'), which adds meaningful context. It doesn't cover all potential side effects, but the safety note improves transparency beyond a minimal statement.
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 a clear prefix. Every word adds value: the entry-point flag, the action, the data source, and the safety behavior. 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?
For a zero-param tool, the description conveys the core function, a safety detail, and its entry-point role, making it sufficient for selecting the tool. However, it does not mention what the tool returns or any prerequisites, which would be helpful but not critical given the low complexity.
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 fully documents this (100% coverage). The description doesn't need to add parameter details, and per the rubric, 0-param tools receive a baseline score of 4.
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 ('Rebuilds') and resource ('versioned project index'), and the '(RECOMMENDED ENTRY POINT)' flag differentiates it as the intended first tool to invoke among siblings. This gives the agent a precise understanding of what the tool does and its role.
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 signals when to use the tool with '(RECOMMENDED ENTRY POINT)', providing clear entry context. However, it does not explicitly state when not to use it or name alternative tools for exclusion, so it stops short of a 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It reveals that the rollback happens in reverse order, that there are drift preconditions, and that dry-run is optional. This is substantive behavior context, though it does not explicitly mention permission requirements or the exact failure mode when drift is detected without force.
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 clearly states the core action and key conditions. Every word earns its place; no filler or 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 tool with 3 well-documented parameters and no output schema, the description provides sufficient context about the operation's core behavior and safety features. It could mention return values or error handling, but those are less critical for a rollback tool, and the schema covers the required input.
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 provides 100% coverage of parameter descriptions, so the baseline is 3. The description adds value by linking 'drift preconditions' to the force parameter and explicitly calling out dry_run as optional, which helps the agent understand the purpose of these parameters beyond their schemas.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's function: reverts a recorded transaction in reverse order. It specifies the resource (transaction) and the action (revert), and the mention of drift preconditions and dry-run distinguishes it from other sibling tools that focus on analysis or patching.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies when to use this tool: to revert a recorded transaction, with a safety mechanism for drift. It does not explicitly name alternatives or provide when-not-to-use guidance, but the context is clear given the sibling tools are mostly read/analysis tools.
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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