Prompt Auto-Optimizer MCP

Overview Schema Related Servers Score Discussions

Server Quality Checklist

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Latest release: v1.0.0
Disambiguation4/5
Most tools have distinct purposes, but some overlap exists: gepa_evaluate_prompt and gepa_record_trajectory both relate to prompt evaluation, and gepa_select_optimal and gepa_get_pareto_frontier both involve selecting optimal candidates. The descriptions help clarify differences, but an agent might occasionally confuse these pairs.
Naming Consistency5/5
All tool names follow a consistent 'gepa_verb_noun' pattern with snake_case, using clear verbs like create, evaluate, get, list, record, recover, restore, select, and start. This predictability makes it easy for agents to understand and use the toolset.
Tool Count5/5
With 12 tools, the server is well-scoped for prompt auto-optimization, covering key areas like evaluation, evolution, backup, recovery, and selection. Each tool has a clear role, and the count is neither too sparse nor overwhelming for the domain.
Completeness4/5
The toolset provides comprehensive coverage for prompt optimization workflows, including evolution (start_evolution), evaluation (evaluate_prompt, record_trajectory), selection (select_optimal, get_pareto_frontier), reflection (reflect), and disaster recovery (backup, restore, integrity_check, recovery). A minor gap is the lack of tools for modifying or deleting components, but core operations are well-covered.
Average 2.8/5 across 12 of 12 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 0 commits in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
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This repository is licensed under MIT License.
This repository includes a README.md file.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
Tip: use the "Try in Browser" feature on the server page to seed initial usage.
Add a glama.json file to provide metadata about your server.
If you are the author, simply .
If the server belongs to an organization, first add glama.json to the root of your repository:
```
{
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}
```
Then . Browse examples.
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To manually sync the server, click the "Sync Server" button in the MCP server admin interface.

How is the quality score calculated?

