Judges Panel

by KevinRabun

Overview Schema Related Servers Score Discussions

TypeScript

Local

Server Quality Checklist

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Latest release: v3.129.9
Disambiguation4/5
The tools cover distinct functionalities (evaluation, dependency analysis, benchmarking, scaffolding, file ops, feedback), but the many evaluate_* variants (evaluate_code, evaluate_code_streaming, evaluate_diff, etc.) could be confused despite descriptive differences.
Naming Consistency4/5
Most tools follow a verb_noun pattern (e.g., evaluate_code, fix_code, list_files), with 'evaluate_' heavily used. Minor deviations like benchmark_gate and session_status are exceptions.
Tool Count3/5
At 25 tools, the count is on the higher end but still reasonable given the server's broad scope (code evaluation, dependency analysis, benchmarking, scaffolding, etc.). Could be streamlined.
Completeness4/5
The tool surface covers evaluation, fixing, dependency analysis, benchmarking, feedback, and scaffolding comprehensively. Minor gaps: no tool to update triage decisions or evaluate arbitrary specific lines outside diffs.
Average 3.8/5 across 25 of 25 tools scored. Lowest: 2.9/5.
See the Tool Scores section below for per-tool breakdowns.
- 0 of 3 community issues answered or closed in the last 6 months
- 0 commits in the last 12 weeks
- Last stable release on April 7, 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 carries full burden for behavioral disclosure. It only states that it 'creates a self-contained plugin file', but does not mention whether it overwrites existing files, required permissions, rate limits, or what happens if the file already exists.
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 two sentences. The first sentence immediately states the action and target, followed by a clear elaboration of what the generated file includes. No wasted 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?
Despite having 8 parameters and no output schema, the description does not explain the return value (e.g., file path written), error conditions, or side effects. For a tool that interacts with the filesystem, this is insufficient.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
All 8 parameters have descriptions in the input schema (100% coverage). The description adds no additional meaning about parameters, remaining at a general overview level. Baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool generates a starter plugin template with specific features (custom rules, optional custom judges, lifecycle hooks). It uses a specific verb 'Generate' and identifies the resource. However, it does not explicitly distinguish from the sibling tool 'scaffold_judge', which could cause minor confusion.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives like scaffold_judge. It lacks any context about prerequisites, use cases, 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 carries full burden. It mentions cloning and report generation but lacks details on data persistence (e.g., temporary clone cleanup), security implications, resource usage (e.g., disk space), or potential side effects. Behavioral traits like 'keepClone' parameter exist but are not explained upfront.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Single sentence efficiently conveys the essential action and output. No fluff; every word adds value. Properly front-loaded with the key steps.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given 13 parameters, no output schema, and no annotations, the description is insufficiently complete. It does not explain 'full judges panel,' report format, or default behavior for critical parameters like maxFiles or maxFileBytes. An agent would need to infer or test many details.
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% (all 13 parameters described in input schema). The description adds no additional meaning beyond the high-level purpose; it does not explain parameter interactions or defaults. Baseline 3 is appropriate as schema handles the documentation.
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 action (clone, run, generate), the resource (public repository), and the output (consolidated markdown report). It effectively distinguishes from sibling tools like evaluate_code (single file) or evaluate_project (likely local) by specifying it works on a public repo via URL.
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 vs. alternatives (e.g., evaluate_code, evaluate_project). The description implies it is for public repos but does not state prerequisites, limitations, or cases where other tools are preferable.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must disclose all behavioral traits. It only mentions return structure (per-file verdicts, aggregate statistics) but omits whether the operation is destructive, required authentication, error handling, performance implications of batching, or limits on input size.
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 purpose and output summary. No redundant or extraneous information. Every word 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?
