Trace MCP

Server Quality Checklist

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Latest release: v1.0.0
Disambiguation4/5
Tools are mostly distinct, but pairs like trace_file/trace_usage and extract_file/extract_schemas have overlapping purposes. Descriptions clarify the scope difference (single file vs. general), so ambiguity is low.
Naming Consistency5/5
All tool names follow a consistent verb_noun snake_case pattern (e.g., extract_file, trace_usage, init_project), making it easy to infer functionality.
Tool Count5/5
With 11 tools, the server covers extraction, tracing, validation, scaffolding, and project management without being bloated. The scope is well-defined.
Completeness5/5
The tool surface covers the full workflow: project init, schema extraction, usage tracing, contract comparison, cross-referencing comments, scaffolding, and watching. No obvious gaps for the intended domain.
Average 3.4/5 across 11 of 11 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 0 commits in the last 12 weeks
- Last stable release on January 10, 2026
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is passing
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.
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```
{
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}
```
Then . Browse examples.
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How is the quality score calculated?

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

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

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

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

Tool Scores

Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description must fully disclose behavioral traits. It states it adds comments to files, implying writes, but does not specify if it is destructive, whether it requires prior validation, or what happens on dryRun=false. Important behavioral details are missing.
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 that front-load the core purpose. Every sentence adds value with no redundant information. Ideal structure for quick comprehension.
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 good schema coverage, the description lacks context about the validation prerequisite, the commenting process, dryRun behavior, and file modification implications. For a tool with no output schema and annotations, more complete context is needed.
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% description coverage, so the schema already explains each parameter. The description adds context about 'validated pairs,' but does not provide significant additional meaning beyond the schema. Baseline 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 adds cross-reference comments to producer/consumer pairs and documents the contract relationship. The verb 'add' and resource 'comments' are specific, and it distinguishes from sibling tools like compare or extract_file. However, it does not clarify what 'validated' means, slightly reducing clarity.
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. The description implies it is for documenting contract relationships after validation, but it does not mention prerequisites, when not to use it, or compare to sibling tools. Usage context is left implicit.
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 must disclose all behavioral traits. It only says 'infers schema' but doesn't mention whether it writes files, requires permissions, or has side effects. Critical behavior like overwriting existing stubs is not disclosed.
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 with no redundancy. Every word is functional and front-loaded.
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?
No output schema or annotations exist. The description does not mention output format (e.g., file paths, generated code language) or prerequisites. For a code generation tool, this is insufficient for an agent to use correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema covers 100% of parameters with descriptions. The description adds context that the stubs are generated from client usage patterns, but does not elaborate on how consumerDir influences inference or how includeHandler affects output.
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 generates API stubs and infers schema from client usage. However, it does not explicitly differentiate from its sibling scaffold_consumer, which likely does the opposite (generate client stubs from API schema).
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 scaffold_consumer or other code generation tools. Context like prerequisites (e.g., consumerDir must exist) is missing.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations exist, so the description must cover behavioral traits. It mentions the what (extract, trace, compare) but not side effects, permissions, or output behavior. Critical details like whether it modifies files or requires network access are missing.
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 highly concise: two sentences, each serving a distinct purpose. The first sentence states the core function, the second broadens scope. No unnecessary words.
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 5 parameters, the description should provide more context about the result format or behavior. It omits any hint of what the comparison outputs (e.g., mismatches report), leaving a significant gap for a 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?
Schema coverage is 100% with clear descriptions for all 5 parameters. The tool description adds no additional parameter semantics beyond what the schema already provides, meeting the baseline expectation.
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 provides a specific verb ('validate') and resource ('contract'), and explicitly distinguishes from siblings by stating it performs three actions (extract, trace, compare) in one tool. It covers multiple languages and protocols, making its scope clear.
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 explain when to use this tool versus the many sibling tools like 'extract_schemas' or 'trace_usage'. There is no guidance on prerequisites, when to avoid, or typical scenarios, leaving the agent to guess.
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 provided, so the description must disclose behavioral traits. It states the output conceptually but does not mention side effects, permissions, or whether it is read-only. The agent cannot infer safety or side effects without more detail.
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 resource. It is concise with no extraneous 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 tool's simplicity (one parameter, no output schema), the description provides sufficient context about what is returned. It could optionally mention that no validation or mutation occurs, but overall it is adequate.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, and the description adds little beyond the schema's 'Root directory with .trace-mcp config'. The description of the parameter is clear but not enriched by the tool description.
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 purpose: 'Get the status of a trace project' and specifies what is included (config, cache state, last validation result). It effectively distinguishes from sibling tools like init_project or trace_file, which are more about creation or extraction.
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. The description lacks prerequisites or context for appropriate usage, leaving the agent to infer from the tool name alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are present, so the description must carry the transparency burden. It fails to disclose side effects (e.g., file overwriting), permissions, or whether the generated code is saved somewhere. Only the basic output types are mentioned.
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 concise sentences with the core action front-loaded. Every word adds value; no redundancy or filler. It is efficiently structured.
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 5 parameters and no output schema or annotations, the description omits important details like return values, file system effects, required directory structure, or error handling behavior. It is insufficient for a code generation 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 100%, so the input schema already documents all parameters. The description adds minimal extra meaning by explaining the output formats (TypeScript, React hooks, Zustand), which aligns with the 'target' enum. Baseline 3 applies.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: generating type-safe client code from API schemas, and lists concrete outputs (TypeScript functions, React hooks, Zustand actions). This differentiates it from the sibling 'scaffold_producer' which presumably generates server-side code.