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

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

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

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

Tool Scores

Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions 'retrieve' which implies a read operation, but doesn't specify whether this is a safe operation, if it requires specific permissions, what happens on failure, or any rate limits. The description is too brief to adequately cover behavioral traits for a tool with no annotation support.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence with no wasted words, making it appropriately concise. However, it could be more front-loaded with critical details, but given its brevity, it scores well for structure.
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 (involving Pareto frontiers and candidate selection), no annotations, and no output schema, the description is incomplete. It doesn't explain what 'optimal candidates' are, how they're determined, what the return format looks like, or any error conditions. This leaves significant gaps for an AI agent to understand the tool fully.
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 parameters (minPerformance, taskFilter, limit) with their types and optional/default status. The description adds no additional meaning beyond what's in the schema, such as explaining how 'optimal candidates' relate to these parameters or providing examples. Baseline 3 is appropriate when the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Retrieve optimal candidates from Pareto frontier' states a clear verb ('Retrieve') and resource ('optimal candidates from Pareto frontier'), but it's somewhat vague about what 'optimal candidates' specifically means in this context. It doesn't distinguish this tool from sibling tools like 'gepa_select_optimal', which might 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 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 'gepa_select_optimal' or other sibling tools. It lacks context about prerequisites, when this tool is appropriate, or any exclusions, leaving the agent with minimal usage direction.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden but only states the action without behavioral details. It doesn't disclose if this is read-only or destructive, what permissions are needed, how long it takes, if it's idempotent, or what happens on failure. The mention of 'comprehensive' is vague and adds minimal context beyond the basic purpose.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence with no wasted words. It's front-loaded with the core action, though it could be more structured by including key details upfront. It earns its place but lacks depth that might require more elaboration.
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 no annotations, no output schema, and a mutation-like tool (integrity check with repair option), the description is incomplete. It doesn't explain return values, error handling, or the system context (e.g., GEPA evolution system). For a tool with potential side effects (autoRepair), more detail is needed to guide safe usage.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema fully documents both parameters (component and autoRepair). The description adds no additional meaning beyond what's in the schema, such as explaining the impact of 'autoRepair' or what 'comprehensive' entails. Baseline 3 is appropriate as the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Perform comprehensive data integrity check' states a clear verb ('perform') and resource ('data integrity check'), but it's vague about scope and doesn't differentiate from siblings like 'gepa_recover_component' or 'gepa_recovery_status'. It lacks specificity about what 'comprehensive' entails or what system it applies to.
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 is provided. It doesn't mention prerequisites, timing (e.g., after errors or periodically), or how it relates to siblings like 'gepa_recover_component' for repairs or 'gepa_recovery_status' for monitoring. Usage is implied but not explicitly stated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It states the tool selects based on 'best prompt candidate' but doesn't explain how selection works, what 'best' means, whether it's read-only or mutative, or any performance characteristics like rate limits. This is a significant gap for a tool with zero annotation coverage.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that directly states the purpose without unnecessary words. It's appropriately sized and front-loaded, though it could be slightly more informative 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?
Given the tool's complexity (involving selection criteria with weights), no annotations, and no output schema, the description is incomplete. It doesn't explain the selection algorithm, return values, or how weights affect outcomes, leaving critical gaps for the agent to understand the tool's behavior and 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?
The schema description coverage is 100%, so the schema already documents all three parameters (taskContext, performanceWeight, diversityWeight) with descriptions. The tool description adds no additional parameter semantics beyond what's in the schema, such as explaining how weights interact or what 'context' entails. Baseline 3 is appropriate when schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Select best prompt candidate for given context' states a clear purpose with a specific verb ('select') and resource ('prompt candidate'), but it doesn't differentiate from sibling tools like 'gepa_evaluate_prompt' or 'gepa_get_pareto_frontier' that might involve similar prompt-related operations. The purpose is understandable but lacks sibling distinction.
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 doesn't mention prerequisites, exclusions, or compare to siblings such as 'gepa_evaluate_prompt' or 'gepa_get_pareto_frontier', leaving the agent with no usage context beyond the basic purpose.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions 'recover' but doesn't specify whether this is a destructive operation, what permissions are needed, potential side effects, or expected outcomes. This leaves critical behavioral traits unclear for a tool that likely involves system changes.
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, direct sentence with no wasted words. It is appropriately sized and front-loaded, efficiently stating the tool's core function without unnecessary elaboration.
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 complexity of a recovery operation with no annotations or output schema, the description is incomplete. It fails to address behavioral aspects like safety, permissions, or result format, leaving gaps in understanding how to use the tool effectively in context with its siblings.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, with clear enums and defaults for both parameters. The description adds no additional meaning beyond the schema, such as explaining the implications of different component types or strategies. Since schema coverage is high, the baseline score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description states the action ('recover') and target ('a specific GEPA component'), which provides a basic purpose. However, it lacks specificity about what 'recover' entails and doesn't differentiate from sibling tools like 'gepa_recovery_status' or 'gepa_restore_backup', making it somewhat vague.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives such as 'gepa_recovery_status' for checking status or 'gepa_restore_backup' for backup restoration. The description implies usage for recovery but offers no context on prerequisites, timing, or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It states the tool creates a backup and what it includes, but lacks critical behavioral details: whether this is a destructive operation (e.g., overwrites existing backups), requires specific permissions, has rate limits, or what the output looks like (e.g., backup ID, status). For a mutation tool with zero annotation coverage, this is inadequate.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence with zero waste. It front-loads the core action and includes key details (what gets backed up) without unnecessary elaboration. Every word earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given this is a mutation tool (creates backups) with no annotations and no output schema, the description is incomplete. It doesn't explain the return value (e.g., backup identifier, success status), error conditions, or behavioral constraints. For a tool that modifies system state, more context is needed to ensure safe and correct usage.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents both parameters ('label' and 'includeTrajectories') with descriptions and defaults. The description adds no additional parameter semantics beyond implying trajectories are included by default, which is already in the schema. Baseline 3 is appropriate as the schema does the heavy lifting.