Given the lack of output schema and moderate complexity (nested objects, optional config), the description should explain more: behavior when config is omitted, error handling, maximum batch size, and how config overrides affect verdicts. The description is too brief for a batch 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% and all parameters are described in the schema. The description adds no additional meaning beyond what the schema provides. With high coverage, baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb ('Evaluate') and resource ('multiple code files'), clearly distinguishing it from siblings that evaluate single files or diffs. It states exactly what the tool does and what it returns.
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 multiple files but provides no guidance on when not to use it or how it differs from alternatives like evaluate_code, evaluate_file, or evaluate_diff. No explicit when-to-use or alternative recommendations.
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 discloses no behavioral traits such as permissions, side effects, rate limits, or return format. For a tool that submits code for analysis, details about whether the analysis is synchronous, what happens on failure, or any idempotency guarantees are missing, leaving agents uninformed about critical operational aspects.
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 two sentences: the first clearly states purpose, the second lists domains and judge IDs. It is front-loaded and relatively concise, though the list of judge IDs is lengthy and partially redundant with the schema. Every sentence adds value, but the list could be trimmed or referenced instead of enumerated.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (7 parameters, nested config object, no output schema), the description lacks completeness. It does not explain what the tool returns (e.g., findings, severity, confidence), how errors are handled, or how the analysis relates to sibling tools like evaluate_code_streaming. The user needs more context to set expectations for the result.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the baseline is 3. The description adds no additional meaning beyond the schema; it repeats the judge IDs list already in the schema's description. It does not explain the interaction of parameters (e.g., how config overrides work with judgeId) or provide behavioral nuance. Thus, no extra 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 'Submit code to a specific judge for targeted domain analysis,' identifying the verb (submit), resource (code), and target (single judge). It distinguishes from sibling tools like evaluate_code and evaluate_batch, and lists all 43 judge IDs, making the tool's scope 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 explains that the tool handles all code types and lists domains and judge IDs, providing clear context for when to use a specific judge. It does not explicitly state when not to use or name alternatives, but the sibling tool list (e.g., evaluate_code, evaluate_project) implies different use cases, making the guidance strong but not fully explicit.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations exist, so the description must fully disclose behavior. It states the tool filters findings to changed lines and supports two input modes. However, it does not mention side effects (e.g., read-only nature), permissions, or limitations (e.g., repo must be accessible). The description covers core behavior but omits important operational details.
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 extremely concise: three sentences that front-load the purpose and then detail modes. Every sentence adds value with no redundancy or fluff. Ideal length for quick comprehension.
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 7 parameters, a nested config object, and no output schema. The description explains the overall workflow and two input modes but does not cover configuration options, auto-tuning, or confidence filtering. While the schema fills the gaps, the agent might benefit from a brief summary of optional features.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so the baseline is 3. The description adds context about the two key modes (repoPath+base vs diffText) but does not elaborate on other parameters like config, autoTune, maxPromptChars, or confidenceFilter. It provides minimal added value beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool evaluates code changes from a git diff, parses unified diff, runs tribunal on changed lines, and supports both live repos and pre-computed diffs. The verb 'evaluate' is specific and the resource 'git diff' is well-defined. However, it does not distinguish from the sibling tool 'evaluate_diff', which may have overlapping functionality.
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 two usage modes (live repo vs pre-computed diff) and provides default base ref, but does not give explicit guidance on when not to use this tool or mention alternative tools (e.g., evaluate_code for whole-file analysis). The context is clear but lacks 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 bears full responsibility. While it states the tool evaluates and applies fixes, it fails to disclose important behavioral traits: whether it is destructive (modifies input), idempotent, what the 'Judges Panel' entails, error handling, or any side effects. For a mutation tool, this is insufficient 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 exceptionally concise (two sentences) and front-loads the core action ('evaluate ... and automatically apply ... patches'). Every sentence is functional with zero redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with 6 parameters, full schema coverage, and no output schema, the description provides a high-level overview but lacks details on output format, behavior when no fixes exist, or error conditions. It is adequate but not comprehensive, especially given the absence of annotations.
