MCP Pyrefly

by kimasplund

Overview Schema Related Servers Score Discussions

Python

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Server Quality Checklist

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Latest release: v0.2.0
Disambiguation3/5
The tools have some clear distinctions (e.g., check_code vs. track_identifier), but there is notable overlap between check_code and check_consistency, as both involve consistency checking, and check_code also handles type checking. Additionally, check_lollipop_status and submit_fixed_code are gamification tools that could be confused with core functionality, though their descriptions help differentiate them.
Naming Consistency4/5
Most tools follow a consistent verb_noun pattern (e.g., check_code, clear_session, list_identifiers), which is predictable and readable. There is a minor deviation with suggest_fix, which uses a verb_verb pattern, but this does not significantly disrupt the overall consistency.
Tool Count5/5
With 9 tools, the count is well-scoped for a Python code analysis and gamification server. Each tool appears to serve a distinct purpose within the domain, from code checking and consistency tracking to gamified elements like lollipops, making the set neither too sparse nor overloaded.
Completeness4/5
The tool set covers core aspects of Python code analysis, including error checking, consistency tracking, and session management, with gamification elements for engagement. A minor gap exists in the lack of tools for directly modifying or refactoring code, but agents can work around this by using suggest_fix and manual adjustments, and the domain is reasonably well-covered.
Average 2.9/5 across 9 of 9 tools scored. Lowest: 1.6/5.
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
- CI status not available
This repository is licensed under MIT License.
This repository includes a README.md file.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
Tip: use the "Try in Browser" feature on the server page to seed initial usage.
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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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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?
No annotations are provided, so the description carries full burden. It mentions earning lollipops and bonus rewards, but doesn't disclose behavioral traits like whether this is a mutation, what happens on submission, error handling, or any 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.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Three short sentences with playful tone, but they're front-loaded with irrelevant details (lollipops, leaderboard) rather than core functionality. The structure is clear but inefficient.
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 3 parameters with 0% schema coverage, no annotations, and an output schema exists, the description is incomplete. It fails to explain the tool's purpose, parameters, or behavior adequately for a mutation-like tool.
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 three undocumented parameters. It provides no information about what 'original_code', 'fixed_code', or 'errors_fixed' mean or how they should be used.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose2/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description mentions 'submit fixed code' which aligns with the tool name, but it's vague about what resource is being submitted to and focuses on rewards (lollipops) rather than core functionality. It doesn't distinguish from siblings like 'suggest_fix' or 'check_code'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines1/5
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 like 'suggest_fix' or 'check_code'. The playful tone about lollipops and leaderboards doesn't provide practical 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 full burden. It mentions the tool returns 'information about potential naming inconsistencies and suggestions', which gives some behavioral insight (non-destructive, read-only analysis). However, it lacks details on permissions, rate limits, error handling, or what constitutes 'existing naming patterns' (e.g., from a database or predefined rules). This is a significant gap for a tool with no 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 brief and front-loaded, with two sentences that directly state the tool's purpose and output. There's no wasted text, but it could benefit from slightly more detail to improve clarity without losing conciseness. The structure is efficient but under-specified.
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 has an output schema (which should document return values), the description doesn't need to explain outputs. However, with no annotations, 0% schema coverage, and one parameter, the description is incomplete—it lacks context on the system (e.g., programming language, project) and behavioral details. It's minimally viable but has clear gaps in guiding the agent.
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 must compensate. It implies a single parameter ('identifier name') but doesn't specify format, constraints, or examples (e.g., is it a variable name, file path, or something else?). The description adds minimal value beyond the schema's structural definition, failing to clarify the parameter's meaning or usage context.
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 tool checks if an identifier name is consistent with existing naming patterns, which is a clear purpose. However, it doesn't distinguish this from sibling tools like 'check_code' or 'check_lollipop_status', leaving ambiguity about what specific domain or system this applies to. The verb 'check' is clear but the scope 'existing naming patterns' is somewhat vague without context.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No explicit guidance is provided on when to use this tool versus alternatives. The description doesn't mention prerequisites, context (e.g., during code review or naming validation), or how it differs from siblings like 'check_code' or 'suggest_fix'. This leaves the agent guessing about appropriate use cases.
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 'runs Pyrefly type checker' and 'checks for naming consistency issues,' which implies a read-only analysis function. However, it lacks details on permissions, rate limits, error handling, or what 'previously seen identifiers' refers to (e.g., session-based tracking). This leaves significant gaps for a tool that analyzes code.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise and well-structured in three sentences. It front-loads the core purpose and efficiently explains the dual functionality (type checking and consistency checking). No wasted words, though it could be slightly more detailed given the lack of annotations and low schema coverage.
