ypollak2/llm-router

Server Quality Checklist

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Disambiguation2/5
Severe overlap between the seven text routing tools (llm_query, llm_route, llm_analyze, llm_code, llm_generate, llm_research, llm_stream) which all route text prompts to LLMs with only subtle 'best for' distinctions. Additionally, five separate usage tracking tools (llm_usage, llm_check_usage, llm_track_usage, llm_update_usage, llm_refresh_claude_usage) create confusion about which to call for budget monitoring.
Naming Consistency4/5
All tools consistently use the 'llm_' prefix and snake_case formatting. While there's a mix of simple verbs (classify, route), nouns (dashboard, health), and verb-noun compounds (cache_clear, set_profile), the overall convention is predictable and readable.
Tool Count2/5
30 tools is excessive for this domain. The toolset suffers from bloat that could be resolved by consolidating the 7 text routing variants into a single tool with a 'task_type' parameter, and merging the 5 usage tools into 2-3 unified calls. The surface area is larger than necessary for the functionality provided.
Completeness4/5
Very comprehensive coverage of the LLM routing lifecycle including setup (llm_setup), multi-modal generation (audio/image/video), orchestration (llm_orchestrate), caching, health checks, quality feedback (llm_rate), and session management. Minor gaps like missing 'list_sessions' or 'delete_cache_entry' are negligible given the breadth.
Average 4.1/5 across 30 of 30 tools scored. Lowest: 3.2/5.
See the tool scores section below for per-tool breakdowns.
This repository includes a README.md file.
This repository includes a LICENSE file.
Latest release: v1.0.0
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Tool Scores

Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. While 'List' implies read-only, the description does not disclose caching behavior, network requirements, rate limits, or whether templates are static or dynamic. Minimal behavioral context.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Single sentence of 7 words with action-fronted structure ('List available...'). Zero redundancy or waste. Every word earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Adequate for a zero-parameter tool with output schema present, but lacks domain context about what these pipeline templates contain or their relationship to the orchestration system. Minimum viable given complexity.
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?
Input schema contains 0 parameters with 100% coverage. Per scoring rules, 0 parameters warrants a baseline score of 4. No additional parameter semantics are needed or provided.
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?
Uses specific verb 'List' with resource 'pipeline templates' and provides domain context 'multi-step orchestration' that distinguishes it from execution-focused siblings like llm_orchestrate. However, it does not explicitly contrast with related tools.
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?
Provides no guidance on when to use this tool versus alternatives like llm_orchestrate (which likely executes pipelines) or when listing is preferable to other operations. No prerequisites or conditions mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It discloses the scope (unified across all providers, includes savings) but fails to mention critical behavioral traits like whether data is cached, real-time, or requires specific permissions. The 'shows' verb implies read-only behavior, but this is not explicit.
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 first two sentences are redundant ('Unified' vs 'complete picture', 'all providers' vs listing specific providers). The 'Args:' header is filler that adds no value. While appropriately brief, the front-loading could be more efficient by merging the first two sentences.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a single-parameter tool with an output schema, the description adequately covers the input parameter. However, given zero annotations and numerous ambiguous siblings, it should provide more behavioral context (caching, read-only confirmation) or differentiation guidance to be fully 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?
Excellent compensation for 0% schema description coverage. The description documents the single parameter 'period' with its specific enum values ('today', 'week', 'month', 'all'), which the schema completely lacks. It clarifies the time period semantics beyond the schema's bare type information.
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 identifies the tool as a 'Unified usage dashboard' that aggregates 'Claude subscription, Codex, external APIs, and savings' across providers. However, it does not explicitly distinguish from siblings like 'llm_dashboard' or 'llm_check_usage', leaving ambiguity about which usage tool to select.
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 provided on when to use this tool versus the numerous sibling alternatives (e.g., llm_check_usage, llm_track_usage, llm_dashboard). No prerequisites, exclusions, or workflow context is mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It successfully constrains behavior by documenting valid values ('budget', 'balanced', 'premium'), but fails to disclose persistence scope, side effects on concurrent operations, or error handling for invalid inputs.
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?
Highly efficient two-sentence structure with zero redundancy. The purpose statement ('Switch the active routing profile') is front-loaded, followed immediately by parameter constraints. Every word earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Adequate for a single-parameter tool with an output schema (which handles return value documentation). Missing behavioral context expected for a state-mutation operation: persistence model (session vs. global), validation behavior, and impact on in-flight requests.
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?
Excellent compensation for the 0% schema description coverage. The Args section provides the critical enum values ('budget', 'balanced', 'premium') completely absent from the input schema, enabling the agent to provide valid inputs. Could improve by describing what each profile entails.
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 ('Switch') and target ('active routing profile'), providing a concrete verb-resource pairing. However, it doesn't differentiate from sibling tools like 'llm_route' or clarify what constitutes a 'routing profile' 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?
No guidance provided on when to use this tool versus alternatives like 'llm_route' or 'llm_providers'. Missing information about prerequisites (e.g., whether a session must be initialized first) or when switching profiles is appropriate.
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. While 'Clear' implies destruction, the description lacks critical safety context: whether the operation is permanent, if it affects other users, performance implications, or required permissions.
