handy_prompt_lab

Server Quality Checklist

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Latest release: v0.2.0
Disambiguation5/5
Each tool has a clearly distinct purpose. Overlaps are minimal; descriptions effectively differentiate between similar operations like annotation workflow steps and evaluation functions.
Naming Consistency4/5
Most tools follow a consistent verb_noun pattern (e.g., get_development_failures, freeze_prompt_candidate). However, 'annotation_summary' and 'capability_report' are noun-only, creating minor inconsistency.
Tool Count5/5
19 tools appropriately cover the full prompt lab lifecycle: data import, annotation, candidate creation, evaluation, and deployment. The count feels well-scoped without being overwhelming or insufficient.
Completeness4/5
The toolset covers main workflows (sync, annotate, create, evaluate, promote). Minor gaps exist (no delete/update for candidates, no undo for promotion), but these are likely intentional and do not block core use.
Average 3.6/5 across 19 of 19 tools scored. Lowest: 2.2/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 2 commits in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI status not available
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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

Behavior1/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description describes 'editing and approving,' which conflicts with the annotation `readOnlyHint: true`. This contradiction undermines transparency. No additional behavioral traits (e.g., UI interaction model, auth needs) are disclosed beyond the annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence, which is concise. However, it omits necessary details, making it under-informative rather than efficiently packed. It is front-loaded but does not earn its place fully.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness1/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with 2 parameters, no output schema, and annotations that may conflict, the description is incomplete. It does not explain what the widget does, how it works, what the user can expect, or how parameters affect behavior.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, meaning the input schema provides no descriptions. The tool's description adds no meaning to parameters 'limit' or 'recordIds' (e.g., whether limit controls batch size, or how recordIds are used). This is insufficient for correct parameter usage.
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 'opens a widget for editing and approving a batch of Codex transcript proposals.' It specifies a resource (widget for transcript proposals) and action (opens), which is distinct from sibling tools that focus on individual records or summaries.
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 'load_annotation_record' or 'save_correction_proposals.' It lacks context or exclusions, leaving the agent to infer usage.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description aligns with the 'readOnlyHint' and 'destructiveHint' annotations, confirming a safe read operation. However, it adds no additional behavioral context beyond what annotations already provide, such as whether the record is transient or how widgets are managed.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence, which is concise and front-loaded. However, it sacrifices necessary detail for brevity, making it less helpful overall.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness1/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema, no parameter descriptions, and no usage guidance, the description fails to provide adequate context for an agent to effectively use this tool. It is too minimal for a tool with two undocumented parameters.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage, the description does not explain the meaning or constraints of the 'index' and 'batchId' parameters, leaving their semantics entirely to the schema (name, type, bounds). This adds no value beyond the schema itself.
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 explicitly states the action ('loads') and resource ('record from the current annotation widget batch'), making the purpose clear and distinct from sibling tools like 'get_development_failures' or 'save_annotation_and_next'.
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 versus alternatives, such as when to use 'load_annotation_record' instead of other annotation-related tools like 'begin_annotation_cycle' or 'show_annotation_lab'.
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?
Annotations show readOnlyHint=false, implying mutation, and destructiveHint=false. Description adds 'freezes' behavioral context but does not detail implications (e.g., irreversibility, state changes). No contradiction with annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence with no waste, but it is too terse to convey necessary information. It lacks structure and detail.
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 role in a pipeline and lack of output schema, the description should explain what 'freeze' means and any side effects. It fails to provide sufficient context for correct usage.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has one parameter (candidateId) with 0% description coverage. The description does not explain this parameter or its role, leaving the agent without guidance on what value to provide.
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 action (freeze) and the object (candidate) with context (before sealed holdout evaluation). It distinguishes from siblings like run_holdout_eval or promote_candidate_to_lm_studio, indicating a specific pipeline step.
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 vs. alternatives. It only says 'before sealed holdout evaluation' but does not explain prerequisites or conditions that would trigger this tool.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Description adds sequential behavior (save then return next) beyond the annotations (readOnlyHint=false, destructiveHint=false). However, it does not disclose potential side effects, error conditions, or what happens if no next record exists.
