Sats4AI

Server Quality Checklist

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Latest release: v1.0.0
Disambiguation5/5
Each tool targets a distinct action or domain, with detailed descriptions that clearly differentiate even related tools (e.g., ai_call vs. open_voice_bridge vs. place_call). No two tools have ambiguous or overlapping purposes.
Naming Consistency5/5
All tool names follow a consistent snake_case verb_noun pattern (e.g., generate_image, extract_document, send_sms). No mixing of conventions, making the set predictable and easy for agents to navigate.
Tool Count4/5
At 49 tools, the count is high but appropriate for the broad scope of the server, which covers voice, video, image, document, and communication services. Each tool earns its place, though the size may feel heavy for narrower use cases.
Completeness4/5
The tool set covers a wide range of common AI tasks and includes necessary infrastructure (payment, job status, error codes). Minor gaps exist (e.g., no tool for bulk operations or advanced audio editing), but the surface is mostly complete for the intended domain.
Average 4.4/5 across 49 of 49 tools scored. Lowest: 3.3/5.
See the Tool Scores section below for per-tool breakdowns.
- 0 of 3 community issues answered or closed in the last 6 months
- 7 commits in the last 12 weeks
- Last stable release on March 13, 2026
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is passing
This repository is licensed under MIT License.
This repository includes a README.md file.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
Tip: use the "Try in Browser" feature on the server page to seed initial usage.
This repository includes a glama.json configuration file.
If you are the author, simply .
If the server belongs to an organization, first add glama.json to the root of your repository:
```
{
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}
```
Then . Browse examples.
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Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.

To manually sync the server, click the "Sync Server" button in the MCP server admin interface.

How is the quality score calculated?

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

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

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

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

Tool Scores

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 (100 sats), payment flow (no API key), and output (download URL). However, it does not clarify whether conversion is synchronous or asynchronous, nor does it mention size limits, rate limits, or failure modes. This is adequate but incomplete.
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 front-loaded with the primary action and is reasonably concise. Each sentence adds information, though some phrasing ('Industrial-grade conversion engine') is slightly promotional. Overall efficient, earning a 4.
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 5 parameters and no output schema or annotations, the description covers payment and format details but omits the return value structure (e.g., exact JSON format) and does not clarify asynchronous behavior despite sibling tools like check_job_status existing. This leaves gaps, scoring a 3.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so baseline is 3. The description adds value by explaining the mutual exclusivity of fileBase64 and fileUrl ('provide this OR fileUrl'), which is not explicitly stated in the schema. This extra context justifies a 4.
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 'Convert files between 200+ formats' and lists specific categories (documents, images, audio, video), making the purpose evident. However, it does not explicitly differentiate from sibling tools like convert_html_to_pdf or merge_pdfs, which warrants a 4 rather than 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 mentions the payment prerequisite ('Requires create_payment with toolName=convert_file') but offers no guidance on when to use this tool versus alternatives. It does not specify preferred use cases or exclusions, leaving agents uncertain about 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, the description carries full burden. It states it's a read operation and requires no payment, which is sufficient for a simple retrieval but lacks details like caching or authentication.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, 11 words, front-loaded with purpose. Every word earns its place with no redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple retrieval tool with one parameter and no output schema, the description is adequate. It does not explain return format but the context is clear.
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 coverage is 100% and the schema already describes 'modelId'. The description adds no extra meaning beyond 'by ID', so baseline of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'Get' and the resource 'pricing for a specific model by ID', distinguishing it from siblings like 'list_models' and 'get_cost_estimate'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage when pricing info is needed and notes 'No payment required', but provides no explicit when-to-use or alternative 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?
No annotations provided, so description carries full burden. Mentions payment via Bitcoin Lightning and no account needed, but lacks details on failures, call duration behavior, or return status. Adequate but not thorough.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Three sentences: catchy intro, use case, prerequisite. No fluff, front-loaded with purpose. Excellent conciseness.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Lacks output schema and description of return value (e.g., call ID, status). For a tool with payment and telephony, expected output is important for agent workflow.
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 covers 100% of parameters with descriptions; description adds no new param details beyond restating message/audio choice. Baseline 3 justified.
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?
Clearly states the action: place an automated phone call to deliver spoken message or play audio. Distinguishes from siblings like ai_call and send_sms.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides explicit use cases (notifying humans, alerts, reaching offline) and prerequisite: requires create_payment. Does not explicitly state when not to use but gives clear context.
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, the description covers behavioral traits: async polling, payment via Bitcoin Lightning, voice tier options with rates, and translation cost. It also specifies the need for a prior create_payment call. Minor omissions: no mention of error handling or failure modes beyond polling.
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 moderately concise, packing essential information into a few sentences. It front-loads the primary purpose and then elaborates on key details. Could be slightly tighter but remains efficient.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity and lack of output schema, the description provides sufficient context for an agent to understand the workflow: asynchronous execution, polling, payment integration, and voice tier trade-offs. It does not cover all edge cases but is adequate for correct invocation.
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 coverage is 100%, so parameters are well-documented in the schema. The description adds value by explaining voice tiers and payment integration but does not provide substantial new semantics for individual parameters beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool converts books (EPUB/PDF/TXT) into full audiobooks with features like chapter detection, multi-voice narration, and translation. It distinguishes itself from sibling tools like text_to_speech by offering a comprehensive conversion pipeline.
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 creating audiobooks from text files and mentions payment requirements, but it does not explicitly state when to use this tool versus alternatives (e.g., text_to_speech) or provide constraints (e.g., file size limits).
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It discloses key behaviors: uses CodeFormer, adjustable fidelity, background enhancement, upsampling, stable endpoint with automatic model upgrades, cost (5 sats), and payment flow. Lacks details on failure modes (e.g., no face detected) or response format, but covers core traits.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is 5 sentences, each adding distinct information: purpose, model citation, fidelity, enhancements, stability and cost. Front-loaded with main action. Concise but could be slightly tighter without losing 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?
No output schema exists, yet the description does not explain what the tool returns (e.g., base64 image, JSON, or job ID). Given the complexity (6 params, no output schema) and sibling tools like get_job_result, the description should clarify the response format. This omission limits completeness.