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 of five words with zero redundancy. It is appropriately front-loaded and sized for a zero-parameter 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?
While the output schema handles return values and the parameter schema is trivial, the description is incomplete for a destructive operation. It lacks scope boundaries (does it clear all entries or user-specific ones?) and safety prerequisites that would guide proper invocation.
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 contains zero parameters, triggering the baseline score of 4. There are no parameters requiring semantic clarification beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb ('Clear') and identifies the exact resource ('prompt classification cache'), distinguishing it from sibling tools like llm_cache_stats (which likely reads stats) and llm_classify (which likely uses the cache).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No explicit guidance on when to use this tool versus alternatives, nor any warnings about when NOT to use it (e.g., during active classification operations). The description states what it does but not why or when an agent should invoke it.
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 successfully indicates scope ('all configured' providers), but fails to clarify whether this performs live API probes or cached checks, potential latency costs, or safety characteristics (read-only vs. 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 consists of a single, efficient sentence with no filler words. It is front-loaded with the core action and subject, making it immediately scannable.
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 low complexity (zero inputs) and the presence of an output schema, the description adequately covers the essential purpose. A minor gap remains in defining 'health status' specifics, but this is likely addressed in the output schema.
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 accepts zero parameters, which establishes a baseline score of 4 per the evaluation rubric. No parameter semantic guidance is required or provided.
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 identifies the action ('Check') and target ('health status of all configured LLM providers'), distinguishing it from siblings like llm_providers (likely just listing) or llm_check_usage (usage metrics). However, 'Check' is a weaker verb than 'Retrieve' or 'Get', and it doesn't specify what 'health' encompasses (connectivity, quotas, latency).
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 invoke this tool versus alternatives like llm_providers or llm_dashboard. It omits prerequisites (e.g., whether providers must be configured first) and triggers (e.g., 'use before generation to verify availability').
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 adds valuable context by distinguishing between 'supported' (available) and 'configured' (active) providers, revealing this is a state-inspection tool. However, it omits other behavioral details like whether results are cached or if the operation requires specific permissions.
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 of nine words with no redundancy. It is front-loaded with the action (List) and immediately specifies the dual nature of the returned information (supported vs configured).
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 low complexity (no parameters) and the presence of an output schema, the description is adequately complete. It clarifies what information the output contains without needing to detail return values, though it could briefly mention this is a discovery/utility 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 zero parameters, establishing a baseline score of 4 per the rubric. The description appropriately focuses on the tool's output behavior rather than non-existent inputs.
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 'Lists all supported providers and which ones are configured,' specifying the verb (List) and scope (providers, configuration status). It effectively distinguishes itself from action-oriented siblings like llm_generate or llm_setup by focusing on discovery and state inspection.
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 invoke this tool versus alternatives. It does not indicate whether this should be called before other operations (e.g., to check available providers before generation) or how it relates to llm_setup.
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. It discloses the caching side effect and data flow (claude.ai API → cache → llm_classify). However, it omits mutation details (destructive overwrite vs merge), validation behavior, error handling, or idempotency characteristics.
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?
Information is front-loaded and generally efficient. The embedded 'Args:' section is slightly awkward structurally but functional. Four sentences cover purpose, invocation method, data format, and downstream usage without excessive verbosity.
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?
Adequate for a single-parameter cache update tool where output schema exists (so return values need not be described). Compensates for poor schema coverage. Could improve by mentioning authentication requirements, validation constraints, or error conditions given the internal API dependency.
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 schema has 0% description coverage (only 'Data' as title). The description compensates effectively by specifying the parameter accepts 'the full JSON object from the claude.ai internal API' and clarifying it comes via browser_evaluate(FETCH_USAGE_JS). Could improve by mentioning required fields within the JSON.
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?
Clearly states it updates cached Claude usage from a JSON API response, with specific verb and resource. Mentions integration with browser_evaluate and llm_classify. However, it does not distinguish from similar siblings like llm_refresh_claude_usage or llm_check_usage.
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?
Provides explicit invocation context: 'Call this with the result from browser_evaluate(FETCH_USAGE_JS)'. Explains the downstream consumer (llm_classify for budget pressure). Missing explicit when-not-to-use guidance or comparison to alternative usage tools like llm_refresh_claude_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?
With no annotations provided, the description carries the full burden. It discloses the analytical scope (routing decisions, downshift rates, cost efficiency) and temporal nature ('specified period'), but omits safety characteristics (read-only status), performance costs, or data freshness (real-time vs cached).
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 good front-loading—the first sentence summarizes the output metrics, followed by behavioral context, then parameter details. The 'Args:' section is functional though slightly informal; no sentences feel wasted.
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 only one simple parameter and an output schema exists (reducing the need to describe return values), the description is reasonably complete. It covers the tool's specific domain (routing quality) and the single parameter, though it could mention permissions or cost implications given the analytical nature.
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 schema has 0% description coverage for the 'days' parameter. The description compensates by documenting it in the Args section: 'Number of days to include in the report (default 7),' providing necessary semantic meaning and default value that the schema lacks.