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 minimal context beyond schema descriptions (e.g., 'same format as .judgesrc' for config), but does not significantly enhance understanding of parameters. It meets the minimal bar without adding extra 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 it evaluates code and applies auto-fix patches, returning fixed code and a summary. It uses strong verbs ('evaluate', 'apply', 'returns') and specifies the resource ('code'). It distinguishes itself from sibling evaluation-only tools like 'evaluate_code' by explicitly mentioning automatic fix application and returning fixed 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 suggests using this tool to fix issues in a single step, but does not explicitly guide when to use alternatives (e.g., 'evaluate_code' for evaluation only, 'evaluate_then_fix' for a two-step process). The usage is implied rather than explicitly stated with when/when-not conditions.
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 burden. Discloses reading file, auto-detection, and full judge panel, but lacks details on side effects, authorization, rate limits, or return format. Partially transparent.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, no redundancy, front-loaded with action. 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?
No output schema or annotations; description omits result format and post-evaluation steps. With 6 parameters and many sibling tools, more context on what the tool returns and how to use results 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?
Schema has 100% description coverage, so description adds little beyond stating config format similar to .judgesrc. Baseline score appropriate as schema already documents parameters.
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 (read and submit), resource (file from disk), and behavior (auto-detect language, all 45 judges). Distinguishes from siblings like evaluate_code or evaluate_batch through focus on file reading.
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 for evaluating a file from disk, but no explicit when-to-use or when-not-to-use guidance. No mention of alternatives like evaluate_code (for code strings) or evaluate_project.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must cover behavioral traits. It only states what the tool does, not side effects, permissions, or safety profile. Assumed read-only but not explicitly stated.
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 extraneous words. The purpose is front-loaded, and the structure is effective for quick understanding.
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 annotations or output schema, the description adequately covers what the tool returns and its filtering capability. However, missing details like pagination, ordering, or output format descriptions.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so parameters are already well-documented. The description adds minimal value by mentioning filtering by triage status, but does not enrich 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 tool lists triaged findings with optional filtering by status, and specifies it shows the triage decision, reason, and author. It distinguishes itself from sibling tools which are evaluation or analysis tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for retrieving triaged findings with optional filtering, but does not provide guidance on when to use vs alternatives or when not to use. No exclusions or context on prerequisites.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Discloses the three steps and mentions AI-fixable items, but with no annotations it fails to detail potential side effects (e.g., does it modify code?), auth needs, or rate limits; only partial 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?
Single sentence, front-loaded with key steps, no filler. Every word 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?
Given 9 parameters, no output schema, and no annotations, the 1-sentence description is insufficient for full understanding. Missing return format, prerequisites, and error handling; adequate but not complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so baseline is 3. Description adds minimal meaning beyond the schema, only hinting at workflow steps but not explaining how parameters like code/files/context map to those steps.
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?
Clearly specifies the tool runs a 3-step app-builder workflow (tribunal review, risk translation, remediation) with AI-fixable P0/P1 items, distinguishing it from single-step evaluation siblings like evaluate_code or evaluate_project.
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 usage for app-builder flows but provides no explicit guidance on when to choose this over alternatives like evaluate_code/evaluate_project, nor any exclusions or prerequisites.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden of behavioral disclosure. It reveals that all 45 judges run on each file and cross-file analysis occurs, but omits details on whether the tool is read-only, data retention, rate limits, or side effects. The provided information is helpful but incomplete for a tool handling multiple files.
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 extremely concise: two sentences that front-load the core action and outcome. Every word adds value; no redundant or verbose phrasing.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (multi-file input, 45 judges, cross-file analysis) and lack of output schema, the description adequately outlines the scope. However, it omits what the output looks like (e.g., a report or findings list) and does not mention how results are returned. This leaves gaps for an agent attempting to invoke the tool and process results.