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 generating client code after having API schemas, but does not explicitly state when to use this over alternatives like scaffold_producer or other tools. No exclusion criteria or prerequisite conditions 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 carries full burden for behavioral disclosure. It only states the function (extracting schemas) without mentioning if the operation is read-only, if it requires any permissions, or what happens on failure. The supported formats are listed, but key behavioral traits are missing.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is composed of two short, information-dense sentences. Every word contributes: the first sentence states the action and scope, the second lists supported languages. No wasted text, and the critical info is front-loaded.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool has one parameter and no output schema. The description explains what the tool does and supported inputs, but does not mention the output format or behavior in edge cases (e.g., unsupported file type). For a simple extraction tool, it is minimally viable but leaves gaps in expected return value and error handling.
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% (one parameter with description 'Path to source file (any supported language)'). The description adds value beyond the schema by listing the specific supported languages (TypeScript, Python, etc.), which clarifies the acceptable input and adds context to the parameter's scope.
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 'Extract API schemas from a single file', which is a specific verb+resource combination. It lists supported languages, making the tool distinct from siblings like extract_schemas (which likely handles multiple files). The purpose is precise 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 Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description does not provide guidance on when to use this tool versus alternatives. No explicit 'when-to-use' or 'when-not-to-use' instructions are given. The user must infer from the tool name that it is for single files, but no contrast with sibling tools is provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. It discloses detection capabilities (HTTP calls, GraphQL queries, etc.), but does not mention side effects, output format, or any limitations. 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.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, efficient and front-loaded with core purpose. No redundancy, but could include a brief example or usage hint without bloating.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple tool with one parameter and no output schema, the description covers the essential behavior. However, lacking differentiation from sibling trace_usage reduces completeness slightly.
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% for the single parameter filePath, which already includes a description. The tool description adds no additional semantic 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 verb 'trace' and the resource 'file', and enumerates specific API types detected (HTTP, GraphQL, MCP, response property). This distinguishes it from sibling tools like trace_usage which may have broader scope.
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 (e.g., trace_usage). No prerequisites, context, or when-not-to-use information provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden. It mentions auto-detection of format, which is helpful, but does not disclose limitations (e.g., behavior on large directories, handling of unsupported formats, or whether schemas are validated). This level of transparency 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.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise (one paragraph) and front-loaded with the main purpose. However, the list of supported formats could be better structured (e.g., bullet points) for readability. It is still efficient with no 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?
Given the complexity (many formats, no output schema), the description lacks explanation of the output format or sample usage. It does not clarify what the extracted schema looks like (e.g., JSON, YAML, or structured data). This leaves some gaps for an agent.
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% for 3 parameters, each with descriptions. The description adds context about supported formats but does not enhance parameter semantics beyond what the schema provides. Baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
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: 'Extract API schemas from source code.' It lists numerous supported formats (MCP tools, OpenAPI, GraphQL, tRPC, REST, gRPC, Python, Go, SQL DDL), making it distinct from sibling tools like extract_file (which extracts files) and compare (which compares schemas).
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 when source code contains API schemas but lacks explicit guidance on when to use this tool versus alternatives. It does not provide when-not-to-use conditions or mention sibling tools as 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?
No annotations provided. Description says it creates project structure, but doesn't disclose idempotency, overwrite behavior, or side effects. Adequate but could be more transparent.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Two concise sentences with no wasted words. Action verb front-loaded. Every sentence adds value.
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?
For a setup tool with 5 parameters and no output schema, description is too minimal. Does not explain return value, what files are created, or what happens if project already exists. Incomplete for effective agent usage.
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%; each parameter is documented in schema. Description adds no additional parameter context beyond schema. 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?
Description clearly states verb (initialize) and resource (trace project), and specifies what it creates (.trace-mcp config directory, project structure for watch mode and caching). Distinguishes from siblings like scaffold_consumer which focus on code scaffolding.
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?
Implies use when starting a trace project, but no explicit guidance on when to use vs alternatives like scaffold_consumer or get_project_status. No exclusions or prerequisites mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must fully disclose behavioral traits. It explains the four actions and their purposes, but lacks details on side effects, permissions, error handling, or whether the tool is safe/destructive. The behavior is partially transparent.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence followed by a bullet-like list of the four actions. It is concise, front-loaded, and contains no extraneous information. Every part earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool has four distinct actions and no output schema. The description does not explain what each action returns (e.g., status output, poll format) or elaborate on 'auto-revalidate contracts'. This leaves gaps for an AI agent to infer behavior. It is complete enough for a simple understanding but lacks depth for robust usage.
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% description coverage, so the schema already documents both parameters (projectDir and action with enum). The description adds minimal extra meaning beyond restating the actions, which are already in the enum descriptions. 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 the tool watches project files for changes and auto-revalidates contracts, listing four specific actions (start, stop, status, poll). This distinguishes it from all sibling tools, which do not offer file watching.
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 file monitoring but does not explicitly state when to use it versus alternatives, nor does it provide when-not or prerequisites. Among siblings, no tool duplicates this functionality, so guidelines are adequate but not 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?
No annotations provided, so description carries full burden. It discloses detection capabilities clearly, but doesn't mention whether it modifies anything, requires network access, or handles errors. Still fairly transparent.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Two efficient sentences, front-loaded with key action and specific examples. Every sentence earns its place with zero waste.
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?
Adequately covers the tool's capability for its complexity. No output schema, but description doesn't need to explain return values. Could mention output format or limitations, but still complete enough.
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% with clear parameter descriptions. The description adds no extra meaning beyond what schema provides, achieving baseline 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?
Description clearly states the tool's purpose: tracing how client code calls APIs. It lists specific types of API calls detected (MCP, HTTP, Apollo, Python libraries), making it specific and distinguishing it from siblings like trace_file.
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?
No explicit guidance on when to use this tool vs alternatives like trace_file or compare. The description implies usage for tracing API usage but lacks when-not-to or alternative recommendations.
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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