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 shows 'routing quality metrics' including 'classification accuracy, savings, model distribution,' and analyzes 'routing decisions' and 'classifier' performance. This distinguishes it from operational siblings like llm_route and llm_classify by positioning it as a retrospective analysis/reporting tool.
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?
While the description clarifies the scope (analyzing routing decisions over a specified period), it lacks explicit guidance on when to use this versus similar siblings like llm_dashboard or llm_analyze. No 'when-not-to-use' or alternative recommendations are provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It discloses the external routing behavior (ElevenLabs vs OpenAI), which is valuable context. However, it omits critical operational details: cost implications, output format (despite having an output schema), error handling for provider failures, and whether operations are idempotent or destructive.
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?
Front-loaded purpose statement followed by structured Args documentation. Given the schema coverage gap, every line serves a necessary function. Minor deduction for the abrupt 'Args:' formatting shift, which slightly breaks narrative flow but remains highly scannable.
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?
Adequate for basic invocation given the output schema handles return values, but incomplete for a dual-provider external API tool. Missing operational context such as authentication requirements, cost per request, audio format details, and timeout behavior that would help an agent handle failures gracefully.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Excellent compensation for 0% schema description coverage. The Args section adds essential domain context: explains 'text' purpose, provides concrete model override examples with provider prefixes, and clarifies voice selection semantics (OpenAI enum values vs ElevenLabs voice IDs). This goes significantly beyond the bare type information in the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
Clear specific verb ('Generate') + resource ('speech/audio') and explicitly distinguishes implementation ('routes to ElevenLabs or OpenAI TTS'). Among siblings like llm_generate, llm_image, and llm_video, this precisely positions the tool as the text-to-speech option.
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?
Provides no guidance on when to use this tool versus llm_generate or other siblings, nor when to choose ElevenLabs over OpenAI. No mention of prerequisites, costs, or rate limit considerations for external API usage.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. Discloses important behavioral details: reads from macOS Keychain, calls external Anthropic endpoint, updates local cache, and platform restriction (macOS only). Missing potential error states or rate limit details, but covers main operational mechanics well.
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?
Three well-structured sentences: front-loaded purpose with key differentiator, mechanism explanation, and prerequisites. Zero waste—every sentence adds essential context not available in structured fields.
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?
Adequate for tool complexity despite no annotations. Covers OAuth flow, caching behavior, platform constraints, and prerequisites. Output schema exists (per context signals), so return value explanation is unnecessary. Could enhance with error condition notes, but sufficient for invocation decisions.
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?
Input schema has 0 parameters, establishing baseline 4. Description appropriately requires no parameter explanation since the tool operates with no inputs (reads from Keychain automatically).
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?
Clear verb ('Refresh') and resource ('Claude subscription usage') with specific mechanism ('via the OAuth API'). Implicitly distinguishes from siblings like llm_usage or llm_check_usage by specifying OAuth/Keychain approach and 'no browser required,' though explicit comparison to alternatives is absent.
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?
Provides prerequisite requirements ('Requires: Claude Code installed and authenticated on macOS') and implies usage context ('no browser required'). However, lacks explicit when-to-use guidance versus sibling tools like llm_update_usage or llm_track_usage, and no 'when-not' 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?
With no annotations provided, the description carries full behavioral burden and excels at explaining the internal model routing logic—specifically mapping complexity levels to specific model tiers (Haiku/Flash for simple, Sonnet/GPT-4o for implementation, Opus/o3 for architecture). It could further improve by mentioning error handling or retry behavior.
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 well-structured with clear sections: a one-line summary, a 'Best for' usage statement, and an Args block with detailed parameter explanations. Every sentence adds value, particularly the model routing details which are essential for proper complexity selection.
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 5 parameters with zero schema descriptions and lack of annotations, the description provides sufficient context for invocation. Since an output schema exists, the description appropriately omits return value details. It could reach 5 by explicitly contrasting with the similar 'llm_codex' sibling tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has 0% description coverage (titles only), and the description comprehensively compensates by documenting all 5 parameters. It adds crucial semantic meaning to the 'complexity' enum values (explaining exactly which models are selected for each tier) and clarifies the purpose of optional fields like 'system_prompt' and 'context'.
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 routes coding tasks to appropriate models and lists specific use cases (code generation, refactoring, algorithm design). However, with siblings like 'llm_codex' and 'llm_edit', it could more explicitly differentiate its specific routing purpose from other coding-related tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The 'Best for' section provides clear positive guidance on when to use the tool (coding tasks). However, it lacks explicit guidance on when NOT to use it or which sibling tools (e.g., 'llm_generate', 'llm_codex') should be preferred for non-coding or specific coding scenarios.
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, description carries full burden and succeeds well: discloses background execution, 30s refresh interval, SQLite data source, localhost binding, and return format. Missing only lifecycle details (how to stop server) or port conflict 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?
Front-loaded with core action, followed by behavioral details, then Args/Returns sections. Efficient structure given schema lacks descriptions. Minor deduction for formal 'Args/Returns' labels which are slightly redundant but necessary given schema gaps.
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?
Adequate for a server-starting tool: covers background process nature, data source, refresh behavior, and return value (URL/instructions). References output schema existence appropriately without over-explaining returns.
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?