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 (code analysis with multiple parameters), no annotations, and 0% schema description coverage, the description is moderately complete. It covers the high-level functionality but misses key details like parameter meanings and behavioral traits. The presence of an output schema mitigates the need to describe return values, but overall completeness is limited for effective agent 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%, meaning the input schema provides no parameter descriptions. The tool description mentions 'provided code' and 'previously seen identifiers,' which loosely relates to the 'code' and 'track_identifiers' parameters. However, it doesn't explain the purpose of 'filename' or 'context_files,' leaving 2 of the 4 nested parameters undocumented. The description adds minimal value beyond the bare parameter names.
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: 'Check Python code for errors and inconsistencies using Pyrefly.' It specifies the action (check), target (Python code), and method (Pyrefly type checker). However, it doesn't explicitly differentiate from sibling tools like 'check_consistency' or 'suggest_fix,' which likely serve related purposes.
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 mentions checking for 'naming consistency issues based on previously seen identifiers,' but doesn't clarify how this relates to sibling tools like 'check_consistency' or 'track_identifier.' There are no explicit when-to-use or when-not-to-use instructions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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 analyzing error messages and providing actionable suggestions, but lacks details on traits like whether it's read-only or mutative, error handling, rate limits, or authentication needs. For a tool with no annotations, 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.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately sized with two concise sentences that front-load the core purpose. Each sentence adds value: the first states the main function, and the second elaborates on the analysis process. There's no wasted text, making it efficient and well-structured.
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 moderate complexity (error analysis), no annotations, and an output schema (which reduces the need to describe return values), the description is partially complete. It covers the basic purpose but lacks details on behavioral traits and parameter semantics. With schema coverage at 0% and no annotations, it should do more to compensate, but the presence of an output schema slightly mitigates this.
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%, meaning parameters are undocumented in the schema. The description doesn't add any meaning beyond the parameter names ('error_message' and 'code_context'), such as explaining what constitutes a valid error message or how code context influences suggestions. With two parameters and no schema descriptions, the description fails to compensate for this gap.
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: 'Suggest fixes for common Python errors based on error messages' and 'Analyzes error messages and provides actionable suggestions.' It specifies the verb ('suggest fixes'), resource ('Python errors'), and method ('based on error messages'). However, it doesn't explicitly differentiate from sibling tools like 'check_code' or 'submit_fixed_code', 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 minimal guidance on when to use this tool, only implying it's for Python error analysis. It doesn't specify when to use it versus alternatives like 'check_code' (which might check code without errors) or 'submit_fixed_code' (which might submit fixes after suggestions). No explicit exclusions or prerequisites are mentioned, leaving usage context vague.
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 'checks' and 'shows' results, implying a read-only operation, but doesn't disclose behavioral traits such as data sources, update frequency, or potential side effects. The mention of 'A/B testing results' and 'making LLMs fix errors' adds some context, but lacks details on authentication, rate limits, or output format.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise with two sentences that directly address the tool's function. The first sentence states the purpose, and the second adds context about effectiveness. There is no unnecessary information, and it's front-loaded with the core action. However, it could be slightly more structured for clarity.
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 has no parameters, annotations, but an output schema exists, the description is moderately complete. It explains what the tool does and its focus on personas and error correction, but lacks details on behavioral aspects like data freshness or integration with other tools. The output schema likely covers return values, so the description doesn't need to explain those, but more context on usage scenarios would improve completeness.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has 0 parameters with 100% schema description coverage, so no parameter documentation is needed. The description doesn't add parameter semantics, but this is acceptable as there are no parameters to describe. Baseline score is 4 for zero parameters, as the schema fully covers the input requirements.
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: 'Check A/B testing results for psychological manipulation personas' specifies the verb (check) and resource (A/B testing results). It distinguishes from siblings like 'check_code' or 'check_consistency' by focusing on personas and psychological manipulation. However, it doesn't fully explain what 'psychological manipulation personas' are in this context.
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 minimal usage guidance. It mentions the tool shows 'which personas are most effective at making LLMs fix errors,' which implies usage when evaluating persona effectiveness in error correction. However, it lacks explicit when-to-use criteria, prerequisites, or alternatives among siblings like 'suggest_fix' or 'submit_fixed_code.' No exclusions or comparative guidance is provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden for behavioral disclosure. It states it lists tracked identifiers, implying a read-only operation, but doesn't clarify if it's safe, whether it requires specific permissions, or how it handles large datasets (e.g., pagination). The mention of 'current session' adds some context, but overall, key behavioral traits like side effects or performance are missing.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with the core purpose in the first sentence, followed by an optional feature. Both sentences are essential and waste-free, making it highly efficient and easy to scan. The structure is logical 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.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has an output schema (which should cover return values), the description doesn't need to explain outputs. However, with no annotations and low schema coverage, it partially compensates by detailing the parameter's semantics. It's adequate for a simple list tool but lacks guidance on usage and behavioral context, making it minimally complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate. It adds meaning by explaining that 'type_filter' can filter by categories like function, variable, class, method, or constant, which clarifies the parameter's purpose beyond the schema's generic title. However, it doesn't specify allowed values or format details, leaving some gaps in parameter understanding.