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 ('Create') and resource ('system backup'), specifying what gets included ('evolution state and trajectories'). It distinguishes from sibling tools like 'gepa_list_backups' (list vs. create) and 'gepa_restore_backup' (create vs. restore), though it doesn't explicitly name these alternatives. The purpose is specific but could be more differentiated.
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. It doesn't mention prerequisites, timing (e.g., before system changes), or exclusions (e.g., not for incremental backups). The context is implied as backup creation, but explicit usage scenarios are missing.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions evaluating performance but fails to detail critical aspects like whether this is a read-only analysis or a mutating operation, potential side effects (e.g., logging results), performance implications (e.g., time-intensive due to multiple tasks), or error handling. This leaves significant gaps in understanding the tool's behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that directly states the tool's purpose without unnecessary words. It's front-loaded with the core action, making it easy to grasp quickly. However, it could be slightly more structured by hinting at key parameters or outcomes to enhance clarity further.
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 complexity of evaluating prompts across tasks, the description is incomplete. With no annotations and no output schema, it fails to explain what the evaluation entails (e.g., metrics used, return format like scores or reports), or how it integrates with sibling tools. This lack of context makes it inadequate for understanding the tool's full scope and usage.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 100%, meaning all parameters are documented in the input schema. The description adds no additional meaning beyond the schema, such as explaining how 'promptId' relates to other tools or what 'taskIds' represent in context. Since the schema handles the heavy lifting, a baseline score of 3 is appropriate, but the description doesn't compensate with extra insights.
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 ('evaluate') and the target ('prompt candidate performance across multiple tasks'), making the purpose understandable. However, it doesn't differentiate this tool from its siblings like 'gepa_select_optimal' or 'gepa_reflect', which might also involve evaluation or selection processes, leaving room for confusion about 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 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, such as 'gepa_select_optimal' for choosing prompts or 'gepa_reflect' for analysis. It lacks context on prerequisites, like needing existing prompts and tasks, or exclusions, such as not being for single-task evaluation, which limits its utility in decision-making.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden but reveals little. 'Record' implies a write operation, but it doesn't disclose if this is idempotent, requires specific permissions, has rate limits, or what happens on failure. The description lacks behavioral context like whether it overwrites existing trajectories or appends to 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?
The description is a single, efficient sentence with zero waste. It's front-loaded with the core purpose and appropriately sized for the tool's complexity.
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 5 parameters (including nested objects), no annotations, and no output schema, the description is inadequate. It doesn't explain the return value, error conditions, or how the recorded data is used. Given the complexity and lack of structured fields, more context is needed for an agent to use this effectively.
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 fully documents all 5 parameters. The description adds no parameter-specific semantics beyond implying that parameters relate to 'execution trajectory' and 'prompt evaluation'. This meets the baseline of 3 when schema does the heavy lifting.
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 'Record execution trajectory for prompt evaluation' clearly states the verb ('Record') and resource ('execution trajectory'), and specifies the context ('for prompt evaluation'). It distinguishes from siblings like gepa_evaluate_prompt (which likely evaluates rather than records) and gepa_reflect (which might analyze rather than record). However, it doesn't explicitly differentiate from all siblings (e.g., gepa_create_backup might also involve recording).
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 doesn't mention prerequisites (e.g., after gepa_evaluate_prompt), exclusions, or relationships to siblings like gepa_reflect or gepa_select_optimal. The agent must infer usage from the name and context alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. While it implies a read-only operation ('Get'), it lacks details on permissions, rate limits, response format, or whether it's safe to invoke frequently. For a tool that likely interacts with critical disaster recovery systems, this is a significant gap in transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that front-loads the core purpose without unnecessary words. Every part of the sentence ('Get comprehensive disaster recovery status and health information') contributes directly to understanding the tool's function, making it highly concise and well-structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the lack of annotations and output schema, the description is incomplete for a tool that likely returns complex disaster recovery data. It doesn't explain what 'comprehensive' entails, what health information is included, or how the response is structured. For a tool in a critical domain with no structured output guidance, this leaves too many unknowns for effective use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, with the single parameter 'includeMetrics' clearly documented in the schema. The description adds no additional parameter information beyond what the schema provides, so it meets the baseline score of 3 without compensating or detracting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with a specific verb ('Get') and resource ('disaster recovery status and health information'), making it immediately understandable. However, it doesn't explicitly distinguish this tool from potential sibling tools like 'gepa_list_backups' or 'gepa_integrity_check' that might also provide status-related information, preventing a perfect score.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. With multiple sibling tools related to backups, recovery, and status checks (e.g., 'gepa_list_backups', 'gepa_integrity_check'), there is no indication of when this comprehensive status check is preferred over more specific tools, leaving the agent without usage context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool analyzes failures and generates improvements, implying a read-and-write operation, but doesn't specify whether it modifies data, requires specific permissions, has rate limits, or what the output format looks like. For a tool with 4 parameters and no annotations, this leaves significant behavioral gaps, such as whether it's destructive or how results are returned.