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?
One sentence that is front-loaded and conveys the core action efficiently. No wasted words, but could be expanded to include key details without losing conciseness.
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 tool with five parameters, 20% of which are enums, and no output schema, the description is insufficient. It omits parameter explanations, error handling, and return value details, making it inadequate for reliable invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description provides no explanation of the five parameters (batchId, recordId, correctedText, status, notes) despite 0% schema description coverage. It does not clarify the meaning of the enum values or required fields.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states a two-step action: save a user-reviewed transcript target and return the next record. This combines save and navigation, distinguishing it from siblings like 'save_correction_proposals' or 'load_annotation_record'.
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 in a batch annotation workflow by mentioning 'widget batch' and returning the next record, but it does not explicitly state when to use this tool versus alternatives or when not to use it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and destructiveHint=false. The description adds no behavioral details beyond what is expected from a reporting tool, such as whether the report includes all capabilities or only a subset.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
A single, front-loaded sentence conveys the purpose effectively. Every word earns its place, with no redundancy or unnecessary detail.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a zero-parameter reporting tool, the description is largely complete. However, lacking an output schema, it could hint at the structure of the reported capabilities. Still, it adequately covers the tool's function.
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 schema coverage is 100%. Per guidelines, baseline score is 4. The description does not need to add parameter meaning.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool reports client information and capabilities. The verb 'Reports' and resource 'client information and capabilities' are specific. While it doesn't differentiate from siblings, the tool name and context make its purpose distinct.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives. The description gives no context about prerequisites or typical use cases, leaving an agent without direction.
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?
Annotations indicate non-read-only and non-destructive. The description adds the constraint that prompt must contain '${output}', which is useful. However, it does not disclose versioning behavior, whether duplicates are allowed, or any error states. No contradictions.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Two concise sentences front-load the purpose and critical constraint. Every word contributes value. However, the structure could be improved by separating the constraint into a note.
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 4 parameters, a nested object, and no output schema, the description is adequate but incomplete. It lacks validation rules, behavior on duplicate 'name', return value, or prerequisites. The constraint on prompt is helpful, but more detail on settings defaults and allowed values is needed.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, so description must compensate. It mentions 'prompt template, model, and inference settings' but does not explain individual properties like 'name', 'settings.minP', or their roles. Only the prompt constraint is clarified.
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 stores an immutable prompt candidate with specific components (template, model, settings) for evaluation. It distinguishes from siblings like 'freeze_prompt_candidate' and 'promote_candidate_to_lm_studio' by focusing on the creation/storage step.
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 mentions 'for evaluation' but provides no explicit guidance on when to use this tool versus alternatives like 'freeze_prompt_candidate' or 'run_development_eval'. It lacks when-not-to-use or exclusion criteria.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate readOnlyHint and destructiveHint. Description adds that it returns only approved pairs and excludes holdouts, which is useful but minimal beyond annotations.
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 are front-loaded and contain no fluff. Every word is meaningful.
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?
Core purpose and key constraint are covered, but details on output format, pagination behavior, and the meaning of 'approved' are missing. Adequate for a simple read tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, yet description does not explain any parameters (limit, offset, datasetId). No meaning is added beyond what the schema provides.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it returns approved raw/target pairs from the development split, distinguishing it from sibling tools like get_development_failures. The mention of holdout records never being returned adds specificity.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage 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 get_development_failures or other siblings. The description does not include explicit context for selection.
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?
Annotations already declare readOnlyHint=true, so the description adds the important behavioral constraint that holdout evaluation details are never exposed. This goes beyond the annotations without contradicting them.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Two concise sentences, front-loaded with the core purpose. Every word earns its place; no wasted 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?
