Atlas UI

by calebsjames

Overview Schema Related Servers Score Discussions

TypeScript

Local

Server Quality Checklist

Profile completionA complete profile improves this server's visibility in search results.

Latest release: v3.0.0
Disambiguation5/5
Each tool targets a distinct aspect of codebase analysis and testing, from finding dead code to auditing templates to verifying data flows. Even tools that seem similar (e.g., check_page vs. render_component vs. capture_flow) have clearly different scopes: one-time snapshot, component rendering, and multi-step flows, respectively. No two tools have overlapping purposes.
Naming Consistency5/5
All tool names follow a consistent verb_noun pattern using lowercase and underscores (e.g., find_dead_code, get_component_detail, capture_flow). No mixing of conventions like camelCase or different verb styles. The naming is predictable and readable.
Tool Count4/5
21 tools is on the higher end but well-justified for a comprehensive codebase exploration and testing suite. Each tool serves a specific purpose, and the count reflects the breadth of features (catalog browsing, impact analysis, runtime verification, etc.) without feeling excessive.
Completeness5/5
The tool set covers the full lifecycle of codebase analysis: discovery (list/search), inspection (get_component_detail, get_data_flow), dependency mapping (get_dependency_chain, whats_affected), dead code detection, template auditing, runtime verification (check_page, render_component, capture_flow), and even login recovery. There are no obvious gaps for the intended use case.
Average 4.1/5 across 21 of 21 tools scored. Lowest: 3.5/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 9 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 MIT License.
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.
Add related servers to improve discoverability.

How to sync the server with GitHub?

Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.

To manually sync the server, click the "Sync Server" button in the MCP server admin interface.