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise with just one sentence ('Analyze failures and generate prompt improvements'), which is front-loaded and wastes no words. Every part of the sentence directly contributes to understanding the tool's purpose, making it efficient and well-structured for quick comprehension.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (4 parameters, no annotations, no output schema), the description is incomplete. It lacks details on behavioral traits, output format, and usage context, which are critical for an agent to invoke it correctly. While the schema covers parameters well, the description doesn't add enough value to compensate for missing annotations and output schema, leaving gaps in overall understanding.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents all parameters thoroughly. The description adds no additional meaning beyond what's in the schema, such as explaining how 'trajectoryIds' relate to failures or what 'focusAreas' might include. With high schema coverage, the baseline is 3, as the description doesn't compensate but also doesn't detract from the well-documented parameters.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with specific verbs ('analyze failures' and 'generate prompt improvements') and identifies the resource (trajectories and prompts). It distinguishes from siblings like gepa_evaluate_prompt (which likely evaluates rather than analyzes failures) and gepa_record_trajectory (which records rather than analyzes), though not explicitly. However, it doesn't fully differentiate from all siblings like gepa_select_optimal or gepa_start_evolution, which may involve similar improvement processes.
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 doesn't mention prerequisites (e.g., needing existing trajectories or prompts), exclusions, or compare to siblings like gepa_evaluate_prompt for evaluation versus improvement. Usage is implied only by the purpose, with no explicit context or decision criteria provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It states the action but fails to mention critical traits: that this is a destructive operation (overwrites current system state), may require high permissions, could cause downtime, or has no output schema for status tracking. This leaves significant gaps for safe agent use.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence with zero wasted words. It front-loads the core action ('Restore system') and specifies the resource ('from a specific backup'), making it easy to parse quickly.
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 complexity of a system restoration tool with no annotations and no output schema, the description is inadequate. It doesn't address safety implications, error conditions, or what happens post-restoration (e.g., system reboot, status checks via 'gepa_recovery_status'). For a high-stakes operation, more context is needed.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so parameters are fully documented in the schema itself. The description adds no additional meaning beyond implying 'backupId' is needed, which is already covered. This meets the baseline for high schema coverage without compensating value.
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 verb ('restore') and resource ('system from a specific backup'), making the purpose immediately understandable. It doesn't explicitly differentiate from siblings like 'gepa_recover_component' or 'gepa_list_backups', but the core action is unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives like 'gepa_recover_component' or 'gepa_create_backup'. The description lacks context about prerequisites, such as needing a backup ID from 'gepa_list_backups', or warnings about system impact during restoration.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions 'Initialize evolution process' but fails to explain what the process does (e.g., genetic algorithm steps), what outputs or side effects to expect (e.g., creates populations, runs generations), or any constraints like rate limits or permissions needed. This leaves significant gaps for a tool that likely involves complex operations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that front-loads the core action ('Initialize evolution process') and key inputs. There is no wasted verbiage or redundancy, making it easy to parse quickly while covering essential elements.
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 complexity implied by 'evolution process' with 4 parameters (including nested config), no annotations, and no output schema, the description is inadequate. It doesn't explain what the tool returns (e.g., evolution ID, status), how it behaves over time, or error conditions, leaving too much undefined for effective agent use.
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 fully documents all parameters. The description adds minimal value by naming 'configuration and seed prompt' as inputs, but it doesn't provide additional context beyond what's in the schema, such as explaining how 'targetModules' relate to evolution or typical values for config parameters. Baseline 3 is appropriate as the schema does the heavy lifting.
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 ('Initialize evolution process') and the key inputs ('configuration and seed prompt'), which distinguishes it from siblings like gepa_evaluate_prompt or gepa_select_optimal that focus on different stages of evolution. However, it doesn't specify what 'evolution' entails beyond optimization, leaving some ambiguity about the exact process.
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 gepa_evaluate_prompt or gepa_select_optimal, nor does it mention prerequisites such as needing a task description. It implies usage for starting evolution but lacks context about timing or dependencies in the workflow.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states it's a list operation, implying read-only behavior, but doesn't cover aspects like rate limits, authentication needs, pagination, or what 'available' means in terms of status or accessibility. For a tool with no annotation coverage, this leaves significant gaps.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that directly states the tool's purpose without any unnecessary words. It's appropriately sized and front-loaded, making it easy for an agent to parse quickly.
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 low complexity (2 optional parameters, no output schema), the description is minimally complete for a list operation. However, with no annotations and no output schema, it lacks details on behavioral traits and return values, making it adequate but with clear gaps for an agent to rely on.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 100%, with clear descriptions for both parameters ('limit' and 'filterLabel') in the input schema. The description adds no additional parameter semantics beyond what the schema provides, so it meets the baseline score of 3 for adequate coverage without extra value.
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 'List available system backups' clearly states the verb ('List') and resource ('available system backups'), making the tool's purpose immediately understandable. However, it doesn't differentiate from sibling tools like 'gepa_recovery_status' or 'gepa_restore_backup' which might also relate to backups, so it doesn't reach the highest score.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention sibling tools like 'gepa_recovery_status' or 'gepa_restore_backup', nor does it specify prerequisites or contexts for usage, leaving the agent with minimal direction.
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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