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?
All 6 parameters have descriptions in the input schema (100% coverage). The description adds marginal value beyond the schema (e.g., reiterating fidelity balance). Baseline 3 is appropriate since the schema already explains each parameter well.
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: 'Restore blurry, damaged, or AI-generated faces to sharp, natural quality.' It specifically names the underlying model (CodeFormer) and cites performance metrics, making it distinct from general image enhancement tools. No confusion with sibling tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly mentions prerequisite: 'Requires create_payment with toolName="restore_face".' Provides context for when to use adjustable parameters (fidelity, background_enhance, etc.). However, does not directly compare with sibling tools like deblur_image or analyze_image, so some inference is needed.
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 discloses that the tool reuses the original payment and returns a call_id for polling. However, it does not mention whether it is a read or write operation or what happens on failure, leaving some behavioral gaps.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Three concise sentences cover action, prerequisite, payment implication, and return value with no wasted words. Each sentence serves a distinct purpose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description explains the prerequisite, payment reuse, and return value for polling, which is sufficient given the tool's simplicity. It lacks details on error handling or alternative flows, but these are not critical.
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 coverage is 100%, and the description adds minimal semantic value beyond the schema. It repeats the sessionId and answers descriptions but does not introduce new parameter details.
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 confirms an AI call after reviewing push-back questions, specifying the resource (AI call) and verb (confirm). It distinguishes from sibling ai_call by indicating it is used when state='pending_confirm'.
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 explicit context for when to use (when ai_call returns pending_confirm) and notes that no new payment is needed. It also mentions polling with check_job_status, but does not explicitly state when not to use it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description fully discloses async polling, payment flow, ephemeral data, language support, file size/duration limits, and privacy. This provides comprehensive behavioral insight.
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 reasonably concise and front-loaded with purpose. Every sentence adds value, though it could be slightly tighter without losing information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity (async, payment, multiple languages, file limits), the description covers all essential aspects including how to retrieve results and privacy. No output schema exists, but the workflow is fully explained.
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 coverage is 100%, so baseline is 3. The description adds value by linking paymentId to create_payment and listing supported languages, but does not elaborate on audio format requirements.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool transcribes audio to text with timestamps, using a specific verb and resource. It does not explicitly differentiate from sibling tools like 'transcribe_translate', but the purpose is unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explains when to use the tool (transcription with timestamps) and includes context on async workflow and payment. However, it lacks explicit exclusions or alternatives, leaving the agent to infer boundaries.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, but description discloses cost (7,500 sats), modes (turbo/HD), and output (reusable voice_id). Could mention limits or expiry, but sufficient.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Two concise sentences: first covers purpose and output, second adds details and prerequisite. 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?
Covers input, cost, modes, and output. Missing details on accepted audio formats, file size limits, or error handling. Adequate but not fully comprehensive.
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 covers all 4 parameters with descriptions (100% coverage). Description reinforces payment flow and model modes but adds limited new insight beyond 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?
Clearly states verb 'clone' and resource 'voice', and specifies it returns a reusable voice_id for text-to-speech. Distinguishes from sibling tools like text_to_speech.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides explicit prerequisite (create_payment with toolName='clone_voice') and payment details. Lacks exclusions or alternative tools but gives clear context.
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?
Since no annotations are provided, the description carries full burden. It discloses the underlying model (Grounding DINO), return type (bounding boxes and confidence scores), cost (5 sats), payment method (Bitcoin Lightning), and the need for no signup. It does not mention rate limits or error modes but provides sufficient behavioral context for a read-only detection 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 concise, with 3 sentences front-loading the core function. Every sentence provides value: tool capability, model name, output type, pricing, and prerequisite call. No extraneous information.
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 complexity (5 parameters, no output schema, no annotations), the description covers the model, pricing, and input requirements well. However, it lacks details on output format (e.g., bounding box coordinates) and error handling (e.g., what if payment is unpaid or detection fails). The absence of an output schema increases the need for such description.
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 for all 5 parameters, so the baseline is 3. The description does not add further detail about parameters beyond what the schema already provides (e.g., box_threshold, text_threshold). It adds context about the model and pricing but not on parameter meaning.
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 detects and locates objects in an image using Grounding DINO, specifying the action 'detect and locate', the resource 'objects in an image', and the natural language query mechanism. It distinguishes itself from sibling tools like 'analyze_image' by focusing on structured bounding box outputs.
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 explains when to use the tool ('describe what to find in natural language'), the pricing model ('5 sats per image'), and the prerequisite of calling 'create_payment' with a specific toolName. However, it does not explicitly mention when not to use it or alternatives among the many sibling tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Discloses model version (Music-2.6), output format (MP3 URL), cost (300 sats), and payment flow. However, does not specify if the operation is synchronous or asynchronous, potentially conflicting with the presence of check_job_status.
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?
Five sentences, front-loaded with key purpose, no redundant information. Efficiently conveys all critical details.
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?
Covers output and payment but omits details on job status polling or error handling, which may be needed given the complexity. The immediate 'Returns MP3 URL' statement may mislead if tool is actually asynchronous.
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 coverage is 100%, so description adds marginal value. It reinforces prompt structure (BPM, key, genre) and lyrics section tags, but does not significantly extend beyond schema descriptions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states 'Generate full songs' with specific details like duration, AI vocals, and BPM/key control, distinguishing it from sibling tools like generate_image or generate_text.
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?
It explains payment requirements and prerequisite call to create_payment, and mentions parameters for customization. Lacks explicit when-not-to-use guidance but context with siblings is clear.
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?
Despite missing annotations, the description discloses important behavioral traits: per-character pricing, automatic model upgrades, stable endpoints, and the need for a paid paymentId. However, it omits details like rate limits, latency, or failure modes, which would benefit an AI agent.
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 fairly long but well-structured and front-loaded with purpose. Each sentence adds informational value, though some repetition (e.g., multiple model listings) could be streamlined without loss.