How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
While no annotations are provided, the description discloses the dual matching approach and scoring factors. It does not mention read-only behavior or side effects, which is acceptable for a search tool but could be more 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?
Two concise sentences front-load the purpose and key details. No redundant 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?
The description covers the matching method, scoring criteria, and result limit, but lacks details on result format, ordering, and any access requirements. Given the number of sibling tools, more completeness would help.
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 single parameter 'description' is well-documented in the schema with examples. The tool description adds context about scoring but does not enhance 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 clearly states the verb 'find', the resource 'components', and the method using keyword and structural matching. It specifies scoring criteria and result limit, distinguishing it from siblings like 'search_components'.
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 explicit guidance on when to use this tool versus alternatives like 'search_components' or 'find_component_usages'. The description does not mention prerequisites or 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 carries full burden. It mentions the tool returns 'reasons explaining why they appear unused', but does not disclose any behavioral traits such as performance impact, limitations (e.g., only scans static imports), or edge cases. This lack of detail reduces 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?
The description is two sentences long. The first sentence defines the tool's action and scope, the second explains output and use case. Every word serves a purpose, with no redundancy or filler. It is front-loaded and efficient.
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 parameter and no output schema, the description adequately covers what the tool does, what it returns (unused exports with reasons), and a typical use case (cleanup). It lacks detail on the exact output format, but the provided context is sufficient for most agents to decide 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% as the single parameter 'layer' has a clear description in the input schema ('Optional: limit dead code search to a specific architecture layer'). The tool description adds no additional meaning beyond the schema, earning the baseline score of 3.
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 'find' and resource 'dead code', with clear scope ('exported components, hooks, services, and adapters'). It distinguishes from siblings like 'find_component_usages' by focusing on unused exports rather than usages of a specific component.
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 states 'Useful for codebase cleanup' but does not explicitly mention when to use this tool versus alternatives (e.g., 'find_component_usages'), nor does it specify when not to use it. The usage context is implied but not formalized.
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 full burden. The verb 'Get' implies a read-only retrieval with no side effects, which is positive. However, the description does not mention any potential limitations (e.g., size of mapping, performance), authentication needs, or data freshness. It is minimally adequate but not thorough.
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 sentence that effectively front-loads the main purpose. It is concise but lists many return items in a stream-of-consciousness style. Slightly improving structure (e.g., listing items) could enhance readability but it remains efficient.
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 zero parameters and no output schema, the description covers the return content comprehensively (routes, components, protection, hooks, child components, dynamic segments, hierarchy). It is nearly complete for a simple retrieval tool, though it lacks mention of any edge cases or limitations.
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 input schema is empty with 100% coverage. According to the guidelines, the baseline for 0 parameters is 4. The description adds no parameter information because there are none, which 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 purpose: getting the complete route -> page -> component mapping. It lists specific return items (protection status, hooks, child components, etc.), making it highly specific and differentiating it from siblings that might focus on other aspects like component details or architecture overviews.
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 on when to use this tool vs alternatives such as get_architecture_overview or get_component_detail. The description only states what the tool does, but omits any context about when it's appropriate or when to choose another sibling tool.
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 covers the ambiguous response behavior and lists return contents, but does not specify error handling for missing names or other edge cases.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise with three sentences: first states purpose, second lists returns, third covers edge case. No fluff and 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?
Covers primary usage and ambiguous disambiguation, lists return contents (compensating for no output schema), but omits behavior when name is not found. Fairly complete for a moderately complex 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?
Input schema already describes both parameters adequately (100% coverage). The description reinforces the disambiguation use of `file` but does not significantly extend 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 retrieves detailed information about a custom hook or Vue composable, distinguishing it from sibling tools like get_component_detail by specifying the resource type and listing specific return fields.
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 explains how to handle ambiguous matches by re-calling with `file`, but does not provide explicit guidance on when to use this tool over alternatives or 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.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description bears full responsibility. It discloses what data is returned but fails to mention whether the operation is read-only, any side effects, or performance implications. For a tool with no annotations, this is minimal 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?
The description is a single, well-structured sentence that front-loads the purpose and lists key outputs. Every word adds value, and there is no redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given there are no parameters and no output schema, the description adequately explains what the tool returns. It is complete for a simple overview retrieval tool.
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 no parameters (empty schema, 100% coverage), so the baseline is 4. The description adds no parameter info, but none is needed as there are no inputs.