Compensates effectively for 0% schema description coverage by documenting the port parameter's purpose ('TCP port for the dashboard server') and default value (7337). Could improve by noting valid port ranges or constraints.
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?
Specific verb ('Open') combined with exact resource ('LLM Router web dashboard') and clear scope (routing stats, cost trends, model distribution). Distinct from text-based siblings like llm_check_usage or llm_cache_stats by emphasizing HTTP server and visualization.
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?
Provides implied usage through description of dashboard content (stats, trends, decisions), indicating when visualization is needed. However, lacks explicit comparison to alternatives like llm_check_usage or guidance on when text vs. web interface is preferred.
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 full disclosure burden. It adds valuable behavioral context about outputs ('Shows per-call savings vs opus and cumulative session savings') and explains the budget-tracking purpose. However, it omits whether this performs a write operation, idempotency characteristics, or side effects on the daily budget state.
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 well-structured and front-loaded, opening with the core purpose, followed by usage timing, business logic (downshifting), output behavior, and a structured Args section. Every sentence provides distinct value with no redundancy or filler text.
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 presence of an output schema, the description appropriately summarizes return values (savings comparisons) without replicating the schema. It covers the operational workflow, parameter semantics, and budget context. A minor gap remains regarding prerequisites or side effects, but overall coverage is strong for a tracking utility.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The Args section fully compensates for 0% schema description coverage by documenting all 3 parameters with clear semantics and explicit enum values: model ('haiku', 'sonnet', or 'opus'), tokens_used ('Approximate tokens consumed by the Agent call'), and complexity ('simple', 'moderate', 'complex'). This adds critical meaning missing from the raw schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool 'Report[s] Claude Code model token usage for budget tracking' with specific context about enabling 'progressive model downshifting.' However, it does not explicitly differentiate from similar sibling tools like 'llm_usage' or 'llm_check_usage,' leaving some ambiguity about which tracking tool to use when.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides explicit workflow guidance: 'Call this after using an Agent with haiku/sonnet to track token consumption against the daily budget.' This establishes clear timing and context. It lacks explicit 'when-not-to-use' instructions or named alternatives, but the temporal cue ('after using an Agent') effectively guides selection.
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 full behavioral disclosure burden. It explains the routing logic across providers and default duration, but omits critical behavioral traits typical for video generation: async/job-based processing, cost implications, or output file format details.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Efficiently structured with the core purpose front-loaded, followed by compact parameter documentation. Every line provides value; no redundancy despite the Arg-style formatting necessitated by missing schema descriptions.
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 presence of an output schema (exempting return value documentation) and the parameter coverage, the description is reasonably complete. Minor gap: lacking mention of async behavior typical for video generation APIs, though this may be evident in the output schema structure.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, requiring the description to fully compensate. The Args section successfully documents all three parameters (prompt semantics, model examples with provider strings, duration defaults), providing necessary context absent from the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states 'Generate a video' with specific video model references (Gemini Veo, Runway, Kling) that immediately distinguish it from sibling tools like llm_image or llm_audio. The routing behavior is explicitly mentioned.
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?
While the description implies usage through the specific video model names mentioned, it lacks explicit guidance on when to choose this over llm_image or llm_generate, or prerequisites like async handling expectations for video generation.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full disclosure burden. Successfully clarifies execution mode ('non-interactively'), cost model ('uses the user's OpenAI subscription'), and locality ('local Codex desktop agent'). However, misses critical operational context for a code-agent tool: no disclosure of potential file system side effects, sandbox status, error conditions (e.g., Codex not installed), or timeout 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?
Well-structured and front-loaded with core purpose. Information flows logically from mechanism to cost model to available options. The 'Args:' section introduces slight redundancy with the schema structure, but is justified given the complete lack of schema descriptions (0% coverage).
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?
Appropriately complete for a 2-parameter tool with existing output schema (no return value explanation needed). Covers parameter semantics, purpose, and sibling differentiation adequately. Minor gap regarding side-effect disclosure for a potentially destructive code-agent tool, but sufficient for basic invocation decisions.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, requiring full compensation. Description comprehensively documents both parameters ('prompt' as 'task or question', 'model' as 'OpenAI model to use') and crucially provides the enum list of available models (gpt-5.4, o3, etc.) entirely missing from the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
Specific verb+resource combination ('Route a task to the local Codex desktop agent') clearly identifies the tool's function. Explicitly distinguishes from siblings by specifying OpenAI/Codex brand, local desktop agent nature, and OpenAI subscription usage versus Claude quota.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides explicit when-to-use guidance ('ideal as a fallback when Claude limits are tight, or for tasks that benefit from OpenAI's models'), establishing clear context for selection. Lacks explicit when-not-to-use or named sibling alternatives, though the Claude vs OpenAI distinction effectively differentiates from the broader llm_* toolset.
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. It successfully discloses model routing behavior ('routes to the best generation model') and cost implications (simple vs. premium models based on complexity). However, it omits safety profiles, rate limits, idempotency, or error handling behavior that would be necessary for a higher score.
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 well-structured with a clear purpose statement upfront, followed by use-case guidance, then parameter details. While the Args section is slightly verbose, every sentence provides necessary information given the lack of schema documentation, making it appropriately sized for its complexity.