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 8 parameters and no output schema, the description covers input semantics, model options, pricing, and payment flow adequately. It lacks explanation of the response format, but for a text generation tool this is acceptable as the agent can infer the output from the prompt.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so baseline is 3. The description adds significant value by explaining model IDs (e.g., id=6 for best), that imageBase64 works only with the best model, and that fileContext is for document Q&A. This enriches parameter understanding beyond schema descriptions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states 'Generate text using frontier AI language models,' specifying the verb (generate) and resource (text). It distinguishes from sibling tools like generate_image or generate_video, and provides additional details about model capabilities and pricing.
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 explains that the tool generates text using frontier models and requires payment via create_payment with specific parameters. It does not explicitly contrast with similar tools like ai_call, but it clarifies prerequisites and the no-signup usage model.
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. It discloses the underlying models (Grounding DINO, Bria Eraser), pricing (15 sats, Bitcoin Lightning, pay-per-request), and the payment workflow. It does not detail output format or error handling, but the key behavioral traits are transparent.
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 well-structured paragraph that front-loads the main purpose, followed by technical details and pricing. It is concise but could be broken into bullet points for better readability. No unnecessary repetition.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has 5 parameters (3 required), no output schema, and no annotations, the description covers the main behavior, technical approach, and payment model. It does not describe the output format, but the overall completeness is adequate for an AI agent to invoke the tool correctly.
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 coverage is 100% with descriptions for all parameters. The tool description adds context for 'query' ('just say person, car, or watermark') and explains the confidence thresholds, but this adds limited value beyond the schema. Baseline is 3 due to 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 action ('remove unwanted objects'), the input method ('by describing what to remove, no mask needed'), and the resource ('from images'). It distinguishes itself from sibling tools like 'remove_background' and 'edit_image' by specifying text-based object removal without masks.
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 explains when to use the tool (to remove objects by text description) and mentions a key prerequisite: 'Requires create_payment with toolName=remove_object.' It does not explicitly state when not to use it or list alternative tools, but the context provides adequate guidance for most use cases.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description discloses plain text only, max lengths (10k body, 200 subject), cost (200 sats), payment method (Bitcoin Lightning), no SMTP/domain needed, and the prerequisite of create_payment. With no annotations, this provides good behavioral context.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise, front-loaded with purpose, then constraints, then payment details. Every sentence adds value without redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description covers prerequisites, constraints, and use cases, but does not mention return values or error handling. No output schema exists, so more context on expected output or errors would improve completeness.
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 coverage is 100% with clear descriptions. The description adds context about the payment flow (requires create_payment with toolName='send_email') and reiterates limits, but does not significantly enhance parameter meaning beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states that the tool sends an email ('Reach anyone with an email address') and specifies its use for formal communication, reports, and contacting people outside chat, distinguishing it from siblings which are mostly multimedia tools.
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?
It explicitly mentions when to use this tool (formal communication, reports, contacting outside chat) and implies that it's for email, but does not exclude alternatives or explain when not to use it. Given no other email tool exists, this is adequate.
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. It discloses the algorithm (Real-ESRGAN), model version (ICCV 2021), performance metric (PSNR 32.73dB), optional face enhancement, pricing (5 sats), payment method (Bitcoin Lightning), and that the endpoint is 'stable' with automatic model upgrades. It does not detail error handling or async behavior, but the disclosure is substantial for an API tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is five sentences, each adding distinct value: purpose, technical credibility, differentiation from interpolation, optional feature, stability, pricing, and prerequisite. There is no fluff; every sentence earns its place. The structure front-loads the core action and then provides essential details.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description lacks explicit mention of the output format (e.g., base64 image) or whether the operation is synchronous/asynchronous. Given no output schema, the agent must infer the return type. While the tool's behavior is well-described, the missing output details reduce completeness for a tool with moderate complexity and no output schema.
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?
Input schema has 100% coverage with descriptions for all 4 parameters. The description adds context (e.g., 'optional face enhancement' for face_enhance, '2x or 4x' for scale, and payment context for paymentId). However, it largely mirrors the schema information; the added value is moderate. Thus a baseline 3 is appropriate given 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 primary function: 'Upscale images 2x or 4x with neural super-resolution'. It specifies the resource (images), verb (upscale), and scope (2x or 4x), and distinguishes from 'interpolation' and other image operations. Among siblings like colorize_image or deblur_image, the purpose is distinct and unmistakable.
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 includes explicit prerequisite guidance: 'Requires create_payment with toolName="upscale_image"'. It also explains the payment model and no signup needed. However, it does not explicitly compare to alternative tools (e.g., when to use upscaling vs. other image enhancements), though the context of 'neural super-resolution recovering real detail' implies it is for low-resolution images.
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, the description provides key behavioral details: model (DDColor), pricing (5 sats, Bitcoin Lightning), and prerequisite payment. It lacks output format and potential limitations but is otherwise informative.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Four sentences, each adding distinct value: purpose, model, pricing, prerequisite. No wasted words, well-structured, and front-loaded.
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?
Covers purpose, model, pricing, and prerequisite adequately. Missing output format (e.g., base64 image) and potential limits, but acceptable for a tool with payment integration and no output schema.
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 coverage is 100%, so parameters are already documented. The description adds context for paymentId (requires create_payment) but does not significantly enhance understanding of imageBase64 or model_size beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description explicitly states 'Colorize black-and-white or grayscale photos,' identifying a specific verb and resource. No sibling tool duplicates this functionality, so it clearly distinguishes itself.
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?
It notes that this tool is the only way to colorize (impossible for LLMs) and requires a prerequisite call to create_payment. However, it does not explicitly list exclusion criteria or alternative tools, but the context implies uniqueness.
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, the description effectively conveys key behavioral traits: async execution (returns requestId, poll with check_job_status), pricing per second, duration constraints, and dependency on payment. It does not cover error handling or full output format, but adds substantial value beyond the 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 a single, dense paragraph that front-loads the core function. It covers purpose, features, async, pricing, and dependencies in about 5 sentences with no redundancy. Could benefit from bullet points for readability, but remains concise.