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 it returns a high-level overview of the application architecture, listing specific elements like counts by layer, category breakdown, data flow chains, and route map. It distinguishes itself from sibling tools that focus on detailed components, hooks, or data flows.
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 use for broad architectural understanding through phrases like 'high-level overview,' but does not explicitly state when to use this tool versus siblings, nor provides when-not or alternative recommendations.
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 full burden. It discloses the type of information returned and ambiguity handling, but lacks details on error cases, side effects (none expected), or what happens if no parameters are provided (since required is empty).
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 in the first paragraph, then a short second paragraph. Front-loaded with purpose, no wasted words. Very 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?
Given no output schema, the description explains return values (prop names, types, etc.) and ambiguity handling. It covers the main use cases, but could be more explicit about error handling when component is not found. Still, it is largely complete for its purpose.
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 100%, baseline 3. The description adds value by explaining the usage pattern (provide either 'name' or 'componentPath', disambiguate with 'file'), and clarifies the relationship between parameters, going beyond the individual parameter descriptions.
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 gets TypeScript prop interface for a component, specifying return fields and supporting both interface and type alias. However, it does not explicitly differentiate from sibling tools like 'get_component_detail' or 'find_component_usages', which could also involve props.
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 clear guidance on how to use the tool: provide either 'name' or 'componentPath', and if ambiguous, re-call with 'file'. It implies when to use it (getting prop interface) but does not mention when not to use it or 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 full burden. It discloses the outputs (screenshot and diagnostics) and the requirement of a running dev server. However, it does not mention whether the tool has any side effects (e.g., modifying state) or authentication requirements.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise (4 sentences) and front-loaded with the primary action. Every sentence adds value, including the purpose, output, mechanism, use case, and requirement.
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 complexity (6 parameters, no output schema, no annotations), the description explains the main outputs and prerequisite but lacks details on the exact return format (e.g., media type of screenshot, structure of diagnostics) and error handling.
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 100% coverage with detailed descriptions for each parameter. The tool description itself does not add any additional meaning beyond what the schema provides, so the baseline score 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 tool renders a catalog component and returns a screenshot plus runtime diagnostics. It distinguishes from siblings by mentioning automatic route resolution via the route map and focusing on visual verification of a component change.
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 explicit context on when to use the tool (to visually confirm a change works) and a prerequisite (dev server running). However, it does not explicitly state when not to use it or list 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.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Without annotations, the description carries the full burden of disclosure. It transparently describes the tool's behavior: it traces data flows, returns per-chain details and allEndpoints, and handles ambiguity. It implies a read-only, idempotent operation. No destructive or side effects are mentioned, which is appropriate for a trace tool.
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 paragraph of three sentences, efficiently covering purpose, behavior, and ambiguity handling. It front-loads the main action. While it could benefit from slight restructuring (e.g., bullet points), it is concise and contains no unnecessary words.
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 complexity of a data flow tracing tool and the absence of an output schema, the description provides sufficient context: it explains what is traced, what is returned (per-chain details with render path, allEndpoints), and how the 'depth' parameter works. It does not detail exact output structure but gives a solid high-level understanding.
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 does not add any new information about parameters beyond what is already in the schema. The schema descriptions for 'name', 'file', and 'depth' are repeated verbatim in the description, providing no extra semantic value.
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 'Trace' and the resource 'full data path from a component... to API endpoints'. It specifies what is included (child components, store-mediated flows) and how ambiguity is handled (returns ambiguous with candidates). This effectively distinguishes it from siblings like 'verify_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 Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context on when to use the tool (for tracing data flows, including child and store-mediated flows) and how to handle ambiguous matches (re-call with 'file'). However, it does not explicitly mention when not to use it or compare it to alternative tools like 'verify_data_flow'.
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 provided, so description must fully disclose behavior. It mentions returning both upstream and downstream relationships and supports recursive traversal with depth up to 3. However, it does not explicitly state it is a read-only operation, nor does it describe the structure of the returned data. This is adequate 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?
Two sentences, front-loaded with the main action, followed by return type and use case. No superfluous information. Every sentence adds value.
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 absence of an output schema, the description only vaguely states it returns relationships. For an agent to properly interpret the tool's output, more detail on the structure of the returned dependency chain (e.g., format of nodes, edges) would be beneficial. However, for a dependency chain tool with recursive traversal, the description provides a reasonable level of completeness.
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 already describes all three parameters with 100% coverage. The description adds value by explaining the depth parameter's purpose more specifically: 'Depth 2+ includes nested dependsOn/usedBy on child nodes.' This goes beyond the schema's generic description of depth as 'Recursion depth (1-3, default 1).' The examples for name parameter also help.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states the tool gets the full dependency chain for any component, hook, or service, specifying both upstream and downstream relationships. This distinguishes it from siblings like find_component_usages or get_data_flow. The mention of recursive traversal with depth parameter adds specificity.
