Yantrix MCP

Server Quality Checklist

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Latest release: v1.0.0
Disambiguation4/5
Most tools have distinct purposes, but there is some overlap in the verification domain (verify_claim, deep_verify_claim, batch_verify) and timezone tools (detect_timezone, convert_timezone, find_timezone_overlap) that could cause mild confusion. The descriptions help differentiate them, but an agent might need to carefully choose between similar tools.
Naming Consistency5/5
Tool names follow a consistent verb_noun pattern throughout (e.g., analyze_call, check_compliance, generate_exam). There are no deviations in naming conventions, making it easy to predict and understand tool functions from their names.
Tool Count2/5
With 33 tools, the count is too high for a single server, indicating a lack of focus. The tools span diverse domains like sales analysis, compliance, timezone handling, and memory management, which should likely be split into multiple specialized servers for better coherence.
Completeness4/5
Within each sub-domain, the tools provide good coverage (e.g., memory tools include read, write, and search; verification tools offer basic, deep, and batch options). However, some gaps exist, such as no delete operation for memories or limited update capabilities in other areas, but agents can generally work around these.
Average 3/5 across 33 of 33 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
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- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
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This repository includes a README.md file.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
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```
{
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}
```
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How is the quality score calculated?

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

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

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

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

Tool Scores

Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions cost ('$0.003 USDC') and service ('indic'), which adds useful context, but lacks critical details like rate limits, authentication needs, output format, or whether it's read-only or mutative. This is inadequate for a tool with potential external dependencies.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise with three short phrases, front-loading the core functionality. However, the cost and service details, while useful, could be integrated more smoothly, and it lacks structural elements like examples or bullet points that might enhance clarity without adding bulk.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (sentiment and emotion detection), no annotations, 0% schema coverage, and no output schema, the description is incomplete. It misses key details: parameter semantics, output structure, error handling, and behavioral constraints. The cost and service hints are helpful but insufficient for full contextual understanding.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 0%, meaning parameters 'text' and 'language' are undocumented in the schema. The description fails to compensate by explaining what these parameters mean (e.g., 'text' as input for analysis, 'language' as code like 'hi' for Hindi), their formats, or valid values. This leaves parameters semantically unclear.
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 performs 'sentiment analysis with emotion detection' and specifies the domain ('Indian languages including Hinglish'), which distinguishes it from siblings like 'analyze_call' or 'verify_claim'. However, it doesn't explicitly mention what resource it acts upon (e.g., text input) or differentiate from potential similar tools not in the sibling list.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides minimal guidance: it implies usage for Indian language text analysis but offers no explicit when-to-use rules, prerequisites, or alternatives. For example, it doesn't specify when to choose this over 'indic_ner' (a sibling) or other sentiment tools, nor does it mention cost considerations as usage factors.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden for behavioral disclosure. It mentions cost and service provider, which adds useful context about external API usage and potential monetary implications. However, it lacks critical details: whether it's idempotent, rate limits, error handling, or authentication needs. For a paid analysis tool, this is insufficient.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise and front-loaded, with the core purpose stated first. The two sentences earn their place: one defines analysis outputs, the other provides cost/service info. No redundant or verbose phrasing, though it could be more structured for readability.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no annotations, 0% schema coverage, no output schema, and 3 parameters, the description is incomplete. It covers the analysis purpose and cost but misses parameter explanations, behavioral traits (e.g., idempotency, errors), and output format. For a paid tool with multiple inputs, this leaves significant gaps for agent understanding.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate for undocumented parameters. It mentions 'sales call analysis' which implies the 'transcript' parameter, but doesn't explain 'call_type' or 'product' at all. The description adds minimal value beyond what's inferable from parameter names, failing to clarify semantics for 2 of 3 parameters.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Full sales call analysis — sentiment, objections, commitments, next steps, deal health score.' It specifies the verb 'analyze' and resource 'sales call' with detailed outputs. However, it doesn't explicitly differentiate from sibling tools like 'detect_fallacies' or 'indic_sentiment' that might also analyze content, leaving room for ambiguity.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides minimal usage guidance. It mentions cost and service provider ('Cost: $0.025 USDC. Service: callsight.'), which hints at external API usage, but offers no explicit when-to-use rules, alternatives, or prerequisites. Without comparing to siblings like 'indic_sentiment', agents may struggle to choose appropriately.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden for behavioral disclosure. It mentions cost and service provider, which adds some context about external dependencies and pricing. However, it doesn't describe what the tool actually does behaviorally (e.g., returns compliance violations, generates a report, requires authentication, has rate limits, or what 'check' entails operationally).
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 and front-loaded with the core purpose. The cost and service information is relevant but could be more integrated. Two sentences with minimal waste, though the second sentence feels somewhat tacked on rather than seamlessly structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no annotations and no output schema, the description is incomplete for a tool with 2 parameters. It lacks details on what the tool returns (e.g., compliance score, violations list), behavioral expectations, error handling, or prerequisites. The cost/service info adds some context but doesn't compensate for missing operational and output details.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate. It mentions 'architecture' and lists frameworks, which aligns with the two parameters, but doesn't explain what 'architecture' should contain (e.g., system design, configuration details) or the meaning of framework choices beyond listing them. No additional semantic context is provided beyond what's inferable from parameter names.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Check architecture against compliance framework' with specific frameworks listed (PCI-DSS, HIPAA, GDPR, ISO27001, SOC2, NIST). It uses a specific verb ('Check') and resource ('architecture'), but doesn't differentiate from sibling tools since none appear to be compliance-related alternatives.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It mentions cost and service provider ('Cost: $0.015 USDC. Service: threatmodel.'), but this doesn't help an agent decide between this and other compliance or analysis tools. No explicit when/when-not instructions or sibling comparisons are included.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden for behavioral disclosure. It mentions cost ($0.010 USDC) and service ('signal'), which adds some context, but fails to describe critical behaviors: what 'compare' entails operationally, how rankings are determined, what format recommendations take, whether this is a read-only or mutating operation, or any rate limits/authentication needs.