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 6 parameters with zero schema coverage, the description successfully documents all inputs and their interactions (e.g., complexity affecting model selection). The presence of an output schema reduces the burden to describe return values. Minor gaps remain around rate limits and explicit safety declarations, but it is otherwise complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has 0% description coverage, but the description fully compensates by providing rich semantics for all 6 parameters. Notably, it explains the business logic for 'complexity' (drives model selection and cost), clarifies that 'temperature' affects creativity, and defines 'context' as conversation history—adding meaning beyond type information.
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 opens with a specific verb ('Generate') and resource ('creative or long-form content'), and explicitly states the routing mechanism ('routes to the best generation model'). This clearly distinguishes it from siblings like llm_analyze, llm_code, or llm_classify by focusing on generative content creation.
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 'Best for' section explicitly lists appropriate use cases (writing, summarization, brainstorming, content creation). However, it lacks explicit negative guidance or named alternatives (e.g., 'use llm_code for programming tasks'), though the content-type focus implicitly steers users away from analytical siblings.
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. It successfully discloses the external dependency ('routes to Perplexity') and data source ('web-grounded'), but omits operational details critical for an external API call: rate limits, authentication requirements, failure modes, or caching behavior.
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 efficiently structured with clear sections: mechanism, use cases, and argument definitions. No sentences are wasted; the information density is appropriate for quick agent parsing.
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 4-parameter tool with no annotations but an existing output schema, the description adequately covers purpose, usage contexts, and parameter meanings. Minor gap: lacks mention of prerequisites (e.g., Perplexity API configuration) or behavioral constraints.
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?
Given 0% schema description coverage, the description fully compensates by documenting all 4 parameters with clear semantics ('The research question', 'Optional system instructions', etc.). It loses a point for somewhat tautological definitions (e.g., 'system_prompt: Optional system instructions') and lack of constraint details (formats, defaults).
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 defines the tool as 'Search-augmented research query' that 'routes to Perplexity for web-grounded answers', providing a specific mechanism (Perplexity routing) and resource type (web data) that clearly differentiates it from generic siblings like llm_generate or llm_query.
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 'Best for:' list explicitly identifies appropriate use cases (fact-checking, current events, finding sources, market research), providing clear positive guidance. However, it lacks explicit negative guidance (when not to use) or named alternatives (e.g., 'use llm_generate for creative writing instead').
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full disclosure burden. It successfully explains the streaming mechanism ('shows output as it arrives', 'streams chunks'), but omits operational details like error handling, retry behavior, rate limits, or side effects (e.g., caching, usage tracking) that would be critical for an LLM tool.
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?
Well-structured with purpose front-loaded, sibling differentiation in the middle, and parameter details at the end. The Args list is necessary given schema deficiencies. Minor verbosity in phrases like 'or any task where seeing partial output early is valuable' could be tightened.
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 output schema exists (per context signals), the description appropriately focuses on inputs and behavior. All 6 parameters are documented. Minor gaps remain around error states and whether streaming affects billing/usage differently than non-streaming siblings, but core functionality is well-covered.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0% (titles only). The description fully compensates by documenting all 6 parameters in the Args section, including semantic meaning (e.g., 'task_type hint'), valid ranges ('0.0-2.0' for temperature), and concrete examples ('openai/gpt-4o', 'gemini/gemini-2.5-flash').
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?
Specific verb ('Stream') + resource ('LLM response') combination clearly states function. Explicitly distinguishes from sibling 'llm_route' by contrasting streaming chunks vs. waiting for full response, making selection unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides clear when-to-use guidance ('Ideal for long-form generation, research summaries, or any task where seeing partial output early is valuable') and implicitly contrasts with llm_route. Lacks explicit 'when not to use' guidance or named alternatives for non-streaming scenarios.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It adds valuable behavioral context about model routing ('strongest reasoning model', specific mention of Opus/o3) and default complexity settings. However, it lacks disclosure on idempotency, cost implications, or caching behavior that would be necessary for a full 4-5 score without 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Highly structured with clear sections (purpose, Best for, Args). Every sentence provides specific value—no filler. The complexity guidance is dense with actionable decision criteria ('Analysis tasks default to at least moderate') without being verbose.
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 output schema exists, the description appropriately focuses on inputs and usage context. It successfully differentiates this tool from the 25+ sibling LLM tools via the 'deep analysis' and 'strongest reasoning model' framing. Missing explicit comparison to specific siblings (like llm_research) prevents a 5.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage, the description fully compensates by documenting all 5 parameters. It provides especially rich semantics for the complexity parameter (enumerating values 'simple', 'moderate', 'complex' and mapping them to specific use cases like 'multi-file reviews'), which goes well beyond basic type information.
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 defines the tool as performing 'Deep analysis' that 'routes to the strongest reasoning model,' distinguishing it from sibling tools like llm_generate or llm_classify. It specifies the resource being operated on (analysis tasks) and the verb (routes/analyzes).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides explicit 'Best for' criteria (data analysis, code review, debugging) and detailed guidance on the complexity parameter (when to use 'complex' for multi-file reviews). However, it does not explicitly name alternative tools (e.g., 'use llm_generate for simple text generation instead') or state 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?