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 complexity (6 params, async, no output schema), the description covers input constraints, pricing, and async polling. However, it does not describe the output format or error codes, leaving an incomplete picture. The polling hint partially mitigates, but a full output description is needed.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Input schema has 100% description coverage, so baseline is 3. The description enhances parameter semantics by explaining pricing implications of duration and mode, and noting that modelId is optional with a default. It also clarifies the payment flow via create_payment.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states 'Generate a video from a text prompt', specifies the model (Kling v3), and mentions key features like quality modes, audio, and async nature. It distinguishes from sibling tools like generate_image or animate_image by focusing on video generation from text.
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: when to use (for generating videos from text), payment prerequisites (requires create_payment), and polling (check_job_status). However, it lacks explicit comparisons to alternatives or exclusions, leaving some ambiguity against similar tools like animate_image.
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?
Despite no annotations, the description discloses that the tool returns partial and final transcripts along with system events, and clarifies it fetches new events since last cursor. It also notes cost implications ('No additional payment'), though it lacks details on error handling or required call state.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Three concise sentences with no wasted words. First sentence provides core purpose, second lists return types, third gives usage guidance and cost info. Front-loaded and efficient.
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 output schema, the description adequately covers return types and polling behavior. It assumes the agent knows about open Voice Bridge calls (from sibling tools), which is reasonable. Minor gap: no mention of errors or prerequisites.
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?
Input schema already describes both parameters well (sessionId and cursor with defaults). The description adds minimal extra value beyond restating the cursor purpose, so baseline 3 is appropriate given 100% 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?
Clearly states the tool fetches new transcript events from an open Voice Bridge call, specifying the verb 'Fetch' and the resource. It distinguishes itself from siblings like open_voice_bridge and end_voice_bridge by focusing on incremental event retrieval.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly recommends polling in a loop with a suggested interval (500ms-1s) and mentions no additional payment, which helps the agent decide usage. However, it does not explicitly state when not to use it or list alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Discloses model (Qwen3-32B), auto-detection of source language, privacy (no data stored), and pricing (1 sat per 1000 chars, min 1 sat). Without annotations, description carries full burden; these details are sufficient. Could mention if synchronous or result format, but still strong.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with purpose and key details, but it redundantly lists all 119 languages already present in schema enums. This makes it verbose. Overall structure is logical otherwise.
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?
Covers primary aspects: function, payment flow, parameters, supported languages, privacy, pricing. Missing output description and potential limitations (e.g., max text length). No output schema exists, so description should mention return format.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, but description adds value: explains source language is optional (auto-detect), paymentId from create_payment, modelId optional. Also notes that language params accept English names or ISO codes, which is not in 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 'Translate text across 119 languages' with high accuracy, specifying the model and auto-detection. It distinguishes from siblings like transcribe_translate (audio translation) and generate_text (text generation) by focusing solely on text-to-text translation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides guidance on when to use: privacy-preserving, pay-per-request with Bitcoin Lightning, no signup. Mentions prerequisite step (create_payment). However, does not explicitly compare to alternative translation tools or state when not to use it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
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 key behaviors: hanging up, billing finalization, refund link generation, and transcript return. However, it does not mention idempotency, irreversibility, or error states, which would be helpful.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences with no wasted words. All information is front-loaded and essential. The description is concise and structured well.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (1 param, no output schema, no annotations), the description covers the main function and outputs. It could explicitly note that the session must be active, but overall it is sufficiently complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% coverage with one parameter, sessionId, described as 'Session ID from open_voice_bridge'. The description adds context by specifying the source (open_voice_bridge), adding value beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Hang up a Voice Bridge call, finalize billing, and return a LNURL-withdraw refund link for unused deposit time. Also returns the final transcript for convenience.' It uses specific verbs and resources, and is distinct from sibling tools like open_voice_bridge and voice_bridge_say.
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 ending a call but does not explicitly state when to use this tool versus alternatives (e.g., if the call is already ended). It only mentions the session ID requirement from open_voice_bridge, providing minimal context.
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 that no payment is needed and the return type (machine-readable catalog). However, it does not mention potential side effects, caching behavior, or performance implications. For a simple read-only tool, this is acceptable but not exhaustive.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence that immediately states the purpose, followed by examples and usage guidance. Every word adds value, with no fluff.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has no parameters and no output schema, the description is complete: it explains what the tool returns (a catalog of error codes with examples) and how to use the output (branch on error_code). No additional context is necessary.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters, and the schema coverage is 100% (by default, as no parameters exist). The description does not need to add parameter semantics, so baseline 4 is appropriate.
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 retrieves a machine-readable catalog of all error codes the API can return, with specific examples (TIMEOUT, CONTENT_FILTERED, etc.). It distinguishes itself from any sibling by focusing solely on error code retrieval.
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?
It advises agents to branch on error_code rather than parsing free-text messages, which is a clear usage directive. It also notes that no payment is required. However, it doesn't explicitly mention when not to use this tool or compare to alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description bears full burden. It discloses delivery speed ('seconds'), no need for telecom accounts, payment via Bitcoin Lightning, and the auto-appended disclaimer. Missing details like error handling or delivery confirmation, but adequate for the tool's simplicity.
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 at several sentences, each adding unique information. It front-loads the primary purpose and flow, followed by constraints. No redundant or vague statements.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has 3 required parameters, no output schema, and no annotations, the description covers the workflow, prerequisites, and key constraints (matching values, disclaimer). It does not describe return values, but that is acceptable without an output schema. Overall, it sufficiently informs an AI agent for correct invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so each parameter has a description. The description adds value by clarifying that paymentId must be paid, phoneNumber/message must match create_payment, and the message length includes an appended disclaimer. This goes beyond the schema docstrings.
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 sends SMS messages for real-world coordination, distinguishing it from other communication tools like send_email or send_fax. The verb 'Reach a human via SMS' is specific and the resource is unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage 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 (real-world coordination) and prerequisites (create_payment with matching parameters). However, it does not explicitly state when not to use or name alternatives, though the sibling list implies the tool is specialized for SMS.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It discloses key behavioral traits: automatic STT muting during injection, coverage by session deposit, and the two modes. It does not cover potential errors or side effects, but the main behaviors are clear.