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 efficiently structured in a single sentence front-loading the core functionality, followed by cost and service details. Every element serves a purpose, though the cost/service information could be better integrated or explained in context.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with no annotations, 0% schema coverage, and no output schema, the description is incomplete. It lacks details on comparison methodology, output format, error conditions, and how it relates to sibling tools. The cost and service mentions are helpful but don't compensate for missing behavioral and parametric context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate. It implies 'topics' parameter corresponds to 'AI trends' but provides no semantic details: what constitutes a valid topic, format expectations, or how the array size (2-5) affects comparison. The description adds minimal value beyond what's inferable from the schema's structural constraints.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: comparing AI trends head-to-head with rankings and recommendations. It specifies the verb 'compare' and resource 'AI trends', along with the scope of 2-5 topics. However, it doesn't differentiate from sibling tools like 'score_trend' or 'model_threats' that might also analyze trends.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It mentions cost and service but doesn't explain scenarios where this comparison is appropriate, prerequisites, or how it differs from similar tools like 'score_trend' in the sibling list.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden of behavioral disclosure. It adds cost and service information, which is useful context, but lacks details on permissions, rate limits, error handling, or what 'CRM-ready structured data' entails (e.g., format, fields). This leaves significant gaps for a tool that processes data.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that front-loads the core purpose. However, the cost and service details, while relevant, could be integrated more seamlessly or placed after the main action to improve flow.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no annotations, 0% schema coverage, and no output schema, the description is incomplete. It lacks details on output format, error cases, and behavioral constraints, making it inadequate for a tool that extracts structured data from transcripts.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate for the undocumented parameter 'transcript'. It implies the parameter is a call transcript but does not specify format, length limits, or language requirements. This adds minimal meaning beyond the schema's type definition.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'extract' and the resource 'CRM-ready structured data from a call transcript', specifying the input source and output format. However, it does not differentiate from siblings like 'analyze_call' or 'verify_claim', which might also process transcripts, leaving some ambiguity about uniqueness.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives such as 'analyze_call' or 'verify_claim'. The description mentions cost and service details, but these do not help in selecting between tools for transcript processing tasks.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions cost ('$0.020 USDC') and service ('examforge'), which adds some context, but fails to describe critical behaviors like response format, error handling, rate limits, or authentication needs for a generative 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?
The description is concise and front-loaded with the core purpose in the first sentence. The second sentence adds cost and service details efficiently, though these could be integrated more smoothly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of a generative tool with 4 parameters, no annotations, and no output schema, the description is incomplete. It lacks details on output format, error cases, and parameter semantics, making it inadequate for reliable agent use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage, the description must compensate for undocumented parameters but does not. It mentions 'up to 20 questions' which hints at 'num_questions', but doesn't explain 'topic', 'difficulty', or 'question_types'. The description adds minimal value beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Generate a full exam with up to 20 questions on any topic.' It specifies the verb ('generate'), resource ('full exam'), and scope ('up to 20 questions on any topic'), though it doesn't explicitly differentiate from sibling tools, which appear unrelated to exam generation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It mentions cost and service details, but offers no context about prerequisites, ideal scenarios, or comparisons to other tools, leaving the agent without usage direction.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions 'Cost: $0.005 USDC' which is useful financial context, and 'Service: indic' which hints at a specialized service. However, it doesn't disclose critical behavioral traits like whether this is a read-only operation, rate limits, authentication needs, error handling, or what the output format looks like.
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 concise with three short phrases that each add value: the core function, the cost, and the service identifier. There's no wasted verbiage, though it could be slightly more structured for readability.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of an NER tool with 2 parameters, 0% schema coverage, no annotations, and no output schema, the description is incomplete. It lacks information about parameter usage, output format, error conditions, and behavioral constraints that would be necessary for an agent to use this tool effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate for the undocumented parameters. The description adds no information about the 'text' or 'language' parameters beyond what the bare schema provides. It doesn't explain what text format is expected, what languages are supported, or provide any examples of valid 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 performs 'Named entity recognition' with a specific focus on 'Indian names, places, organisations, festivals', which provides a specific verb+resource combination. However, it doesn't explicitly distinguish this from sibling tools like 'detect_code_switching' or 'indic_sentiment' that might also process Indian text, missing full differentiation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It mentions 'tuned for Indian names, places, organisations, festivals' which implies a context, but doesn't specify when-not-to-use cases or name any of the 30+ sibling tools as alternatives for related tasks.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions cost and service, but lacks critical details such as rate limits, authentication needs, output format, or error handling for a mutation tool (rewriting implies content modification).