With no annotations provided, the description carries the full burden and succeeds well: it discloses cache internals (stores ClassificationResult objects), key composition (SHA-256 of prompt + quality_mode + min_model), and critical validity semantics (budget pressure applied fresh, so entries remain valid). Could explicitly state read-only 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 total, both essential: first establishes function and metrics, second provides critical cache invalidation logic. Front-loaded with the action verb. No redundant or wasted language.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has an output schema (not shown but indicated), the description appropriately omits return value details. It adequately covers the tool's purpose, internal cache mechanics, and validity constraints for a parameter-less statistics inspection 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?
Input schema contains zero parameters. Per guidelines, 0 parameters establishes a baseline of 4. The description correctly omits parameter discussion since none exist, and no parameter semantics are needed.
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 specific action ('Show') and resource ('prompt classification cache statistics'), listing exact metrics provided (hit rate, entries, memory usage). It effectively distinguishes from sibling 'llm_cache_clear' by specifying read-only statistics retrieval versus deletion.
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?
While the function is implied by 'Show... statistics' (use when monitoring cache performance), there is no explicit guidance on when to prefer this over alternatives like 'llm_cache_clear' or prerequisites for interpretation. No 'when-not-to-use' or comparison to sibling tools is provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. Discloses caching logic, conditional return types (cached data vs Playwright JS snippet), execution context ('no page navigation needed'), and side effects on model routing. Lacks only potential error states or 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?
Three tightly focused paragraphs: core purpose, caching/implementation details, and routing implications. No redundant text; every sentence provides actionable information. Well front-loaded with the primary function.
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 output schema exists, the description adequately covers return value semantics (data vs snippet) and operational context (routing impact). Complete for a zero-parameter tool, though explicit error handling mention would perfect it.
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?
Zero parameters present (empty schema), establishing baseline 4. Description appropriately focuses on return behavior and side effects rather than non-existent 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?
States specific action ('Check') and resource ('real-time Claude subscription usage') with precise scope ('session limits, weekly limits, extra spend'). The mention of 'real-time' and caching behavior distinguishes it from siblings like 'llm_usage' or 'llm_refresh_claude_usage'.
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?
Explains operational behavior (cached data vs JS snippet return) and crucial downstream impact ('budget pressure feeds directly into model routing'). However, it does not explicitly name sibling alternatives or state when to prefer this over 'llm_usage' or 'llm_refresh_claude_usage'.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. Discloses core behavioral traits: automatic decomposition, multi-model routing, and tier-based step limits. Could improve by disclosing error handling behavior (what happens if a step fails), idempotency, or whether partial 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.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Well-structured and front-loaded: purpose (sentence 1), mechanism (sentence 2), usage pattern (sentence 3), constraints (sentences 4-5), parameters (final section). Efficient use of space, though 'Args:' section is slightly informal. No wasted sentences.
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 high complexity (multi-step orchestration) and presence of output schema, description adequately covers invocation requirements, behavioral constraints, and parameters. Minor gap: lacks discussion of side effects, persistence of intermediate steps, or timeout behavior expected for long-running orchestration tasks.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%—neither parameter has a schema description. Description fully compensates: defines 'task' as the 'complex task to accomplish,' and details 'template' with four specific enum values ('research_report', 'competitive_analysis', etc.) and explains omitting it triggers auto-decomposition.
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?
Opens with specific verb 'Multi-step orchestration' and resource 'complex tasks across multiple LLMs.' Explicitly distinguishes from single-step siblings (llm_generate, llm_analyze) by detailing chaining of 'research, analysis, generation, and coding steps' and 'routing each to the optimal model.'
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides clear internal usage guidance: 'Use templates for common patterns or let the AI decompose' and lists four specific template options. Includes operational constraints (Free tier: 2-step vs Pro: unlimited). Lacks explicit comparison to specific sibling tools like llm_pipeline_templates or llm_research for when to prefer those over this.
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 and discloses key behavioral logic: the routing algorithm (complexity→model mapping), auto-detection from prompt length, and model override behavior. Missing cost implications or latency expectations that would be relevant for an LLM API 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?
Well-structured with clear front-loading: one-line summary, routing logic explanation, then Args section. Every sentence adds value; no repetition of schema structure or tautology. The formatting efficiently packs detailed parameter semantics without waste.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a 7-parameter tool with complex routing logic, the description is complete. Since an output schema exists, the description correctly focuses on input parameters and routing behavior rather than return values. All necessary invocation context is provided.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Given 0% schema description coverage, the description comprehensively compensates by documenting all 7 parameters including data types (temperature range 0.0-2.0), optional vs required semantics, and behavioral triggers (complexity auto-detection, model bypass logic).
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 'Send[s] a general query to the best available LLM' with specific verb and resource. The term 'general' effectively distinguishes it from specialized siblings like llm_code, llm_analyze, and llm_audio, positioning it as the default text-generation tool.
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?
Provides excellent internal usage guidance via the complexity routing explanation (simple/moderate/complex mappings), but lacks explicit external guidance comparing when to use this versus specialized siblings (e.g., 'use llm_code for programming tasks'). Usage versus alternatives is only implied by the word 'general'.