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?
Description indicates it is useful for understanding impact of changes and tracing data flow, providing clear context. However, it does not explicitly state when not to use it or compare to alternative tools like find_component_usages or whats_affected, which could help further differentiation.
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 provided, so description carries full burden. It discloses navigation, screenshot, diagnostics output, and dependency on dev server. Does not mention side effects, but as a read-only diagnostic tool, 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 sentences, front-loaded with key action and output. Every sentence adds value 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?
Given no output schema, the description lists return values (screenshot, diagnostics) and requirements. It could mention error handling or response format, but is sufficiently complete for a diagnostic tool with detailed parameter 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 baseline is 3. The description adds no parameter-specific detail beyond the schema; the schema already explains each parameter thoroughly.
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 navigates to a URL and returns a screenshot with runtime diagnostics. It uses specific verbs and distinguishes itself from sibling tools as a page-breakage checker.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides clear when-to-use guidance: 'call it after editing to confirm the page still renders clean.' Requires dev server running. Lacks explicit alternatives or when-not-to-use, but context is sufficient.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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 discloses the tool's behavior well: it checks parent-child event wiring, excludes certain cases, and is Vue-only. It implies a read-only analysis but does not state if any side effects occur. Overall, it is transparent about its functional scope.
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 moderately long but well-structured, with each sentence providing important information. It front-loads the purpose and uses clear language. While slightly verbose in places, it avoids unnecessary details and maintains focus.
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 explains what the tool does and its constraints, but it does not describe the output format or return value. Given the tool's complexity (cross-component analysis) and the absence of an output schema, this is a notable gap. The description would benefit from stating what the tool returns (e.g., list of matches, parent-child pairs).
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 'file' is described in both the schema and the description. The description adds context: 'Optional path substring — only check parent components whose path matches.' This extends the schema's generic 'Optional path substring' by specifying how it scopes the analysis, adding value 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 finds dead event wiring across the catalog, specifying that it detects parent-child event binding issues where the child never fires the event. It distinguishes between dead plumbing (declares but doesn't emit) and typo/renamed events (neither declares nor emits). This is specific and differentiates from related tools like get_component_detail.
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 explains when to use the tool (to find dangling event listeners) and what it excludes (native DOM events, dynamic/undeclared emit APIs) to avoid false positives. It also references get_component_detail as a companion tool. However, it does not explicitly mention when not to use it or compare to other sibling tools like find_dead_code.
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?
Without annotations, the description carries full burden. It discloses successful behavior (returns full metadata) and edge case handling (ambiguous returns candidates). It does not explicitly state read-only nature, error handling for missing items, or performance considerations, but covers key behavioral aspects adequately.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise (two sentences), front-loads the primary purpose, and efficiently covers the edge case. Every sentence adds value with no redundancy or fluff.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Despite no output schema, the description specifies the types of metadata returned (props, hooks, etc.) and the ambiguous case. It is missing explicit error handling (e.g., 'component not found') but overall provides sufficient context for the tool's 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?
Schema coverage is 100% with descriptions for both parameters. The description adds value by explaining the disambiguation use case for the 'file' parameter and specifying example values for 'name'. It clarifies the 'ambiguous' return, which is not in 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 specifies a clear action ('get detailed information') and resource ('specific component, page, hook, service, adapter, or store by name'). It lists the extensive metadata returned, distinguishing it from sibling tools like get_component_props or get_hook_detail which are more focused.
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 explains behavior when multiple matches occur and instructs to re-call with 'file' parameter. However, it does not explicitly advise when to use this tool over specialized siblings (e.g., 'for props only, use get_component_props'). The usage context is implied but not fully clarified.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must carry full weight for behavioral disclosure. It states the tool 'walks React/Vue dev internals' and requires the dev server, which are important constraints. However, it does not mention whether the tool is read-only, what happens on error (e.g., if the dev server is not running), or performance implications. This leaves some gaps beyond what the context signals provide.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise (4 sentences) and front-loaded with the core purpose. Every sentence adds value: first sentence defines the tool, second explains the internal mechanism, third lists input options, fourth covers output format and prerequisite. No redundant or vague phrasing.
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 complexity (5 parameters, no output schema, no annotations, 21 siblings), the description covers the main use case, prerequisite, and key distinction from render_component. It lacks explicit description of the output format beyond 'text-only output' and does not discuss error scenarios or edge cases. An example of the output would improve completeness, but it is still adequate for an agent to decide when to invoke.