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 extra meaning beyond the schema; it mentions 'multiple files' but does not elaborate on config, context, or other parameters. No additional value beyond what the schema already provides.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Submit multiple files for project-level analysis.' It specifies that all 45 judges evaluate each file and that cross-file architectural analysis detects issues like code duplication, inconsistent error handling, and dependency cycles. This contrasts with sibling tools that focus on single files or specific judges, making it easy to distinguish.
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 comprehensive project analysis but lacks explicit guidance on when to use this tool versus alternatives like evaluate_batch, evaluate_code, or evaluate_file. No 'when not to use' or prerequisite conditions are provided, leaving the agent to infer context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It mentions validation of ID and rule prefix uniqueness but does not disclose whether file creation is destructive, what happens on validation failure, or if it modifies index.ts directly. Some 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?
Two sentences. First sentence immediately states the tool's main function (generate boilerplate), second adds validation. No wasted words, front-loaded with key action.
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?
Description lacks details on return value, error handling, and the effect of the dryRun parameter. Given no output schema and no annotations, more context about the generation process and its outcomes would be beneficial.
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 schema already documents all 10 parameters. The description adds minimal value beyond summarizing the purpose and restating the uniqueness validation. 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?
Description clearly states it generates boilerplate files for a new judge, creates definition, evaluator skeleton, and provides the line to add to index.ts. The verb 'scaffold' + resource 'judge' is specific and distinguishes from siblings like scaffold_plugin.
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 adding a new judge but does not explicitly state when to use this tool versus alternatives like scaffold_plugin or when not to use it. No comparison with siblings 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?
With no annotations, the description carries full burden. It explains the tool runs checks and returns pass/fail with metrics, but does not disclose whether any state changes or side effects occur (e.g., writing logs or locking resources). The behavior appears read-only but is not confirmed.
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 covering purpose, return value, and use case. No fluff; every sentence is informative and front-loaded. Ideal for quick understanding.
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 (pass/fail with metrics) and use case (CI pipelines), which compensates for the lack of an output schema. It could mention how thresholds are used or default actions, but is largely sufficient.
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 each parameter having a description. The tool description adds little beyond listing return metrics; it does not explain how parameters interact or provide usage examples. Baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool runs a benchmark suite and checks results against thresholds, returning pass/fail with metrics. It distinguishes from 'run_benchmark' by emphasizing the gate functionality, though it does not explicitly differentiate from siblings.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly recommends use in CI pipelines to prevent quality regressions, providing clear context. However, it does not mention when not to use or compare to other tools like 'run_benchmark', leaving some ambiguity.
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 bears the full burden. It discloses that 45 judges independently review with automated and deep contextual analysis, and returns a combined verdict. However, it does not disclose potential costs, execution time, or side effects, which would be valuable for such a resource-heavy 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 three concise sentences, front-loaded with the main action. Each sentence adds value without redundancy, making it efficient and easy to digest.
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 7 parameters, 2 required, nested config, and no output schema, the description covers the tool's input, process, and output adequately. It mentions the verdict includes scores, findings, and guidance, but could be more explicit about the output structure. Overall, it provides enough context for the 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%, so the baseline is 3. The description adds minimal parameter-level insight beyond what the schema provides, such as listing code types but not explaining the config object or relatedFiles in more depth.
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 ('Submit code to the full Judges Panel for evaluation') and specifies the resource (code). It distinguishes from siblings by emphasizing 'full Judges Panel' and 'ALL code types', contrasting with tools like evaluate_code_single_judge or evaluate_diff.
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 full panel evaluation but does not explicitly state when to use this tool over alternatives such as evaluate_code_single_judge or evaluate_code_streaming. No exclusions or 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?
No annotations provided. Description only says 'List files and directories.' No mention of hidden files, sorting, permissions, or whether recursion is included beyond schema defaults. Minimal behavioral disclosure.
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 states action, second provides usage context. No filler 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?
Sufficient for a simple listing tool with full schema descriptions and a clear use case. No output schema needed. Missing details like output format, but overall 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 coverage is 100% with descriptions for both parameters (path and depth). Description adds no additional meaning beyond the 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?