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 ('List') and resource ('tracked identifiers in the current session'), making the purpose understandable. It distinguishes from siblings like 'track_identifier' (which creates) versus this listing operation. However, it doesn't explicitly contrast with other read operations among siblings, keeping it from 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. It mentions optional filtering by type but doesn't specify scenarios for filtering or when to choose this over other sibling tools like 'check_code' or 'suggest_fix'. There's no mention of prerequisites or exclusions, leaving usage context vague.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden for behavioral disclosure. It mentions 'track' and 'register' which imply a write/mutation operation, but doesn't specify permissions needed, whether this is idempotent, what happens on duplicate registration, or any rate limits. The description adds minimal behavioral context beyond the basic action.
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 two sentences that directly state purpose and usage. Every word earns its place with zero waste or redundancy. It's appropriately sized for a simple tracking tool.
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 has an output schema (which handles return values), no annotations, and simple parameters, the description is minimally complete. However, for a mutation tool with 0% schema description coverage, it should provide more parameter guidance and behavioral context. The description covers basic purpose but leaves significant gaps.
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 must compensate for undocumented parameters. The description mentions 'identifier' but doesn't explain what parameters are needed (name, type, signature, file_path) or their semantics. It fails to add meaningful parameter information beyond what's implied by the tool name.
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: 'Explicitly track an identifier for consistency checking' and 'register identifiers that should be used consistently throughout the codebase.' It specifies the verb ('track', 'register') and resource ('identifier'), but doesn't explicitly differentiate from sibling tools like 'list_identifiers' or 'check_consistency'.
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 provides some usage context: 'Use this to register identifiers that should be used consistently throughout the codebase.' This implies when to use it (for consistency checking), but doesn't explicitly state when not to use it or mention alternatives among sibling tools like 'check_consistency' or 'list_identifiers'.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It adds useful context: the warning about 'checking too often' suggests potential rate limits or social implications, and it hints at competitive aspects ('position relative to Mystery_Coder_X'). This goes beyond basic functionality, though it lacks details on auth needs or exact behavioral traits.
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 brief and front-loaded with the main purpose, followed by a warning. Both sentences earn their place by adding value—the first states the action, and the second provides behavioral context. It avoids unnecessary verbosity, though the playful tone might slightly obscure clarity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has 0 parameters, 100% schema coverage, and an output schema exists (so return values needn't be explained), the description is reasonably complete. It covers the purpose and adds behavioral warnings, though it could benefit from more explicit usage scenarios or prerequisites to fully guide the agent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 0 parameters with 100% coverage, so no parameter documentation is needed. The description doesn't add param info, but this is acceptable given the baseline. It implies no inputs are required, aligning with the schema, so it compensates adequately.
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 tool checks 'lollipop collection and leaderboard position,' which provides a general purpose. However, it's vague about what 'lollipop collection' entails and doesn't clearly differentiate from sibling tools like 'check_code' or 'check_consistency' in terms of specific functionality or domain.
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 includes a warning about checking too often, which implies a usage constraint, but it doesn't provide explicit guidance on when to use this tool versus alternatives like 'track_identifier' or 'list_identifiers.' No clear context or exclusions are stated, 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.
Behavior3/5
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 indicates this is a destructive/reset operation ('clear all', 'start fresh', 'reset'), which is valuable context. However, it doesn't specify what 'tracked identifiers' are, whether the action is reversible, or what happens after clearing (e.g., does it return confirmation?). The description adds some behavioral insight but leaves 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 extremely concise with only two sentences, both of which add clear value. The first states the core action, and the second provides usage context. There's no wasted text, and information is front-loaded appropriately.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (0 parameters, has output schema), the description is reasonably complete. It explains what the tool does and when to use it. With an output schema present, it doesn't need to detail return values. However, for a destructive operation with no annotations, it could benefit from more explicit warnings about irreversible effects or confirmation of success.
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 0 parameters with 100% schema description coverage, so the schema fully documents the lack of inputs. The description appropriately doesn't discuss parameters, focusing instead on the tool's purpose and usage. This meets the baseline expectation for parameterless tools.
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 ('clear all tracked identifiers', 'start fresh') and identifies the resource being acted upon (tracked identifiers). It doesn't explicitly differentiate from siblings like 'list_identifiers' or 'track_identifier', but the action is distinct enough to understand its unique function.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use this tool ('when starting a new project or to reset the consistency tracking'), which helps distinguish it from read-only siblings like 'check_consistency' or 'list_identifiers'. However, it doesn't explicitly state when NOT to use it or name specific alternatives, keeping it from a perfect score.
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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