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 100% description coverage, so baseline is 3. The description adds value by explaining the relationship between component, route, and url: 'Give a catalog component, a route, or a raw url.' This clarifies that these are alternative ways to specify the target page, which is not obvious from the schema alone. The settleMs parameter is not elaborated in the description, but the schema handles it.
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: 'open a live page and report which CATALOG components are actually mounted on it, mapped back to their source files.' It distinguishes from siblings by explicitly noting 'Text-only output (use render_component for a screenshot).' The verb 'inspect' and the resource 'rendered page' are specific and unambiguous.
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 clear usage guidance: 'use it to go from the thing I see on screen to the file I should edit without grepping.' It mentions an alternative tool (render_component) and a prerequisite (dev server must be running). However, it doesn't explicitly contrast with other siblings like check_page or find_component_usages, which could also be relevant.
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 appropriately discloses that the tool uses fuzzy matching and multi-token scoring, and returns ranked results with a _score. It implies read-only behavior, though not explicitly stated. No contradictions.
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: first covers action and scope, second covers matching method and follow-up instruction. No wasted words, front-loaded with key 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?
The description explains the return format (compact summaries with name, layer, path, _score) which is important since no output schema. It could mention the default limit (20) but otherwise complete for a search 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 description coverage is 100%, so baseline is 3. The description adds context on matching algorithm and return format, but not significantly beyond the schema's parameter descriptions. The layer and limit parameters are already well-described in 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 it searches across the full codebase by name, path, description, or keywords, listing specific entity types. It distinguishes itself from sibling tools like get_component_detail and get_component_props by noting it returns compact summaries and advising follow-up for full data.
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 good context on when to use this tool (initial search) and explicitly mentions follow-up with get_component_detail/props for full details. However, it does not explicitly state when not to use it or other alternatives beyond those two.
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. It details the audit's scope (repo-wide), outputs (signals and findings), and references a related tool. It doesn't explicitly state read-only behavior, but the nature of an audit suggests no 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.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is informative but slightly lengthy. It front-loads the core purpose, then lists outputs and notes. While efficient, it could be trimmed slightly without losing meaning.
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 complexity of a repo-wide audit tool, the description covers return values (both per-component and synthesized findings) and mentions configuration dependency for z-index max. Without an output schema, this level of detail is 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?
The schema has one parameter 'file' with description. The description adds value by explaining its optional usage: 'Pass `file` to scope by path substring.' This context, combined with high schema coverage, justifies a score above baseline.
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: 'Repo-wide template-layer design-drift audit for Vue overlays/modals.' It specifies what it returns (per-component signals and synthesized findings) and distinguishes itself as a replacement for manual grepping of specific patterns.
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 usage for Vue overlay/modal audits and mentions the optional file scoping. It does not explicitly state when not to use it or provide alternatives, but it clearly indicates Vue-only applicability and scope limitation.
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, description carries full burden. It covers what is searched (components, pages, hooks, services) and what is returned (files, parent items, line numbers, usage type). However, it doesn't disclose limitations, performance implications, or behavior for non-existent names.
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, all information is relevant: purpose, scope, return data, use case. No redundant words or filler.
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?
Despite no output schema, the description sufficiently explains the return format. Tool is simple with one parameter, and the description covers purpose, scope, return data, and use case, making it complete for decision-making.
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 100% with a good schema description. The tool description provides concrete examples of valid names (e.g., 'Button', 'useHandoffState', 'patientService'), adding helpful 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?
Description states exactly what the tool does: find where a component/hook/service is used across the codebase, including imports and renders. It specifies the returned data (files, parent items, line numbers with usage type). Clearly distinguishes from siblings like search_components and get_component_detail.
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 'Useful for impact analysis', providing a clear use case. Does not explicitly mention when not to use or name alternatives, but the context of siblings and the description's specificity imply appropriate usage.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full behavioral disclosure. It describes the return structure (totalCount, lastScanned, byLayer, components), notes that byLayer covers the whole catalog, and warns that verbose can be 'large'. This provides sufficient transparency for a read-only operation.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise, with no wasted words. It front-loads the core purpose and structures information logically: return format, then parameter guidance, then follow-up suggestion.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema, the description adequately explains the return structure. It covers the two parameters and provides a follow-up recommendation. For a tool with many siblings, it provides sufficient context for an agent to decide when to use it.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Both parameters have 100% schema description coverage, so the schema already documents them. The description restates that layer filters the components list and verbose returns full metadata, but does not add significant new 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 clearly states the tool lists the codebase catalog as compact summaries with specified fields. It distinguishes itself from sibling tools like get_component_detail (which provides full data for one item) and get_architecture_overview.