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise and front-loaded with the core purpose. The cost and service details are relevant but could be integrated more smoothly; overall, it avoids unnecessary verbosity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a mutation tool with no annotations, no output schema, and low schema coverage, the description is incomplete. It misses behavioral traits, parameter details, and output expectations, leaving significant gaps for an AI agent to use it correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate. It implies parameters (content, target styles) but does not explain their semantics, constraints, or the effect of 'preserve_facts'. The style examples are helpful but insufficient for full parameter understanding.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Rewrite content in a target writing style' with examples of styles provided. It specifies the verb (rewrite) and resource (content), but does not explicitly differentiate from sibling tools, which appear unrelated to style rewriting.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives. The description mentions cost and service details, but does not specify contexts, prerequisites, or exclusions for usage relative to other tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions a monetary cost, which is useful, but doesn't cover other critical aspects like required permissions, rate limits, response format, or whether the operation is read-only or mutative. The description is insufficient for a tool with potential financial implications.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is brief and front-loaded with the core functionality, followed by cost and service details. However, the second sentence about cost and service feels somewhat disconnected from the main purpose and could be integrated more smoothly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (competitor analysis with financial cost), lack of annotations, 0% schema coverage, and no output schema, the description is incomplete. It omits essential details like parameter meanings, behavioral constraints, and expected outputs, making it inadequate for safe and effective use by an agent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, meaning none of the three parameters (company, url, focus) are documented in the schema. The description doesn't explain what these parameters mean, their expected formats, or how they influence the scan. For example, it's unclear if 'focus' expects specific values like 'pricing' or 'features' from the listed scan areas.
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 performs a 'full competitor scan' covering pricing, features, messaging, funding, strengths, and weaknesses, which is a specific verb+resource combination. However, it doesn't explicitly differentiate from sibling tools like 'company_lookup' or 'compare_trends' that might have overlapping functionality.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It mentions a cost of $0.010 USDC and service 'compwatch', but doesn't specify prerequisites, exclusions, or recommend other tools for different scenarios, leaving the agent without contextual usage instructions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It adds some context: the cost ('$0.002 USDC') and service provider ('timezonedetective'), which are useful. However, it lacks critical details such as whether this is a read-only or mutating operation, error handling, rate limits, or what the output looks like (since no output schema exists). For a tool with no annotations, this is insufficient.
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 highly concise and front-loaded: it states the core purpose in the first clause, followed by cost and service details. Every sentence earns its place with no wasted words, making it easy to scan and understand quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (involving timezone calculations and scheduling), lack of annotations, and no output schema, the description is incomplete. It omits details on output format, error cases, behavioral traits, and parameter semantics. While concise, it doesn't provide enough context for an agent to use the tool effectively without guesswork.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, meaning parameters are undocumented in the schema. The description mentions '2-5 locations' and 'working hours,' which loosely maps to 'timezones' and implies 'work_hours_start/end,' but it doesn't explain parameter meanings, formats (e.g., timezone strings like 'UTC'), or constraints beyond the array size. It fails to compensate for the lack of schema documentation.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Find working hours overlap between 2-5 locations for scheduling.' It specifies the verb ('Find'), resource ('working hours overlap'), and scope ('between 2-5 locations'). However, it doesn't explicitly differentiate from sibling tools like 'convert_timezone' or 'detect_timezone', which might have related but distinct purposes.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides minimal guidance: it implies usage for scheduling with 2-5 locations, but offers no explicit when-to-use rules, exclusions, or alternatives. For example, it doesn't clarify when to use this versus 'convert_timezone' or other time-related siblings, nor does it mention prerequisites or constraints beyond the basic scope.
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 discloses cost ('$0.001 USDC') and service provider ('timezonedetective'), which are useful behavioral traits. However, it lacks details on error handling, rate limits, or authentication needs, limiting transparency for a tool with no annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise and front-loaded with the core functionality. The additional cost and service details are relevant but could be more integrated. It avoids unnecessary verbosity, though it could be slightly more structured for clarity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no annotations, 0% schema coverage, and no output schema, the description is incomplete. It lacks details on parameter formats, return values, error cases, and usage context. For a tool with three required parameters and no structured support, this leaves significant gaps for an agent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate. It mentions 'datetime', 'from_tz', and 'to_tz' implicitly but does not explain their semantics, formats, or examples. This leaves parameters largely undocumented, failing to add meaningful value beyond the bare 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's purpose: converting a datetime from one timezone to another. It specifies the verb ('convert') and the resource ('datetime'), but does not distinguish it from sibling tools like 'detect_timezone' or 'find_timezone_overlap', which prevents a score of 5.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It does not mention sibling tools like 'detect_timezone' for identifying timezones or 'find_timezone_overlap' for comparing timezones, leaving the agent without context for tool 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 the full burden of behavioral disclosure. It adds useful context: the cost ($0.003 USDC) and service type ('indic'), which are not in the schema. However, it doesn't describe output format, error handling, rate limits, or performance characteristics, leaving gaps for a tool with no output schema.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise and front-loaded with the core purpose. The cost and service details are relevant but could be structured more clearly. It avoids redundancy, though it could benefit from better organization (e.g., separating functional and operational details).
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity (code-switching detection with 2 parameters), no annotations, 0% schema coverage, and no output schema, the description is incomplete. It lacks details on parameter usage, output format, error cases, and how language mix ratios are presented, making it inadequate for reliable tool invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate for undocumented parameters. It doesn't explain the 'text' parameter (e.g., expected input format or length) or 'primary_language' (e.g., what values are valid or its role in detection). The description adds no parameter-specific information beyond what's in the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: detecting Hinglish/Tanglish code-switching with language mix ratios. It specifies the verb 'detect' and the resource 'code-switching', and mentions the specific language varieties (Hinglish/Tanglish). However, it doesn't explicitly differentiate from sibling tools like 'indic_sentiment' or 'transliterate', which might also process Indic language text.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides minimal usage guidance: it mentions the service 'indic', implying it's for Indic language contexts, but doesn't specify when to use this tool versus alternatives like 'indic_sentiment' or 'transliterate'. No explicit when/when-not instructions or prerequisites are given.