Given the tool's simplicity (2 parameters, no output schema), the description is partially complete. It covers purpose and a key behavioral constraint, but fails to describe the parameters, which is a notable gap for proper usage.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate. However, it does not explain the runId parameter or the limit parameter beyond what the schema already provides (type, default, constraints). No additional semantic meaning is given.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it returns detailed failed examples for a development evaluation, distinguishing it from a hypothetical sibling like get_development_examples. The mention that holdout evaluation details are never exposed adds specificity.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage when failed examples are needed, but does not explicitly state when not to use or list alternatives. The sibling names suggest get_development_examples is for successful examples, but the description itself lacks such guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description discloses that the tool 'locks annotation access,' which is a behavioral effect beyond what annotations provide (readOnlyHint=false, destructiveHint=false). However, it does not elaborate on the nature of the lock (e.g., is it reversible? who is affected?) or other side effects, so transparency is adequate but not thorough.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description consists of two clear, concise sentences. The first sentence states the tool's action, and the second provides a usage condition. There is no unnecessary information, and the key points are 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?
Given the tool's complexity (2 parameters, no output schema, and annotations present) and the sibling context, the description covers the essential purpose and a usage constraint. However, it omits details about parameter semantics, the meaning of 'locks annotation access,' and any side effects or outputs, making it only minimally complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description adds no information about the two parameters (holdoutFraction and freshTaskConfirmation). With schema description coverage at 0%, the description fails to explain the meaning, defaults, or constraints of holdoutFraction or the required confirmation string, leaving the agent without guidance on how to invoke the tool correctly.
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 creates a chronological development/holdout split and locks annotation access. This is a specific verb+resource combination that distinguishes it from sibling tools like get_development_failures or freeze_prompt_candidate, which have 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 explicitly states 'Call only from a fresh Codex task that has not seen transcript proposals,' providing a clear condition for proper usage. However, it does not mention alternatives or when not to use it, only that it must be called from a fresh task.
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?
Annotations indicate readOnlyHint=false and destructiveHint=false. The description adds behavioral context beyond annotations by explaining that stored proposals are held for review and not automatically added to the evaluation set, clarifying side effects.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two short sentences with no extraneous content. It front-loads the primary purpose and adds a single secondary sentence for behavioral context, achieving maximum conciseness.
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 adequately explains the tool's purpose and interim state of proposals, but it does not mention return values (e.g., success indication, stored proposal IDs). For a simple tool with no output schema, this leaves the agent uncertain about the result of the call.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate for parameter meaning. However, it contains no information about the 'author' or 'proposals' parameters, their defaults, constraints (maxItems, minItems), or the structure of proposal objects.
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 ('Stores') and resource ('Codex-generated proposals'), clearly distinguishing this tool from siblings like save_annotation_and_next. The additional context about proposals not entering the evaluation set until user approval further clarifies its unique role.
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 after Codex generates proposals and for storage before user review, but does not explicitly state when to use this tool versus alternatives or provide exclusion criteria. No sibling comparisons are made.
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 annotations (readOnlyHint=false, destructiveHint=false) are minimal, so the description carries the burden of behavioral disclosure. It adds important context: that the evaluation is sequential, metrics are recorded, and LM Studio's prior model state is restored afterward. This informs the agent about side effects and cleanup, going beyond annotations.
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, consisting of two sentences. The first sentence efficiently states purpose and captured metrics; the second adds a critical behavioral note. No unnecessary words, making it easy to parse.
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 simplicity (2 parameters, no output schema), the description covers the core action and a behavioral note. However, it does not describe the return value or output format, which is important since there is no output schema. It also fails to differentiate between required and optional parameters.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0% and the description does not explain the parameters (datasetId, candidateId). The description only mentions evaluating a candidate but does not clarify that candidateId is required or what datasetId affects. This lack of parameter documentation creates a significant gap for the agent.
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 a prompt candidate against development examples and records specific metrics (exact-match and latency). The verb 'evaluates' and the resource 'candidate against development examples' are explicit. It is easily distinguished from the sibling tool 'run_holdout_eval' which evaluates on a holdout set.
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 evaluating on a development set and notes that prior model state is restored afterward, which hints at a non-permanent operation. However, it does not explicitly state when to use this tool over alternatives like 'run_holdout_eval' or provide context on prerequisites or conditions for use.