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 very concise: five sentences with no filler. The first sentence states the purpose, then the two modes, then the STT muting, then payment. Every sentence adds value.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given 6 parameters, no output schema, and no annotations, the description covers the core functionality well. It explains modes, muting, and cost. It could be more complete by mentioning error handling (e.g., invalid session) but is adequate for an agent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
All parameters have schema descriptions (100% coverage). The description adds significant context by explaining the two modes and how parameters relate (e.g., text vs audioBase64+encoding), which goes beyond the schema's individual descriptions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
Description starts with a clear action verb ('Inject audio into an open Voice Bridge call') and specifies two distinct modes (TTS or custom audio). It differentiates from sibling tools like open_voice_bridge and end_voice_bridge by focusing on injecting audio into an existing call.
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 explains that STT is muted automatically and that no extra payment is needed. However, it does not explicitly state when to choose mode 1 vs mode 2, nor does it provide any 'when not to use' guidance or alternatives. Usage is implied but not fully specified.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must carry the full burden of behavioral disclosure. It discloses payment requirement, model used, and capabilities, but does not detail failure modes, rate limits, or whether the operation is read-only (assumed from analysis). The cost and model information are valuable behavioral traits.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is relatively concise (four sentences) and front-loaded with the core purpose. However, the sentence 'Uses Qwen VL — multimodal vision-language model with strong OCR, chart reading, and spatial reasoning.' could be slightly more concise without losing meaning. Overall, it efficiently communicates key information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has 4 parameters (3 required) and no output schema, the description covers essential aspects: purpose, prerequisites (payment flow), model details, and parameter notes. It provides sufficient context for an AI agent to decide when and how to invoke the tool correctly, including the mandatory payment step.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% (all parameters have descriptions). The description adds value beyond the schema by explaining the payment requirement for 'paymentId', noting that 'modelId' is optional, and mentioning the model's capabilities (Qwen VL) which helps interpret the 'prompt' parameter. This enhances understanding beyond the schema alone.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description explicitly states the tool's purpose: 'Analyze and describe image content, answer visual questions, extract information from screenshots or photos.' It also specifies the underlying model (Qwen VL) and its capabilities (OCR, chart reading, spatial reasoning), making it distinct from sibling tools like 'detect_objects' or 'generate_image'.
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 clearly states the prerequisite: 'Requires create_payment with toolName='analyze_image'.' It also mentions cost (21 sats) and that no API key is needed. However, it does not explicitly differentiate when to use this tool versus other image-related tools (e.g., 'detect_objects', 'generate_image'), relying on the purpose to imply appropriate contexts.
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?
Despite no annotations, the description discloses key traits: async polling, cost per second, duration range, Bitcoin Lightning payment, no API key needed. Lacks details on failure modes or image constraints, but covers major aspects.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Three sentences, each providing distinct value: purpose, async nature, cost, and payment. No redundant or vague statements; highly efficient.
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?
Covers purpose, inputs, prerequisites, async behavior, cost, and duration. Lacks output format details (no output schema) and potential error conditions, but sufficient for an async tool with polling.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema already has 100% description coverage; the tool description adds value by explaining payment flow (create_payment with toolName) and duration range. This extra context aids the agent beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool converts a still image to video with text guidance, using a specific model. It distinguishes from siblings like generate_video by specifying 'animate a still image' and preserving source fidelity.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides clear context: async behavior, prerequisite create_payment, and payment method. Does not explicitly list when not to use or alternatives, but the context is sufficient for typical usage.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description fully carries behavioral disclosure. It reveals the output format (classification label and is_nsfw boolean), cost (2 sats per image), and payment mechanism (Bitcoin Lightning, no signup). It does not cover error handling or rate limits, but the core behaviors are transparent.
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 four sentences, each adding distinct value: purpose, cost advantage, return format, and payment instructions. It is front-loaded with the core action and avoids redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the simplicity of the tool (two parameters, no output schema), the description covers all critical aspects: purpose, usage context, behavioral traits, and parameter semantics. It could mention error cases, but it is largely complete for a moderation tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% coverage with descriptions for both parameters. The description adds context by explaining how to obtain a paymentId (via create_payment with this tool name), which enhances the schema. No further parameter detail is needed.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: classifying image safety into three categories (normal, suggestive, explicit). It distinguishes itself from siblings by emphasizing cost and speed advantages over LLMs, which is a unique selling point.
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 guidance on when to use the tool, calling it 'essential for content moderation pipelines.' It also explains the prerequisite payment flow (create_payment with toolName='detect_nsfw'), but does not explicitly name alternative tools or state when not to use it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden. It discloses model used, output format (JSON with image URL), pricing per resolution, and payment requirement. It does not cover rate limits, error responses, or image size limits, but covers key operational aspects.
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?
Description is a single paragraph, efficient and front-loaded with purpose. Each sentence adds value: model, capabilities, output, pricing, payment prerequisite. No wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool has 6 parameters (3 required) and no output schema. Description explains payment dependency and pricing but only briefly mentions 'Returns JSON with image URL'. Could elaborate on error scenarios or response structure. Otherwise, sufficient for a paid editing tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% so baseline 3. Description adds value by detailing resolution-to-pricing mapping and the need for create_payment. This extra context clarifies the paymentId parameter's origin and the cost implications of resolution.
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 edits images with natural language instructions, specifying capabilities like object addition/removal, style transfer, and inpainting. It distinguishes itself from siblings like generate_image or remove_background by focusing on editing an existing image with language.
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 explains the payment flow (create_payment with toolName and resolution) and pricing tiers. It does not explicitly compare to alternatives but the capabilities are clear. It could mention when to prefer this over other image tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Without annotations, the description covers the OCR engine (Mistral), handling of complex layouts, cost (10 sats/page), and the payment flow. It does not mention limitations like file size, but the core behavior is well-disclosed.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Four sentences, each adding essential information. Front-loaded with the main action, then details. No filler or redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description covers the output (Markdown), payment flow, and OCR capabilities. It lacks mention of error handling or file size limits, but given schema coverage and no output schema, it is reasonably complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
All parameters have schema descriptions, but the description adds value by clarifying the payment prerequisite, explaining that paymentId must be paid, and noting modelId is optional. This goes beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it extracts text from PDFs and images as clean Markdown. It specifies the verb 'extract', the resource 'PDFs and images', and the output format, making the purpose unmistakable.