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. It successfully discloses the auto-routing behavior and critical provider-specific constraints (quality is 'DALL-E only'). It could be improved by mentioning rate limits or failure modes, but the provider-specific warnings demonstrate good behavioral 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 efficiently structured with a single-sentence purpose statement followed by a clear Args list. Every line provides actionable information; there is no redundant text or generic filler. The formatting makes parameter requirements immediately scannable.
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 existence of an output schema (covering return values) and the description's comprehensive handling of the 0%-covered input parameters, the description is appropriately complete. It addresses the complexity of multi-provider routing. A minor gap is the lack of mention of what occurs when model is null (though implied by 'auto-routes').
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has 0% description coverage, so the description fully compensates by documenting all four parameters. It adds crucial semantic meaning through concrete examples (e.g., 'gemini/imagen-3', 'openai/dall-e-3', '1024x1024') and value constraints ('standard' or 'hd'), without which the agent would lack guidance on valid inputs.
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 opens with a specific verb ('Generate') and resource ('image'), clearly distinguishing it from text-generating siblings like llm_generate. It further differentiates by specifying the four supported provider families (Gemini Imagen, DALL-E, Flux, Stable Diffusion), establishing clear scope.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context through the 'auto-routes' clause, indicating the tool should be used when automatic provider selection is desired, while the 'model override' parameter implies when to specify a provider manually. While it lacks explicit 'when not to use' exclusions, the auto-routing behavior serves as a functional usage guideline.
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 and discloses important behavioral traits: 'writes to .env file securely' and 'safe, read-only' for the discover action, 'Key is validated before saving,' and global scope for hooks. It misses minor details like error handling or idempotency, but covers the critical safety and side-effect concerns.
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 well-structured with a front-loaded purpose statement followed by actionable bullet points. The Args section efficiently maps parameters to specific actions. Minor redundancy exists between the bullet points and Args descriptions, but this is justified given the zero schema coverage and multi-modal nature of the tool.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a complex 8-mode tool with 0% schema coverage, the description is remarkably complete. It documents every action's behavior, all parameter constraints, and side effects (file writes, global installation). Since an output schema exists, the description appropriately omits return value documentation, focusing instead on input semantics and operational modes.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Given 0% schema description coverage, the description fully compensates by documenting all three parameters in the Args section: it lists the 8 valid action values, specifies which actions require the 'provider' parameter, and notes that 'api_key' is only for the 'add' action with validation behavior. This provides essential context missing from the structured schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description opens with a clear purpose statement ('Set up and manage API providers, hooks, and routing enforcement') and distinguishes itself from execution-oriented siblings (llm_generate, llm_analyze, etc.) by focusing on configuration management. The 8 bullet points provide specific verb-resource pairs for each action mode.
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 implicit usage guidance through the detailed action breakdown (e.g., when to use 'discover' vs 'add' vs 'test'), allowing the agent to select the correct action based on context. However, it lacks explicit 'when not to use' guidance or differentiation from potentially overlapping siblings like 'llm_providers'.
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 successfully explains side effects (storage in 'routing_decisions' table), long-term machine learning impact (retraining the classifier), and return value semantics (confirmation or error). It does not mention idempotency or duplicate-rating behavior, leaving minor 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 docstring structure is exemplary: front-loaded summary sentence followed by mechanism, Args, and Returns sections. No sentences are wasted; the explanation of the retraining signal adds necessary context beyond the tautological 'rate decision' to justify why an agent should invoke this.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a 2-parameter feedback tool, the description is complete. It explains the data destination, the ML feedback loop, and acknowledges the existence of an output schema by describing the return type. The relationship to the broader routing ecosystem (implied by 'routing decisions') is sufficiently established.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Given 0% schema description coverage, the Args section provides essential compensatory documentation: 'good' is explained as thumbs-up/down semantics, and 'decision_id' clarifies the None/default behavior for targeting the most recent decision. This fully addresses the schema's descriptive absence.
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 opens with a precise action ('Rate') and target ('routing decision'), clearly distinguishing this feedback tool from siblings like 'llm_route' (which likely creates decisions) and other llm_* utilities. The scope (good/bad rating) is immediately apparent.
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 establishes clear context by specifying this rates 'the last (or a specific) routing decision,' implying temporal usage after routing occurs. It explains the 'why' (retraining the classifier) but stops short of explicitly naming the sibling tool (e.g., llm_route) to call first or contrasting with alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full disclosure burden. It effectively explains the internal mechanism (cheap classifier assessment, model tier selection) and auto-detection behavior. It could improve by explicitly mentioning API key requirements or cost implications of external LLM calls.
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?
Perfectly structured with mechanism first, sibling differentiation second, and parameter details in a dedicated Args section. No redundant text; every sentence adds value beyond the structured fields. Length is appropriate for the complexity (7 parameters, routing logic).
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 existence of an output schema, the description appropriately omits return value details. It comprehensively covers the routing tiers and parameter meanings. Minor gap: lacks mention of authentication requirements or rate limits for external LLM calls, which would be relevant for an agent invoking external APIs.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage, the Args section fully compensates by documenting all 7 parameters. It adds critical semantics: task_type examples and auto-detection note, complexity_override options, temperature range (0.0-2.0), and the purpose of context (conversation history).