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 explains when to use verbose mode and suggests following up with get_component_detail for full item data. It also mentions the layer filter. However, it does not explicitly exclude cases or compare with other sibling tools like search_components.
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?
Without annotations, the description carries full burden. It outlines the workflow (render, interact, wait, compare) and explains key outputs (unexpectedApiCalls, verdict) and behavior (method-aware, delayed reading). Minor omission of side effects, but overall transparent.
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 dense paragraph but efficiently conveys all necessary information. It could be improved with structural elements like bullet points, but it remains readable and focused.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (6 params, nested objects, no output schema), the description thoroughly covers the workflow, expected outputs, and prerequisites (dev server). It leaves no critical gaps for an agent.
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 100%, so baseline is 3. The description adds value by explaining the purpose of actions (exercising mutations) and settleMs (wait time), and provides examples for within. This elevates it above baseline.
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 defines the tool's function: comparing real network traffic against static analysis predictions. It uses a specific verb (verify), resource (data flow), and methodology (method-aware matching), differentiating it from siblings like get_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 Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explains when to use the tool (after static analysis) and notes a prerequisite (dev server running). It also details how actions drive mutation endpoint verification, providing sufficient context despite lacking explicit alternatives.
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 full burden. It discloses the upstream walk, risk classification, ordering by risk, and auto-detection from git status. It does not mention authorization or rate limits, but for a read-only analysis tool, the transparency 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.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single dense paragraph that is efficient but could be slightly more structured for readability. It front-loads the key action ('edit→verify glue') and provides all necessary details 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?
Given the tool's complexity (dependency graph, risk classification, paging) and lack of output schema, the description adequately describes the output components (risk per file, overallRisk, ordered routes). It provides sufficient context for an agent to understand the return value.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, and the description adds significant context: auto-detection for the files param, paging for offset and maxItems, and distance cap meaning for maxDistance. It also explains the risk classification and overall risk in the output.
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: given changed files (or auto-detected from git status), it walks the dependency graph upstream to find affected components/pages, maps to routes, and returns verification targets. It distinguishes from siblings like check_page or render_component by being a planning/discovery tool, not a direct action tool.
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 says 'Call it after editing to know exactly what to re-check in the browser and how carefully,' providing clear when-to-use guidance. It doesn't explicitly mention when not to use or alternatives, but the context implies it's for post-edit analysis, not for other tasks.
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 provided, so description carries full burden. It discloses the recovery nature, no-op condition, and that it clears state. Lacks mention of permissions or if repeated calls have side effects, but still provides solid 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.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Description is moderately sized but each sentence is meaningful. It front-loads the main action, follows with usage guidance and edge cases. Minor redundancy but overall 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 tool with one optional parameter and no output schema, the description covers use cases, edge cases (no-op, public routes), and parameter behavior. It does not mention return value, but is adequate for agent decision-making.
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 100% for the single parameter. Description adds value by stating default behavior (relogin defaults true) and effect of false (just clear state). Goes beyond what schema 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 recovers the browser's authenticated session in-place, with specific actions: re-read config, clear cookies/storage, re-run login flow. It distinguishes from sibling tools which are about component analysis, not session management.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly states when to use: when authed runtime tools fail with login error or after changing browser.login config. Also clarifies when not needed: public routes can skip login entirely, providing a clear alternative parameter.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Without annotations, the description fully discloses key behaviors: state persistence across steps, API call reporting per step, pass/fail aggregation with screenshot on failure, and dev server requirement. This provides complete behavioral transparency for an AI 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 focused paragraph with a strong front-loaded topic sentence. Every subsequent sentence adds essential detail (state, API calls, diagnostics, prerequisite) without redundancy or filler.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (3 params, many action types, no output schema), the description covers flow building, navigation, interactions, error handling, and output expectations. It leaves no critical gaps for an AI agent to use the tool effectively.
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 100%, so baseline is 3. The description adds value beyond the schema by explaining interactive behavior (e.g., visible matches preferred, within scoping logic, per-step navigation vs actions). It enriches parameter understanding without repeating 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 specific verbs ('Drive', 'screenshot') and clearly identifies the resource ('multi-step user flow against a SINGLE persistent page'). It distinguishes from siblings by highlighting the multi-step, stateful nature, which is unique among sibling tools like check_page or inspect_rendered_page.
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 explains when to use this tool (for multi-step flows with navigation and interactions) and implicitly contrasts with simpler siblings. It notes a prerequisite ('Requires the dev server to be running') but lacks explicit when-not-to-use guidance or alternatives.
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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