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 explains the prerequisite of creating a payment via create_payment with specific parameters, and notes that no API key is needed. It doesn't explicitly list when not to use it, but provides clear context for using it correctly.
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, the description carries the full burden. It discloses key behaviors: async operation (returns requestId), cost per model, payment requirement with Bitcoin Lightning (no API key needed), and the use of Seed3D technology. It does not cover error handling or rate limits, but the main behavioral traits are well specified.
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 concise (4 sentences) and front-loaded with the main action. Every sentence adds distinct information: purpose, technology, async nature, cost, payment method, and prerequisite. No wasted words, efficient and clear.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (3 params, async, payment flow) and no output schema, the description covers the primary use case, async polling, cost, and payment prerequisite. It sets expectations for output (GLB model, requestId) but omits error handling details. Overall, it is sufficiently complete for an agent to use correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so baseline is 3. The description adds value beyond schema: it clarifies that paymentId must be a paid valid ID (referencing create_payment), that imageBase64 should be a single photo in valid formats, and that modelId can be omitted for default. This enriches parameter understanding.
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: 'Convert a single photo into a textured 3D GLB model.' It uses a specific verb (convert) and resource (photo to 3D model), and distinguishes from siblings by noting async behavior, cost, and payment requirement, which are unique among sibling tools.
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 explicit context: when to use (to generate a 3D model from a photo), async nature (poll with check_job_status), cost (350 sats), and payment method (requires create_payment). It does not explicitly state when not to use or list alternatives, but given no similar sibling tools, the guidance is clear and actionable.
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, the description carries full burden. It discloses behavioral traits: stable endpoints, automatic model upgrades, pay-per-request with Bitcoin Lightning, and no signup needed. It also mentions return format (JSON with image URL). This is sufficient for safe invocation.
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 5 sentences and front-loaded with the main action. Every sentence adds unique value, though it could be slightly more concise. Still, it's well-structured and easy to parse.
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 4 parameters, no output schema, and no annotations, the description covers the essential aspects: payment requirement, model choices, img2img, automatic upgrades. It lacks mention of error handling or rate limits, but is fairly complete for a tool of this complexity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, but the description adds significant value beyond the schema: it explains the payment flow (requires create_payment with specific toolName), elaborates on model options with pricing and defaults, and clarifies the img2img use case for imageBase64. This makes the parameters much more understandable.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states 'Generate an image from a text prompt' and lists available models with their characteristics. It distinguishes this tool from siblings like analyze_image or edit_image by specifying its core function.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides context on when to use the tool (generating images), mentions img2img capability, and explains the payment prerequisite. However, it doesn't explicitly state when not to use it compared to other image tools on the server.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, but the description discloses async behavior (returns requestId, poll with check_job_status), flat pricing covering STT and translation, payment requirement, and source auto-detection. It could mention file size limits or supported audio formats, but overall transparency is high.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Front-loads key information (compound endpoint, use case). Some redundancy (e.g., 'Pay with Bitcoin Lightning — no API key or signup needed' could be shorter), but overall efficient and well-organized.
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?
No output schema, but description explains return type (requestId) and how to retrieve result (poll). Covers payment, async nature, source/target languages, and cost comparison. Missing details on output format (e.g., transcript structure) but given complexity, it's reasonably complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so baseline is 3. Description adds value: explains paymentId requires a paid payment, audioBase64 is base64-encoded, targetLanguage supports English name or ISO-639 (119 languages), sourceLanguage is optional with auto-detection and lists the 13 supported languages. This is meaningful enrichment.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it is a compound endpoint that transcribes audio in 13 source languages and translates to 119 target languages. It distinguishes itself from siblings like transcribe_audio and translate_text by highlighting the combined offering and cost savings.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides concrete examples (WhatsApp voice messages, meeting recordings), notes auto-detection of source language, async polling via check_job_status, cost comparison to alternatives, and prerequisites (create_payment). This gives explicit guidance on when and how to use.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Discloses idempotence, safety, and no payment required. Also mentions sort order. No annotations provided, so description carries the full burden and does so adequately.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Four focused sentences, front-loaded with purpose, then output format, then guidance. No unnecessary words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With no required params and no output schema, the description covers output format, usage guidance, safety, and idempotence fully. It is complete for a simple list tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
No parameters exist, schema coverage is 100%. Description adds value by explaining output format and usage, which goes beyond the empty 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?
Explicitly states it lists planned services with vote counts, returns a JSON array with specific fields, and differentiates from sibling vote_on_service by indicating that the slug values can be used with that tool.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides clear usage guidance: no payment required, free discovery tool, and how to use results with vote_on_service. Lacks explicit when-not-to-use but is implied by the free nature.
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, the description fully discloses behavioral traits: preserves bookmarks/links/formatting, returns a temporary URL (valid ~1 hour), cost, payment method (Bitcoin Lightning), and that no API key is needed. It does not mention error handling but is otherwise thorough.
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 moderately concise, containing multiple sentences but each adds value. It front-loads the core purpose and then adds details. Could be slightly shorter but no redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the lack of output schema, the description explains the return format (JSON with url). It covers constraints, payment, ordering, and provides an alternative tool. The information is sufficient for an agent to correctly invoke the tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema already provides 100% coverage for the two parameters. The description adds context: paymentId must be a valid paid payment (implied from create_payment), and files are base64-encoded PDFs. This goes beyond the schema's descriptions, justifying a score above the baseline.