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 of behavioral disclosure. It adds some context: it specifies that detection includes 'severity and correction' and mentions cost and service details. However, it lacks critical behavioral traits such as rate limits, error handling, or output format (since no output schema exists). The description does not contradict annotations, but it is incomplete for a tool with no structured behavioral data.
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 and front-loaded: the first sentence states the core purpose, and additional details (cost and service) are concise. However, the cost and service information, while potentially useful, could be considered extraneous if not directly relevant to tool selection. Overall, it is efficient with minimal waste.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity (a detection tool with behavioral nuances), lack of annotations, and no output schema, the description is incomplete. It covers the basic purpose and adds some cost/service context but misses key details like output structure, error conditions, or usage constraints. For a tool with no structured support, more comprehensive guidance is needed to aid an AI agent effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate for undocumented parameters. It does not add any meaning beyond the input schema—it does not explain what the 'argument' parameter should contain (e.g., text format, length limits) or provide examples. With one parameter and no schema descriptions, this leaves the parameter semantics largely undefined, failing to compensate adequately.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's function: 'Detect logical fallacies in an argument with severity and correction.' It specifies the verb ('detect'), resource ('logical fallacies'), and scope ('in an argument'), but does not explicitly differentiate from siblings like 'verify_claim' or 'deep_verify_claim', which might involve similar argument analysis. This makes it clear but not fully sibling-distinctive.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It mentions cost and service details ('Cost: $0.003 USDC. Service: debateclub.'), but these are not usage guidelines—they do not indicate scenarios, prerequisites, or comparisons with sibling tools like 'verify_claim' or 'analyze_call'. Without such context, users must infer usage based on the purpose alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It adds some context: cost ('$0.005 USDC') and service provider ('papertrail'), which are useful. However, it lacks critical behavioral details such as rate limits, authentication needs, error handling, or what 'filing intelligence' entails, making it insufficient for a tool with no annotation support.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise and front-loaded, stating the core purpose in the first clause. The additional cost and service details are relevant but could be more integrated. It avoids unnecessary fluff, but the structure could be slightly improved by linking the extra information more seamlessly to the tool's function.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of retrieving GST details and the lack of annotations and output schema, the description is incomplete. It doesn't explain what 'filing intelligence' includes, the format of returned data, potential errors, or usage limits. For a tool with no structured output or behavioral annotations, this leaves significant gaps for an agent to use it effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, with the parameter 'gstin' documented as a '15-character GSTIN.' The description doesn't add any meaning beyond this, such as format examples or validation rules. Since schema coverage is high, the baseline score of 3 is appropriate, as the description doesn't compensate but also doesn't detract.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Get GST registration details and filing intelligence for a GSTIN.' It specifies the verb ('Get') and resource ('GST registration details and filing intelligence'), making the function understandable. However, it doesn't differentiate from sibling tools, as none appear to be related to GST data, so a 4 is appropriate rather than a 5.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It mentions cost and service provider details, but these don't help an agent decide between this and other tools. There's no mention of prerequisites, constraints, or comparison with similar tools, leaving usage context unclear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden for behavioral disclosure. It mentions cost ('$0.010 USDC') which is valuable, but doesn't cover other critical aspects like rate limits, authentication requirements, response format, or what 'generate' entails operationally. The description is insufficient for a tool that presumably creates analysis output.
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 efficiently structured in a single sentence that states the core purpose, followed by cost and service information. Every element serves a purpose, though the cost/service details could potentially be moved to annotations for better separation of concerns.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with 4 parameters (one required), no annotations, and no output schema, the description is incomplete. It doesn't explain what the tool returns, how parameters interact, or provide enough behavioral context for safe and effective use, especially given the cost implication mentioned.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage for all 4 parameters, the description provides no information about parameter meanings. It doesn't explain what 'architecture', 'system_type', 'tech_stack', or 'data_sensitivity' represent, leaving the agent to guess based on parameter names alone without semantic context.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with specific verb ('Generate') and resource ('STRIDE threat model for any system architecture'), and distinguishes it from all sibling tools which focus on different domains like verification, analysis, or data processing rather than threat modeling.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. While it mentions cost and service, it doesn't specify scenarios, prerequisites, or exclusions, leaving the agent without context for tool selection among the many sibling options.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions cost and service, which is useful, but lacks critical details like permissions needed, rate limits, response format, or whether it's a read-only or mutation operation. For a tool with no annotation coverage, this leaves significant gaps.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise and front-loaded, stating the core purpose in the first clause. The additional cost and service details are relevant but could be integrated more smoothly. Overall, it avoids unnecessary verbosity while maintaining clarity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of contract analysis, no annotations, no output schema, and 0% schema description coverage, the description is incomplete. It omits details on response structure, error handling, and practical usage scenarios, making it inadequate for informed tool selection and invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate for undocumented parameters. It adds no information about 'clause_text' or 'jurisdiction' beyond what the schema provides (e.g., format, examples, or meaning of 'general' default). This fails to address the coverage gap, leaving parameters poorly understood.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with a specific verb ('Analyse') and resource ('a single contract clause'), specifying the outcome ('for risk and get a suggested revision'). It distinguishes itself from siblings like 'scan_contract' by focusing on individual clauses rather than entire contracts.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It mentions cost and service but doesn't specify prerequisites, appropriate contexts, or exclusions. Without this, users might misuse it or overlook better-suited tools like 'scan_contract' for broader analysis.