Clear verb+resource: 'List files and directories'. States a specific use case: 'before evaluating code'. Distinguishes from siblings like read_file (reads content) and evaluate_file (evaluates).
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 context: 'Useful for exploring project structure before evaluating code.' Does not explicitly state when not to use or alternatives, but the sibling set implies when more specific tools are appropriate.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must cover behavioral traits. It mentions read operation and errors for large/missing files but does not explicitly state non-destructive nature or other side effects.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences with no fluff. The first sentence states the core action, the second specifies return value and error conditions. Highly concise 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?
The description covers core functionality and error conditions. While it does not describe return format or line numbering, the schema handles those details. Minor gaps in behavior disclosure.
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 additional meaning beyond 'Read the contents of a file'. Baseline score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses the specific verb 'Read' and resource 'file', clearly stating the tool's function. It distinguishes from sibling tools like 'list_files' which list directory contents.
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 file contents are needed but does not explicitly state when to use or alternatives. No exclusion criteria or context-sensitive guidance is provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It describes the tool's features like policy profiles, evidence calibration, and confidence scoring but does not disclose any side effects, permissions, or whether it is read-only. Given the lack of annotations, the description is adequate but not fully transparent.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise with two sentences: the first lists key features, the second provides usage guidance. It is front-loaded with essential information and contains no extraneous text.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool has 9 parameters, nested objects, and no output schema. The description covers the high-level purpose and usage but omits details about the output format, confidence scoring specifics, or uncertainty reporting structure. Given the complexity, the description is adequate but could be more comprehensive.
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 by explaining how parameters like evidence and policyProfile fit into the overall evaluation, but it does not significantly augment the meaning beyond what the schema already provides.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool performs policy-aware evaluation with specific named profiles, evidence calibration, and confidence scoring. It differentiates from siblings like evaluate_code by focusing on compliance and vertical-specific requirements.
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 'Use this when code must meet specific compliance or vertical requirements,' providing a clear directive. It implies alternatives for general evaluation but does not explicitly state when not to use, which would push it to 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the transparency burden. It mentions auto-fix support, returning patches, and the combined workflow, but lacks details on side effects (e.g., no file modification), error states, or prerequisites. It is acceptable 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise (two sentences), front-loaded with the primary functionality, and contains no unnecessary words. It earns its space efficiently.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity (6 parameters, nested config, no output schema), the description is minimal. It mentions output as 'verdict alongside patches' but does not detail the response format or how patches are applied. It is adequate for the tool's simplicity but could provide more guidance.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so all parameters are described in the input schema. The tool description adds no additional explanation beyond the schema. Baseline score is 3, and no extra value is provided.
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 evaluates code and generates fix patches for auto-fixable findings. It returns both a verdict and patches. This distinguishes it from sibling tools like `evaluate_code` (evaluation only) and `fix_code` (apply fixes), as it combines both steps.
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 advises to use this for a 'single-step review + fix workflow', providing clear context. However, it does not explicitly state when to use alternative tools (e.g., evaluate_code for pure evaluation), though this is implied by sibling tool names and the description.
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 behavioral burden. It discloses that the tool applies 'auto-tune and confidence filtering' and explains parameters like maxPromptChars (truncation control) and confidenceFilter (threshold for dropping findings). It does not mention side effects or destructive actions, but the behaviors described are accurate and useful.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences, with the first clearly stating the purpose and the second elaborating on the context. It is reasonably concise, though the second sentence is somewhat lengthy. It front-loads the key purpose, which aids agent comprehension.
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 does not explain the return value or output format, which would help the agent understand what to expect after invocation. Given the complexity (11 parameters, no output schema), additional detail on result structure would improve completeness. The description covers high-level behavior but leaves output unspecified.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so baseline is 3. The description adds context about the feedback loop but does not provide significant additional semantic detail beyond the parameter descriptions in the schema. Each parameter is adequately described in the schema, so the description's added value is marginal.