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 specific verbs ('classifies', 'routes') and clearly identifies the resource (external LLMs). It explicitly distinguishes itself from sibling tool 'llm_classify' by stating when to use that alternative instead ('For routing to Claude Code's own models... use llm_classify').
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides explicit guidance on when to use the sibling tool 'llm_classify' instead (for Claude Code's own models without API keys). The tiered routing examples (simple→budget, complex→premium) provide clear context for expected usage patterns.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
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, detailing the classification algorithm (complexity tiers), budget pressure thresholds (0-85%, 85-95%, 95%+), downshift behavior, and return characteristics (budget usage bar). It transparently explains exactly how the recommendation logic works.
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 efficiently structured with purpose upfront, followed by return value description, algorithm logic with clear bullet points, and parameter documentation. Every section earns its place; the detailed budget pressure thresholds are essential behavioral context, not verbosity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given that an output schema exists, the description appropriately focuses on the classification algorithm and high-level return characteristics rather than field-by-field output documentation. The complex budget/complexity interaction logic is thoroughly explained, making it complete for a tool of this complexity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Despite 0% schema description coverage (only titles provided), the Args section fully documents all three parameters with semantic meanings and valid enum values ('best'/'balanced'/'conserve' for quality, 'haiku'/'sonnet'/'opus' for min_model). The description completely compensates for the schema's lack of descriptions.
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 opens with 'Classify a prompt's complexity and recommend which model to use,' providing a specific verb and resource that clearly distinguishes it from sibling tools like `llm_generate` (content generation) and `llm_route` (routing). It further clarifies the scope by detailing the complexity-to-model mapping (simple→haiku, etc.).
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 establishes clear context for when to use the tool (complexity classification with budget awareness) and explains the decision logic thoroughly. However, it lacks explicit guidance on when *not* to use it versus specific alternatives like `llm_route` or `llm_generate`.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full behavioral disclosure burden and excels: it notes the 32 KB file truncation limit, specifies relative path resolution from CWD, discloses the cheap model cost optimization, details the JSON output structure ({file, old_string, new_string}), and explains the required downstream integration with the Edit 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?
Information is perfectly front-loaded and hierarchically organized: purpose statement → cost/mechanism explanation → integration instructions ('How to use the result') → applicability guidelines → parameter details. No sentences are redundant; every line addresses specific agent decision-making needs.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (multi-step workflow involving external model routing, file I/O, and required downstream Edit tool usage), the description is complete. It appropriately leverages the existence of an output schema by summarizing the key output structure without redundancy, and covers operational constraints (32KB limit, path resolution) that the schema and annotations fail to document.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has 0% description coverage (only titles), but the description fully compensates by documenting all three parameters: 'task' includes an example ('Add type hints...'), 'files' explains path resolution and size constraints, and 'context' clarifies its conversational purpose. This is maximum value-add for the parameter schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description opens with a specific action (route code-edit reasoning) and output (exact edit instructions), immediately clarifying the tool's role. It distinguishes from siblings by specifying this uses a 'cheap model' for cost efficiency rather than expensive reasoning, and targets code editing specifically versus general generation tasks like llm_generate.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides explicit positive guidance ('Best for: refactoring, bug fixes, adding small features') and contrasts with an alternative approach ('Instead of Opus reasoning...'). However, it lacks explicit negative constraints or direct comparison to specific sibling tools like llm_code or llm_generate that might also handle code tasks.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden and delivers substantial behavioral context: it uses a 'cheap model' (cost implication), generates a 'compact summary' (lossy compression), persists to 'SQLite' (storage mechanism), and affects 'future routed calls' (side effects on sibling tool llm_route). It also documents the skip threshold for small sessions.
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?
Four tightly constructed sentences with zero redundancy: sentence 1 states purpose, sentence 2 explains mechanism, sentence 3 describes side effects, and sentence 4 gives usage timing. Every sentence earns its place and the information is front-loaded.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the zero-parameter simplicity and existence of an output schema (which excuses the description from detailing return values), the description is complete. It adequately covers the tool's role within the larger family of llm_* tools by explaining how it feeds context to future routed calls.
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 contains zero parameters, which establishes a baseline of 4. The description appropriately does not invent parameters, and the constraint about 'fewer than 3 exchanges' is correctly positioned as internal behavior rather than an input parameter.
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 'summarize[s] and save[s] the current session for cross-session context' using specific verbs (summarize, save, persist) and identifies the resource (session). It distinguishes itself from siblings like llm_generate or llm_analyze by focusing on persistence and cross-session continuity rather than content creation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides explicit temporal guidance ('Call this before ending a session or when switching to a different task') and discloses an important constraint ('Sessions with fewer than 3 exchanges are skipped'). This clearly defines when to invoke the tool and when it will silently do nothing.
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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Please follow the instructions in the GitHub documentation.

Once GitHub recognizes the license, the system will automatically detect it within a few hours.

If the license does not appear on the server after some time, you can manually trigger a new scan using the MCP server admin interface.

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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.

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