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 useful context about cost ($0.005 USDC) and service type ('signal'), which aren't captured in the schema. However, it doesn't describe important behavioral aspects like rate limits, authentication requirements, error conditions, or what the return format looks like beyond the high-level categories mentioned.
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 concise at two sentences. The first sentence clearly states the purpose and outputs, while the second provides operational details (cost and service). There's no wasted verbiage, though it could be slightly more structured by separating purpose from operational details more explicitly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with no annotations, no output schema, and 0% schema description coverage for parameters, the description is inadequate. While it states the purpose and some operational details, it doesn't explain the parameter semantics, doesn't describe the return format beyond high-level categories, and provides no usage guidance. The description should do more to compensate for the lack of structured documentation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage for both parameters, the description provides no information about what 'topic' and 'context' parameters should contain. The description mentions scoring 'AI trend topics' which gives some hint about the 'topic' parameter, but offers no guidance on format, examples, or what constitutes appropriate input. The 'context' parameter is completely unexplained.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Score an AI trend topic' with specific outputs (momentum, maturity, hype vs reality). It uses a specific verb ('Score') and identifies the resource ('AI trend topic'). However, it doesn't explicitly distinguish this tool from sibling tools like 'compare_trends' or 'model_threats' which might also analyze trends.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It mentions cost and service information, but doesn't indicate scenarios where this tool is appropriate or when other tools like 'compare_trends' or 'model_threats' might be better choices. There's no mention of prerequisites or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden for behavioral disclosure. It adds valuable context about cost ($0.005 USDC) and service provider (memex), which aren't captured elsewhere. However, it doesn't describe important behavioral aspects like rate limits, authentication requirements, error conditions, or what constitutes 'most relevant matches' in the search algorithm.
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 brief with three concise sentences that each add value: the core functionality, return behavior, and cost/service information. There's no wasted text, though it could be slightly more structured for readability.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a search tool with 3 parameters (2 required), 0% schema coverage, no annotations, and no output schema, the description is insufficient. It doesn't explain parameter meanings, return format, error handling, or important behavioral constraints. The cost information is helpful but doesn't compensate for the major gaps in understanding how to use the tool effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage for all 3 parameters, the description provides no information about parameter meanings. It doesn't explain what 'agent_id' refers to, what format 'query' should take, or what 'top_k' controls. The description fails to compensate for the complete lack of schema documentation.
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 performs 'semantic search across all agent memories' and 'returns most relevant matches', specifying both the action (search) and resource (agent memories). It distinguishes from siblings like 'read_memory' and 'write_memory' by focusing on search functionality rather than direct memory access. However, it doesn't explicitly contrast with all potential alternatives.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention when this search is appropriate compared to other memory-related tools like 'read_memory', nor does it specify prerequisites or exclusions. The cost information is useful but doesn't constitute usage guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It discloses persistence across sessions, cost ($0.001 USDC), and service (memex), which are useful behavioral traits. However, it lacks details on permissions, rate limits, error handling, or what happens on updates, leaving gaps for a mutation 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?
The description is brief and front-loaded with the core purpose. The cost and service details are relevant but could be more integrated. It avoids unnecessary fluff, though it could be slightly more structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a mutation tool with 5 parameters, 0% schema coverage, no annotations, and no output schema, the description is inadequate. It misses parameter explanations, behavioral details like side effects, and return values, making it incomplete for effective use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate. It does not explain any parameters (agent_id, key, value, tags, ttl_seconds), their meanings, formats, or constraints. This leaves all 5 parameters undocumented beyond their schema types.
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 ('Write or update') and resource ('persistent memory for an agent'), specifying it survives across sessions. However, it does not explicitly differentiate from sibling tools like 'read_memory' or 'search_memory', which would require mentioning it's for writing rather than reading/searching.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives such as 'read_memory' or 'search_memory'. The description includes cost and service details, but these do not help in tool selection or contextual 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 of behavioral disclosure. It adds useful context about cost ('Cost: $0.003 USDC') and service provider ('Service: papertrail'), which helps the agent understand operational aspects. However, it doesn't describe rate limits, authentication needs, response format, or error conditions, leaving important behavioral traits unspecified.
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 concise with three short phrases that each add value: the core functionality, cost information, and service provider. It's front-loaded with the main purpose. However, the cost and service details could be better integrated into the main sentence for slightly improved flow.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (3 parameters, no output schema, no annotations), the description is minimally adequate. It covers the core purpose and adds operational context (cost/service), but lacks parameter explanations, usage guidelines, and behavioral details like response format. It meets basic requirements but has clear gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description provides no information about parameters beyond what the schema already shows. With 0% schema description coverage and 3 parameters (company_name, cin, gstin), the description fails to explain what these parameters mean, their formats, or how they interact. The baseline would be 3 if schema coverage were high, but here it must compensate and doesn't.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Lookup Indian company profile and regulatory info from MCA21, GST portal.' It specifies the verb ('Lookup'), resource ('Indian company profile and regulatory info'), and data sources. However, it doesn't explicitly differentiate from sibling tools like 'gst_intelligence' or 'check_compliance', which prevents a perfect score.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It mentions cost and service details, but doesn't indicate scenarios, prerequisites, or exclusions. With sibling tools like 'gst_intelligence' and 'check_compliance' that might overlap, this lack of differentiation is a significant gap.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It discloses cost ('$0.001 USDC') and service ('memex'), which are useful behavioral traits. However, it lacks details on permissions, rate limits, error handling, or what 'persistent memory' entails. The description doesn't contradict annotations, but could provide more operational 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?