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?
Description adds value beyond annotations by noting that only aggregate metrics are returned. It does not contradict annotations and provides useful context about the tool's output nature.
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 adds value, making the description efficient and easy to parse.
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?
Provides essential information about return type but lacks context on prerequisites (e.g., candidate must be frozen) and what 'sealed holdout' entails. Adequate for a simple tool but could be more complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, and the tool description does not explain the two parameters (datasetId and candidateId). 'Frozen candidate' implies candidateId is required, but datasetId remains undocumented.
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 evaluates a frozen candidate on the sealed holdout, using a specific verb and resource. It distinguishes itself from siblings like 'run_development_eval' by focusing on holdout evaluation.
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 when-to-use or when-not-to-use guidance is provided. The description implies usage after freezing a candidate, but does not mention alternatives or exclusions.
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 adds value beyond the annotations by clarifying that the tool reads from Handy's local history database without modifying it and imports transcripts into the prompt lab. Annotations only provide readOnlyHint:false, destructiveHint:false, so the description fills in the behavioral intent. However, it could mention if the import is incremental or full.
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 of 18 words, front-loaded with the action and resource. Every word adds value, and it is efficient without 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?
The description covers the basic action but does not explain what happens after import (e.g., return value, errors, or triggering of downstream processes). For a tool with no parameters and no output schema, it is adequate but could be more complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
There are no parameters, so the schema coverage is 100% trivially. The description adds no parameter information, but the baseline for zero parameters is 4. The description does not need to explain parameter meanings.
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 reads a local history database without modifying it and imports new raw transcripts into the prompt lab. The verb 'Reads' and 'imports' specify the action and resource, and it distinguishes from sibling tools which are focused on development, evaluation, annotation, and promotion.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It does not mention any preconditions, when not to use it, or compare with sibling tools such as list_transcripts_needing_proposals or other data import tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and destructiveHint=false. The description adds specific context about the categories of counts returned, which is helpful. No contradictions, and no mentions of rate limits or autflow, which is acceptable for a simple read-only tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence of 11 words, front-loaded with the verb, and contains no unnecessary words. It is maximally concise while conveying the core purpose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a no-parameter tool with a straightforward output, the description is complete enough. It lists the specific counts returned (imported, proposed, etc.). While it does not detail the output format, this is acceptable given the tool's simplicity and the presence of read-only 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?
No parameters exist; schema coverage is 100%. According to guidelines, with high schema coverage, baseline is 3. The description does not need to add parameter info, and it implies the tool operates on all data without filters, which is clear.
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 explicitly states it returns counts for specific transcript statuses (imported, proposed, etc.). The verb 'Returns' and the resource are clear, and it distinguishes from sibling tools that perform editing, promotion, or evaluation.
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 checking annotation progress but does not explicitly state when to use this tool versus alternatives. No exclusions or when-not-to-use guidance is provided, though the context of sibling tools suggests this is for summary queries.
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?
Description aligns with annotations (readOnlyHint=true) by stating returns a batch; adds context about boundedness and expected follow-up actions.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Two concise sentences, front-loaded with purpose, 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?
Sufficient for a simple list tool with pagination; instructions on next steps compensate for lack of output schema.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
No parameter descriptions in schema or description; limit and cursor are self-explanatory from schema constraints, but description adds no 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?
Description clearly states the tool returns a batch of raw transcripts needing proposals, with explicit instruction to generate corrections and call save_correction_proposals, distinguishing 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?
Explicitly states the action to take with results and references sibling tool save_correction_proposals, but lacks when-not-to-use guidance.
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?
Annotations already declare readOnlyHint=true, openWorldHint=false, destructiveHint=false. The description adds that it 'Shows the exact... fields that would be changed' and 'Makes no changes,' which aligns with and reinforces the read-only nature. No contradictions. It adds context about what is displayed.