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 merges PDF files into a single document, preserving bookmarks, links, and formatting. It explicitly distinguishes itself from the sibling tool 'convert_file' by specifying the alternative for format conversion.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit usage guidelines: minimum 2 files, no maximum, files concatenated in array order, and a clear alternative (convert_file) for format conversion. It also explains the payment requirement and the process with create_payment.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description fully explains behaviors: 24h window, caller ID matching, email delivery, optional OCR with cost and partial refund on failure, callback webhook, no refund if no fax arrives, and Bitcoin Lightning payment. No contradictions with 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 several sentences long but every sentence adds value, covering use case, mechanism, optional features, and policy. It is well-structured and informative, though could be slightly more concise.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the lack of an output schema, the description explains the outcome (fax delivered to email, optional OCR file, webhook) but does not specify the immediate return value of the tool (likely a job ID or status). It covers all key aspects for a receive tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
All 6 parameters are described in the schema (100% coverage), and the description adds significant additional meaning: explains fromNumber matching, OCR cost and failure handling, callback_url for webhook, and paymentId prerequisite. This goes well beyond the schema descriptions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool is for receiving faxes, using a specific shared number with a 24-hour window. It distinguishes itself from sibling tools like send_fax by focusing on inbound reception.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly says when to use (expecting a fax back) and provides context like caller ID matching and email delivery. It does not explicitly exclude other use cases or recommend alternatives, but the scenario is clearly defined.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It details the underlying model (BiRefNet, state-of-the-art), capabilities (handles complex edges), endpoint stability, pricing (5 sats per image), and payment method (Bitcoin Lightning, no API key). This is highly transparent.
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 concise (5 sentences) and well-structured: first line states purpose, then technology, capabilities, then pricing and prerequisite. Every sentence adds value with no redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema, the description adequately states the return type ('transparent PNG'). It covers payment flow, image formats, and model details. Minor omissions like size limits or timeouts do not detract significantly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema covers both parameters with descriptions. The description adds context beyond the schema: it explains that paymentId requires a prior call to create_payment with toolName='remove_background', and imageBase64 supports data URI. This adds meaningful value.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Remove background from any image, returning transparent PNG.' It specifies the action (remove background) and resource (image), and is distinct from sibling tools like edit_image or colorize_image.
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, including the requirement to call create_payment beforehand. It does not explicitly state when not to use this tool or mention alternatives, but the context is sufficient.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations exist, so the description fully discloses behavior: async polling via check_job_status, auto-retry on voicemail (up to 3), state='pending_confirm' requiring confirm_ai_call, cost range (~150-250 sats), languages, and payment method (Bitcoin Lightning). This is comprehensive.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is lengthy (multiple paragraphs) but front-loaded with key purpose and usage. Every sentence adds value, making it more informative than verbose. However, it could be slightly more concise without losing detail. Still, it is well-structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema and complexity (6 params, async, state machine, payment prerequisite), the description fully covers the workflow: async polling, required confirm_ai_call on pending_confirm, cost, languages, and exclusions. It leaves no critical gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, baseline 3. The description adds value beyond schema for the 'language' parameter (explains guessing logic and override cases) and 'task' (what constitutes instructions). The 'beginMessage' is also clarified. However, the schema already describes parameters well; the description provides incremental 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: 'send an AI voice agent to handle the call' for human-interaction tasks like booking, negotiating, and IVR navigation. It distinguishes from sibling tools (open_voice_bridge, place_call, send_sms) by specifying when NOT to use each alternative.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly provides when-to-use (human-required tasks) and when-not-to-use scenarios, naming specific sibling tools as alternatives (open_voice_bridge, place_call, send_sms). Also mentions prerequisite create_payment. This is exemplary guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Describes response format with statuses, progress for epub-audiobook, and that it is free. No annotation provided, so description carries full burden. Missing auth or rate limit details, but overall transparent.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Well-structured with purpose first, then usage details. Slightly verbose but each paragraph adds value.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Complete for a polling tool: covers all job types, next steps, progress details for specific case. No missing information for correct invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Adds significant meaning beyond schema: explains requestId comes from async tools and gives mapping for jobType enum to tool names. Schema coverage is 100%, description enhances usability.
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?
Clearly states 'Poll the status of an async job' with specific verb and resource. Lists which tools are async, distinguishing from synchronous siblings.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly tells when to use (after async tools returning requestId), polling interval (5-10 seconds), and what to do on completion (get_job_result) or failure (do not retry). Also advises against use for synchronous tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Discloses return JSON structure, expiry behavior, and that the tool is free and non-destructive. No annotations to contradict.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Four concise sentences with no redundancy; first sentence states the core purpose immediately.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Despite no output schema, description fully explains return format, lifecycle context (expiry, follow-up steps), and tool cost; complete for a simple check tool.
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 already describes the parameter fully (the paymentId from create_payment). Description adds minimal extra meaning beyond the schema, so baseline 3.
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?
Clearly states the tool checks if an invoice is paid, specifies return format with three statuses, and distinguishes from siblings by noting it is called after create_payment.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides explicit instructions: call after create_payment, before target tool; mentions 10-minute expiry and action if expired; notes instant payment in most clients.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description fully discloses behavior: processing time (~2 min), cost (20 sats), payment via Lightning, model details, and requirements (payment from create_payment). This adequately informs the agent of all relevant traits.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is somewhat lengthy but every sentence adds unique value, covering purpose, limitations, alternatives, pricing, and payment flow. It is front-loaded with the core functionality, though minor trimming could improve clarity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description provides comprehensive context given no output schema: it explains processing time, cost, payment flow, model performance, and limitations. It lacks explicit return format but the tool's output can be inferred (a deblurred image).
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
All three parameters are described in the schema (100% coverage). The description adds value by explaining the difference between option enum values, default behavior, and the paymentId requirement, enhancing understanding beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool recovers detail from camera-shake and accidental motion blur, specifies the model and benchmarks, and explicitly lists what it is not effective for, making it highly specific and distinct.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicit when-to-use and when-not-to-use conditions are provided, including best uses (handheld shake, bumped camera) and ineffective cases (panning blur, bokeh). Also mentions denoising support and payment requirements, guiding appropriate invocation.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It discloses the dual-model pipeline (Mistral OCR + Kimi K2.5), capabilities (crumpled, faded, multi-page), pricing model, payment flow (requires create_payment), and authentication method (Bitcoin Lightning, no API key). This provides comprehensive behavioral insight.