Extremely concise and front-loaded: the first sentence states the core purpose, followed by cost and service details. Every sentence earns its place with no wasted words, making it easy to scan and understand quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given 2 parameters with 0% schema coverage, no annotations, and no output schema, the description is incomplete. It covers basic purpose and cost but misses parameter meanings, return format, error conditions, and operational constraints. For a tool with persistent storage implications, more context is needed.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate. It mentions 'key' but not 'agent_id', leaving half the parameters unexplained. No details on key format, agent_id purpose, or constraints. The description adds minimal value beyond the bare 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 verb ('Read') and resource ('persistent memory'), with the specific constraint 'by exact key'. It distinguishes from sibling 'search_memory' by implying exact vs. fuzzy matching, though not explicitly named. However, it doesn't fully differentiate from all siblings, as other tools like 'analyze_call' or 'verify_claim' are unrelated.
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. The description implies it's for reading by exact key, but doesn't specify when to choose 'read_memory' over 'search_memory' or other data retrieval tools. No mention of prerequisites, exclusions, or typical use cases.
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 value by specifying the cost ('$0.0001 USDC') and service ('heatmap'), which are not in the schema, giving context about financial implications and the analytics service used. However, it lacks details on permissions, rate limits, or what happens after tracking, leaving behavioral traits partially covered.
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 highly concise and front-loaded, consisting of a single sentence that states the purpose, cost, and service. Every word earns its place with no waste, making it easy to scan and understand quickly. The structure is efficient and to the point.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of tracking API events with 4 parameters, no annotations, and no output schema, the description is incomplete. It lacks parameter explanations, usage guidelines, and details on behavioral aspects like error handling or response format. While it mentions cost and service, this is insufficient for a tool with undocumented parameters and no structured safety hints.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 0%, so the description must compensate for the lack of parameter documentation. It does not mention any parameters or their meanings, failing to add semantics beyond the bare schema. With 4 parameters (api_key, endpoint, status_code, latency_ms) and no explanation in the description, this leaves significant gaps in understanding what each parameter represents.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Track an API usage event for analytics.' It specifies the verb ('track') and resource ('API usage event'), and mentions the service ('heatmap'). However, it doesn't explicitly differentiate from sibling tools, which include various analytics and verification tools like 'analyze_call' or 'score_trend', leaving some ambiguity about its unique role.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It mentions a cost ('$0.0001 USDC') and service ('heatmap'), which hints at financial and service context, but offers no explicit when/when-not instructions or references to sibling tools. This lack of usage context makes it unclear how it fits among the many analytics-related 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 discloses cost ($0.005 USDC) and service provider ('groundtruth'), which are useful behavioral traits. However, it lacks details on rate limits, authentication needs, output format, or error handling, leaving significant gaps for a tool that likely involves external API calls and paid services.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise with two sentences: one stating the tool's purpose and features, and another providing cost and service info. It's front-loaded with the core functionality. However, the second sentence could be integrated more smoothly, and some redundancy exists (e.g., 'verification' is implied in the name).
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity (multi-source verification with analysis), lack of annotations, no output schema, and low schema coverage, the description is incomplete. It misses critical details like what the output contains (e.g., verification results, contradictions), how domain analysis works, and operational constraints (e.g., response time, error cases). The cost and service info are helpful but insufficient.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 33% (only 'domain' has a description), so the description must compensate but doesn't mention parameters at all. It adds no meaning beyond the schema, failing to explain what 'claim' should contain or how 'require_sources' affects verification depth. The baseline is 3 due to low coverage, but the description doesn't improve upon it.
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 performs 'deep multi-source verification with contradictions and domain analysis', specifying the action (verification), method (multi-source), and additional analyses. It distinguishes from the sibling 'verify_claim' by emphasizing depth and additional features. However, it doesn't explicitly contrast with other verification-related siblings like 'batch_verify'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It mentions cost and service provider but doesn't indicate scenarios where deep verification is preferred over simpler verification ('verify_claim') or batch processing ('batch_verify'), nor does it mention prerequisites or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It adds useful context about cost ('$0.010 USDC') and service provider ('contractscan'), but lacks details on permissions, rate limits, error handling, or response format. The description covers basic operation but misses deeper behavioral traits.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is highly concise and front-loaded, with two sentences that efficiently cover purpose, outputs, cost, and service. Every element adds value without redundancy, making it easy to parse quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (risk analysis with 3 parameters) and lack of annotations or output schema, the description is incomplete. It omits parameter explanations, behavioral details like error cases, and output structure, leaving significant gaps for the agent to operate effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the schema provides no parameter documentation. The description fails to compensate by explaining any of the three parameters (contract_text, jurisdiction, contract_type), their formats, or semantics. This leaves all parameters undocumented, severely hindering tool invocation.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with specific verbs ('scan a contract for risk') and resources ('contract'), distinguishing it from siblings like 'scan_clause' or 'scan_competitor' by focusing on comprehensive contract risk assessment. It explicitly lists outputs (risk score, risky clauses, red flags, missing protections), making the scope unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives like 'scan_clause' or 'check_compliance', nor does it mention prerequisites or exclusions. It only states what the tool does, leaving the agent to infer usage context without explicit direction.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It discloses cost ('$0.020 USDC') and service provider ('groundtruth'), which are useful behavioral traits. However, it doesn't cover other important aspects like authentication needs, rate limits, error handling, or what 'verify' entails operationally (e.g., what verification means in this 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?