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 consists of two concise, front-loaded sentences. The first states the action, the second clarifies no changes are made. Every sentence adds value without redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the simple tool (one parameter, no output schema) and the presence of annotations clarifying safety, the description is largely complete. It explains what is shown (the defaults file and changed fields) and that no changes are made. It does not describe the return format, but for a preview tool, the context is sufficient.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With schema description coverage at 0%, the description carries the burden of explaining the parameter. It mentions 'candidate' in the description but does not explicitly define 'candidateId' or its format. The parameter is simple (single required string), but the description adds minimal meaning beyond the parameter name.
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 shows the exact LM Studio per-model defaults file and fields that would be changed for a candidate, and that it makes no changes. This specifies the verb (shows) and resource (defaults file and fields) and distinguishes it from the sibling tool 'promote_candidate_to_lm_studio' which actually performs the promotion.
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?
By stating 'Makes no changes,' the description implies this is a preview tool to be used before applying changes. Among sibling tools, 'promote_candidate_to_lm_studio' is the counterpart for actual changes, providing context for when to use each. However, the description does not explicitly state 'when to use' or 'when not to use.'
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?
Annotations indicate it is not read-only and not destructive. The description adds that it performs a backup before updating and that 'Handy remains read-only,' providing useful behavioral context beyond what annotations offer.
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 waste. The first sentence delivers the core action and conditions; the second adds a relevant constraint. Information is front-loaded and concise.
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 single-parameter tool with no output schema, the description covers prerequisites and side effects. It could be improved by clarifying backup location or return values, but overall it provides adequate context for the tool's use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description bears full burden. It does not explicitly describe the candidateId parameter's meaning or format, only indirectly referring to a 'frozen candidate.' This is insufficient for clarity.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'backs up and updates' and the resource 'LM Studio's per-model defaults' with specific conditions ('frozen candidate that completed holdout evaluation'). This distinguishes it from siblings like 'freeze_prompt_candidate' and 'plan_lm_studio_promotion'.
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 the tool should be used 'after form confirmation' and only for 'frozen candidate that completed holdout evaluation', but does not explicitly name alternative tools or state when not to use it. It provides clear prerequisites but lacks explicit exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide readOnlyHint=true and destructiveHint=false. The description reinforces that it does not download or change models, adding some context but not disclosing additional behavioral traits (e.g., performance, rate limits).
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
A single, front-loaded sentence that communicates the tool's purpose and boundaries without any redundant or extraneous 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 parameters and annotations covering safety, the description is mostly complete. It could optionally describe the output format, but for a simple list tool it is adequate.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
There are no parameters, and schema coverage is vacuously 100%. The description does not need to explain parameters; it meets the baseline for zero 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?
The description uses the verb 'lists' and specifies the resources ('downloaded local models and loaded instances from LM Studio'), clearly distinguishing it from sibling tools by explicitly stating what it does not do ('does not download or change models').
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 by stating its purpose and explicitly excluding other actions. Although it does not name alternative tools, the negative statement helps the agent understand when not to use this tool.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations are minimal (readOnlyHint=false, destructiveHint=false). The description adds the behavioral detail that this tool 'unlocks' access after explicit confirmation, which is a useful side effect beyond static annotations. No contradiction detected.
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 no filler. Front-loaded: the first sentence states the core action, and the second gives usage context. 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 no parameters and no output schema, the description covers the essential purpose and usage. It could mention expected outcomes (e.g., 'returns success') but is largely complete for a simple initialization 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 input schema has no parameters (0 params, 100% coverage). Per calibration guidelines, 0 params baseline is 4. The description does not need to add parameter info, and it correctly omits any.
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 'unlocks annotation access for newly collected Handy transcripts after explicit confirmation', which is a specific verb-resource-action. The title 'Begin a new annotation cycle' reinforces its role as the initial step, distinguishing it from sibling tools like 'save_annotation_and_next' or 'annotation_summary'.
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 advises 'Use from a fresh annotation task', providing clear context for when to invoke it. However, it does not explicitly state when not to use it or mention alternatives, though the sibling tools imply other stages.
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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