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: re-evaluating code with developer-provided context from a multi-turn conversation. It specifies the inputs (disputed findings, accepted findings, additional context) and positions it as an 'agentic feedback loop' for adjusting evaluations, distinguishing it from simpler evaluation tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage when the developer wants to provide contextual feedback from a conversation, mentioning disputedRuleIds, acceptedRuleIds, and developerContext. However, it does not explicitly state when to use this tool instead of alternatives like evaluate_code, leaving some ambiguity about the precise trigger for re-evaluation.
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 full behavioral disclosure. It details the output metrics (precision, recall, F1, etc.) but does not mention potential side effects (e.g., runtime, destructive actions) or authorization needs. Moderate 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?
Two sentences with no redundancy. First sentence states action and output details; second sentence states purpose. Extremely concise 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?
Given the tool's complexity (full benchmark suite) and no output schema, the description adequately covers output metrics and breakdowns. Missing details like expected runtime or prerequisites, but sufficient for understanding the tool's value.
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 additional parameter meaning, but the schema already clearly documents 'format' (enum) and optional 'judgeId' with allowed values.
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?
Clearly states verb 'Run' and resource 'full benchmark suite' with specific deliverables (dashboard with per-judge, per-category, per-difficulty breakdowns). Distinguishes from sibling tools like evaluate_code or evaluate_batch by emphasizing comprehensive scope.
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 advises using the tool to 'understand overall system quality and identify weak spots', providing clear use context. However, does not mention when not to use it or point to alternatives for more targeted evaluations.
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 responsibility. It discloses key behaviors: all 45 judges run sequentially, per-judge results stream as each completes, and running aggregates are provided. Missing details on error handling or auth, but the disclosed streaming behavior 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 concise sentences: the first defines the core functionality and output, the second reinforces the use case and sequential execution. No wasted words, front-loaded with essential 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?
Given no output schema and a moderately complex input, the description covers the streaming behavior well. It lacks explicit details about the expected response structure (beyond 'single structured response') or error scenarios, but the core functionality is clear and complete for the tool's purpose.
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 solid field descriptions. The tool description does not add new parameter-level insights beyond restating the streaming nature. Baseline of 3 is appropriate since the schema already documents the parameters sufficiently.
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 submits code for streaming evaluation, specifies it returns per-judge results with running aggregates, and contrasts with siblings like evaluate_code and evaluate_code_single_judge by emphasizing sequential execution and progressive feedback.
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 explicitly recommends the tool for long evaluations requiring progressive feedback, which provides a clear use case. While it does not mention exclusions or alternative tools by name, the sibling list implies differentiation and the context is sufficient for an agent to decide.
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?
The description discloses that all 45 judges run on the full file but findings are filtered to changed lines, explaining the underlying behavior. No annotations are provided, so this is valuable context. It does not mention performance implications, but the core pipeline is transparent.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two concise sentences with zero waste. It is front-loaded with the key purpose and immediately provides the unique filtering behavior.
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 7 parameters and a nested config object, the description explains the core mechanism (full analysis + filter) and mentions 45 judges. No output schema exists, but the description is still informative. A complete outline of the return value is missing but not critical.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so the schema already describes all parameters. The description adds no additional meaning beyond briefly referencing changed lines. Baseline 3 is appropriate as the description does not enhance parameter understanding.
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 'evaluate' and the resource 'changed lines in a code diff', distinguishing it from sibling tools like evaluate_code (which evaluates entire file) and evaluate_git_diff. It specifies the unique filtering behavior.
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 recommends the tool for 'PR reviews and incremental analysis,' providing clear context for use. It does not mention when not to use it or list alternative tools, but the 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?