The description is extremely concise with three short phrases, each adding value: the core function, cost, and service provider. It's front-loaded with the main purpose and wastes no words, making it efficient for an AI agent to parse.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no annotations, 0% schema coverage, and no output schema, the description is incomplete. It lacks details on what 'verify' means, the format of claims, expected output, error conditions, and how it differs from sibling tools. The cost and service info are helpful but insufficient for full contextual understanding.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate. It mentions 'claims' as the parameter but doesn't explain what constitutes a claim, expected format, or validation rules. The 'up to 10' hint partially addresses the array size, but overall, parameter semantics are minimally covered beyond what the schema's structure implies.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Batch verify up to 10 claims at once.' It specifies the verb ('verify'), resource ('claims'), and scope ('batch', 'up to 10'). However, it doesn't explicitly differentiate from the sibling tool 'verify_claim', which appears to be a single-claim version.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage context through 'batch verify up to 10 claims at once,' suggesting this tool is for multiple claims rather than single ones. However, it doesn't explicitly state when to use this versus 'verify_claim' or other verification-related tools, nor does it mention prerequisites or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It adds useful context about cost ('Cost: $0.0005 USDC') and service ('Service: ratelord'), which are not in the schema, but lacks details on permissions, rate limits, or what happens upon consumption (e.g., quota deduction effects). This provides some value but is incomplete for a mutation tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is highly concise and front-loaded, with two sentences that efficiently convey key information (action, context, cost, service) without any wasted words. Every sentence earns its place by adding distinct value.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of a quota consumption tool with 3 parameters, 0% schema coverage, no annotations, and no output schema, the description is incomplete. It lacks details on parameter meanings, behavioral outcomes (e.g., error handling), and integration with sibling tools, making it inadequate for full agent understanding.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate for undocumented parameters. It does not explain the meaning or usage of 'agent_id', 'api_name', or 'calls_used' beyond what the schema provides (e.g., types and defaults). No additional semantics are added, failing to address the coverage gap effectively.
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 ('consume rate limit quota') and the context ('after making an API call'), which specifies the verb and resource. However, it does not explicitly differentiate from sibling tools like 'check_quota' or 'track_api_event', which might handle related quota or API tracking functions, leaving some ambiguity about uniqueness.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage context ('after making an API call'), suggesting when to use it, but does not provide explicit alternatives or exclusions. For example, it does not clarify if this should be used instead of or in conjunction with tools like 'check_quota', leaving the agent to infer proper usage without clear guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It usefully mentions the cost ($0.001 USDC) and service provider, which are important behavioral traits. However, it doesn't describe error handling, rate limits, accuracy expectations, or what happens with ambiguous inputs. The description adds some value but leaves significant gaps in behavioral understanding.
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 efficiently structured in a single sentence that packs multiple pieces of information: the core function, examples of acceptable inputs, cost, and service provider. There's no wasted text, though some might argue the cost/service information could be separated for better front-loading of the core purpose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no annotations, 0% schema description coverage, and no output schema, the description provides basic but incomplete context. It covers the purpose and gives parameter examples, but doesn't explain return values, error conditions, or operational constraints. For a detection tool with monetary cost, more completeness would be expected regarding what constitutes successful detection versus failure.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 0% description coverage, so the description must compensate. It provides examples of what the 'input' parameter can contain (city, country, address, IP hint, phone prefix), which adds meaningful semantics beyond the bare schema. However, it doesn't specify format requirements, constraints, or how different input types affect results. The description partially compensates for the schema gap but not completely.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: detecting timezone from various input types (city, country, address, IP hint, phone prefix). It specifies the verb 'detect' and resource 'timezone' with examples of acceptable inputs. However, it doesn't explicitly differentiate from sibling tools like 'convert_timezone' or 'find_timezone_overlap', which would be needed for a score of 5.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It mentions the service provider and cost, but doesn't indicate scenarios where this detection tool is preferred over sibling tools like 'convert_timezone' or 'find_timezone_overlap'. There's no mention of prerequisites, limitations, or comparative use cases.
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 some context by mentioning the cost ('$0.001 USDC') and service ('indic'), which are useful for understanding operational aspects. However, it lacks details on rate limits, error handling, output format, or performance characteristics, leaving gaps in 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.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately sized and front-loaded, with the core purpose stated first. The additional cost and service details are concise and relevant. However, the lack of usage guidelines or parameter details slightly reduces efficiency, as more information could be included without sacrificing brevity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of a conversion tool with 3 parameters, 0% schema coverage, no annotations, and no output schema, the description is incomplete. It fails to explain parameter semantics, output format, error conditions, or detailed usage scenarios. The cost and service hints are helpful but insufficient for full contextual understanding.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 0%, so the description must compensate for undocumented parameters. It implies the parameters ('text', 'source_script', 'target_script') through the action described but does not explain their semantics, allowed values (e.g., specific script names), or constraints. This adds minimal value beyond what can be inferred from the schema structure.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with specific verbs ('Convert text') and resources ('between any two Indic scripts or Latin'), and it distinguishes itself from siblings by specifying its unique domain (9 Indic scripts). It explicitly mentions the service ('indic') which further differentiates it from unrelated tools like 'convert_timezone' or 'analyze_call'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It does not mention any prerequisites, constraints, or comparisons with similar tools (e.g., 'convert_timezone' for time-related conversions or other text processing tools). The cost information hints at a paid service but does not specify usage contexts or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It usefully adds cost information ('Cost: $0.001 USDC') and service provider context ('Service: phonevalidator'), which are important behavioral traits not captured elsewhere. However, it doesn't mention rate limits, authentication requirements, or error handling.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise and front-loaded with the core purpose. Every sentence earns its place: the first states the function, the second lists outputs, and the third provides cost/service context. No wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no annotations and no output schema, the description does an adequate job for a validation tool. It specifies what the tool does and what it returns, plus cost information. However, it lacks details about error conditions, response format, or limitations that would be helpful for an agent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 50% (only 'country_hint' has a description). The description doesn't add any parameter-specific information beyond what's in the schema. It mentions phone number validation generally but doesn't clarify format expectations for the 'phone' parameter or how 'country_hint' affects validation.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Validate any phone number globally' with specific outputs listed (E.164 format, country, carrier, line type). It uses a specific verb ('Validate') and resource ('phone number'), but doesn't distinguish from siblings since there are no obvious phone-related tools in the sibling list.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It mentions cost and service provider details, but doesn't indicate scenarios where this validation is appropriate or when other tools might be better suited for related tasks.
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 burden. It discloses cost ($0.001 USDC) and service provider, which are valuable behavioral traits. However, it doesn't mention rate limits, error conditions, authentication requirements, or what happens with invalid IFSC codes, leaving significant gaps for a lookup 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?
The description is appropriately concise with three short sentences that each add value: purpose, return data, and cost/service information. However, the cost information could be better integrated rather than appearing as a separate fragment.
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 lookup tool with no annotations and no output schema, the description provides adequate purpose and cost information but lacks details about response format, error handling, and usage constraints. It's minimally complete but leaves the agent guessing about important operational aspects.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already fully documents the single 'ifsc' parameter. The description doesn't add any parameter-specific information beyond what's in the schema (11-character IFSC code requirement), meeting the baseline for high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with specific verb ('Lookup') and resource ('Indian bank IFSC code'), and distinguishes it from siblings by focusing on financial code lookup rather than analysis, verification, or memory operations. It explicitly mentions the return data fields (bank, branch, address, payment support).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. While it mentions cost and service provider, it doesn't indicate prerequisites, limitations, or comparison with sibling tools like 'validate_phone' or 'gst_intelligence' that might serve related financial validation purposes.
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 discloses cost ('Cost: $0.005 USDC') and service provider ('Service: debateclub'), which are useful behavioral traits. However, it lacks details on rate limits, authentication needs, response format, or potential side effects, leaving gaps for a tool with no annotation coverage.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is highly concise and front-loaded: the first sentence states the core purpose, and the second adds essential behavioral details (cost and service). There is no wasted text, and every sentence earns its place by providing critical information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity (3 parameters, no annotations, no output schema), the description is incomplete. It covers the tool's purpose and some behavioral aspects but fails to explain parameters, return values, or operational constraints. For a tool with no structured data support, this leaves significant gaps for an AI agent to use it effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate. It does not mention any parameters or their semantics. The three parameters (claim, context, domain) are undocumented in both the schema and description, leaving their purpose and usage unclear. The description fails to add meaning beyond the bare schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Get the strongest arguments both for and against any claim.' It specifies the action (get), the resource (arguments), and the scope (for and against any claim). It also distinguishes itself from siblings like 'verify_claim' or 'deep_verify_claim' by focusing on argument generation rather than verification.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for usage: 'Get the strongest arguments both for and against any claim.' It implies this tool is for debate or analysis purposes. However, it does not explicitly state when to use this tool versus alternatives like 'verify_claim' or 'deep_verify_claim', nor does it mention any exclusions or prerequisites.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It discloses cost ($0.0005 USDC) and service provider (ratelord), which are useful behavioral traits. However, it does not mention other aspects like rate limits, permissions needed, or what happens if quota is insufficient, leaving gaps in transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded and concise, consisting of a single sentence that efficiently conveys the purpose, cost, and service. Every part earns its place without unnecessary details.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of a quota-checking tool with no annotations and no output schema, the description is incomplete. It lacks details on parameter meanings, return values, error handling, and behavioral constraints, making it insufficient for full agent understanding.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 0%, so the description must compensate. It does not explain the parameters (agent_id, api_name, calls_needed) at all, failing to add meaning beyond the bare schema. This is a significant gap given the low coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with a specific verb ('Check') and resource ('rate limit quota'), and it distinguishes from siblings by specifying the exact function of verifying quota availability before API calls. It also mentions the cost and service provider, adding specificity.
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 on when to use this tool ('before making an API call'), but it does not explicitly state when not to use it or name alternatives. It implies usage for pre-checking quota, which is helpful but lacks exclusion criteria or direct sibling comparisons.
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 burden and discloses key behavioral traits: it specifies the return format (verdict, confidence, source), cost ($0.002 USDC), and service provider (groundtruth), though it lacks details on rate limits or error handling.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with the core purpose, followed by return details and cost/service info in a single, efficient sentence with zero wasted words, making it highly concise and well-structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no annotations and no output schema, the description compensates well by explaining return values and behavioral context, though it could improve by addressing potential limitations or prerequisites for a more complete picture.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents both parameters. The description does not add meaning beyond the schema, such as examples or constraints, but does not contradict it, meeting the baseline for high coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with specific verbs ('verify a factual claim') and resources ('against real-time web sources'), and distinguishes it from siblings like 'deep_verify_claim' by specifying cost and service details, making the scope explicit.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for factual verification with cost and service context, but does not explicitly state when to use this tool versus alternatives like 'deep_verify_claim' or 'argue_claim', leaving some ambiguity in tool selection.
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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