No annotations are provided, but the description accurately conveys a read-only listing operation with no side effects. It does not mention pagination or ordering, but for a 0-parameter 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single concise sentence that efficiently conveys the tool's purpose and output. Every word adds value.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has no parameters and no output schema, the description adequately specifies what is returned (list of judges with expertise and evaluation criteria). However, it lacks details on the response structure or any limits, but for a simple list tool, this is sufficient.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 0 parameters, so the description does not need to add parameter details. The schema coverage is 100%, and the description reinforces that no parameters are required.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('List all available judges'), the specific resource ('Agent Tribunal panel'), and what information is included ('areas of expertise and what they evaluate'). It distinguishes from sibling tools which are all about evaluation actions, not listing judges.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies when to use this tool (when you need to see available judges), but no explicit guidance on when not to use it or alternatives. Siblings like 'evaluate_code' and 'evaluate_batch' serve different purposes, but no direct comparison is provided.
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?
Despite no annotations, the description reveals that feedback calibrates confidence scores during the current session. This is sufficient behavioral context for a simple feedback tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Two concise sentences cover purpose, use, and effect with no wasted 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 (2 params, no output schema), the description provides adequate context for an agent to use it correctly. Minor omission of return behavior is acceptable.
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 descriptions for both parameters. The description adds no additional parameter-specific meaning beyond restating verdict values, so baseline of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool records user feedback on a finding with specific verdicts (tp, fp, wontfix). It distinguishes from sibling tools like evaluate_* and fix_code, which serve different purposes.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explains when to use (to mark findings as tp, fp, or wontfix) and the effect (calibrates confidence scores). It lacks explicit when-not-to-use instructions, but the context is clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden for behavioral disclosure. The description states the tool analyzes files for risks, which implies a non-destructive read operation. However, it does not disclose whether the tool has side effects (e.g., saving reports), authentication requirements, rate limits, or the return format. While adequate, it lacks some behavioral context that would be helpful.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise, consisting of two sentences. The first sentence clearly states the purpose and scope, and the second sentence provides exclusion criteria and alternatives. No extraneous information, well front-loaded.
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 simplicity (2 parameters, no output schema, no nested objects) and the lack of annotations, the description is complete enough for correct selection and invocation. It specifies input format, allowed manifest types, and what not to use the tool for. The absence of output schema details is not a flaw as the tool likely produces a standard report.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 100% schema coverage, the schema already documents both parameters (manifest and manifestType). The description adds value by clarifying that 'manifest' should be the full content of the file and lists the exact enum values for manifestType, reinforcing the schema's meaning. This adds practical context beyond the schema alone.
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 analyzes package manager manifest files for supply-chain risks, version pinning issues, typosquatting indicators, and dependency hygiene. It explicitly lists accepted file types (package.json, requirements.txt, Cargo.toml, go.mod, pom.xml, .csproj) and distinguishes from sibling tools by specifying what not to use it for (infrastructure code like Bicep, Terraform, etc.).
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?
The description provides explicit usage guidance: it is specifically for manifest files and not for infrastructure/deployment configuration. It names alternative tools (evaluate_code or evaluate_code_single_judge) that should be used for excluded scenarios, giving clear when-to-use and when-not-to-use instructions.
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?
Although no annotations are provided, the description implies a read-only operation with 'Get' and explains the return content. It does not mention side effects, authorization, or rate limits, but for a status query, the behavioral traits are sufficiently clear.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise: two sentences, no unnecessary words. The first sentence defines what the tool does and returns, the second provides usage context. Every part earns its place.
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 has no parameters and no output schema, the description fully covers what an agent needs to know: what the tool returns and when to use it. It is complete for a simple status-checking 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?
The tool has no parameters, so the description does not need to elaborate on them. According to the guidelines, 0 parameters leads to a baseline score of 4.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Get the current evaluation session status' and lists specific information returned (evaluations run, frameworks, verdict history, stability indicators). This verb+resource combination is specific and distinguishes from sibling tools that focus on running evaluations or analyzing code.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description includes a usage hint: 'Useful for understanding what the tribunal has already reviewed.' This provides context for when to use the tool. However, it does not explicitly state when not to use it or mention any alternative tools for similar purposes.
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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