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 somewhat lengthy but each sentence adds value. It is well-structured with clear information about purpose, process, and requirements. A slight reduction in wordiness could improve conciseness without losing essential detail.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given there is no output schema, the description explains the return format (JSON with merchant, date, line items, etc.) and the overall pipeline. It covers cost, payment method, and limitations. For a 2-parameter tool, this is highly complete and leaves minimal ambiguity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with both parameters described. The description adds meaningful context: paymentId must be a valid paid payment from create_payment with toolName='extract_receipt', and documentBase64 is a base64-encoded image or PDF. This goes beyond the schema definitions.
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 'Extract structured data from receipts, invoices, and financial documents' and lists specific output fields (merchant, date, line items, etc.). It distinguishes itself from siblings like 'extract_document' by focusing on financial documents and mentioning the dual-model pipeline.
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 usage context: handles crumpled receipts, faded text, and multi-page invoices. It also explains pricing (50 sats/page) and payment requirements. However, it does not explicitly tell when not to use this tool or compare with similar tools like 'extract_document'.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. It states 'No payment required, no side effects' and 'Returns { amount_sats, breakdown, currency },' fully disclosing behavior and return value.
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?
Five sentences, each serving a purpose: purpose, use case, examples, return value, and omitting behavior. No redundancy, fully front-loaded.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Despite no output schema, the description covers return format, side-effect safety, and parameter usage patterns. For a query-only tool, this is complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, but description adds value by explaining parameter combinations (e.g., 'chars for TTS and translate') and the 'omit to list all' pattern, going beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states 'Get an exact sat cost quote for a service BEFORE creating a payment.' It specifies the verb, resource, and purpose, distinguishing it from sibling tools like create_payment.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly says 'Useful for budget-aware agents to price-check before committing.' It provides concrete examples (e.g., 'Pass service=text-to-speech&chars=1500') and notes that omitting params lists all services. It lacks explicit when-not-to-use but is clear enough.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description bears full responsibility. It clearly states the tool is free, requires no payment, and is idempotent—safe to call repeatedly. It also describes the output format. This fully informs the agent of 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 concise at 4 sentences, front-loads the purpose, and uses a clear example of the output format. Every sentence adds essential information with no wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Despite no output schema, the description provides a full example of the return format. It covers the purpose, usage, filtering, isDefault behavior, and safety aspects. Given the tool's simplicity (one optional parameter), the description is complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with the category parameter described. The description adds the context that filtering narrows results 'to a specific tool', which clarifies the enum values correspond to tool names, adding value beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Discover available AI models with numeric IDs, tier labels, capabilities, and per-call pricing in sats.' It distinguishes from sibling tools like create_payment and get_model_pricing by specifying its role in selecting a modelId and noting it is free and idempotent.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly advises calling this tool before create_payment to find the right modelId, and explains how the isDefault flag works. It also mentions filtering by category. While it doesn't explicitly compare to other tools like get_model_pricing, the guidance is clear and practical.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Discloses session lifecycle (speak, poll, end), refund mechanism, privacy policy (transcripts in memory, 30-min GC, no audio persistence), and payment method (Bitcoin Lightning). All critical behavioral traits covered despite 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?
Long but justified given complexity; front-loaded with purpose and usage, then details. Every sentence adds value, no waste. Could slightly condense, but effective.
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?
Covers prerequisites, payment, refund, privacy, and interaction pattern. No output schema, but description sufficiently explains the tool's role in a multi-step process. Missing explicit return value, but minor.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema covers 100% of parameters, baseline 3. Description adds context for paymentId (from create_payment), phoneNumber format, durationMinutes as deposit, refundAddress for refund, and language/codec options, raising usefulness.
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?
Clearly states 'Open a Voice Bridge session: a live phone call where YOUR LLM is the brain.' with specific verb (open) and resource, and distinguishes from siblings ai_call and place_call.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly states when to use (privacy, drive turns), when not to use (managed calls, TTS broadcasts, low-value polling), and prerequisites (create_payment).
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Despite no annotations, the description thoroughly discloses behavioral traits: three tiers with specific capabilities (chars/sat, language support), adjustable speed range, return type (audio URL), and payment process (Bitcoin Lightning, no API key). Covers all critical aspects.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness2/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely verbose, containing extensive lists of voices and languages that could be referenced via schemas or separate tool calls. While well-structured with paragraphs and headers, it sacrifices conciseness for completeness.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity (7 parameters, no output schema), the description is complete: covers all tiers, parameter usage, payment, return value (audio URL), and provides exclusion criteria against sibling tools. A user can fully understand tool behavior without external references.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Adds immense value beyond the 100% schema coverage by explaining the three tiers and how they relate to parameters, providing a massive enumerated list of voices with detailed descriptions (gender, age, style), and listing all 646 language ISO codes with full names. The description enriches parameter understanding significantly.
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 converts text to speech with three distinct tiers, each described with unique features (language coverage, voice cloning, speed). It distinguishes from sibling tools like place_call and ai_call in the 'When NOT to use' section.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides explicit guidance on when to use each tier (e.g., OmniVoice for rare languages) and when not to use the tool, with references to alternative tools (place_call, ai_call, open_voice_bridge). Includes payment prerequisites.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Discloses temporary URL duration (~1 hour), cost (50 sats), and payment flow (requires create_payment). No contradictions with missing 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?
5 sentences, each serves purpose, but could be slightly tighter. Information is front-loaded with core action.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Covers output format, use cases, payment, sibling comparison, and technical requirements. No gaps for a conversion tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema covers all parameters; description adds context for format parameter (set to 'markdown' for Markdown) and explains paymentId in context. Adds value beyond 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?
Clearly states it converts HTML or Markdown to PDF, with specific output format (JSON with temporary URL). Differentiates from sibling convert_file.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly suggests use cases (invoices, reports, etc.) and directs to convert_file for file conversion. Also mentions payment requirement and setup.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. Discloses return JSON structure, invoice expiration (10 minutes), error behavior for missing required params, and pricing dependencies for each target tool. Completely transparent.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Well-structured with clear sections for purpose, return values, workflow, expiration, and per-tool requirements. Slightly long but all content is necessary; no filler.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Comprehensive coverage of all aspects: purpose, workflow, return values, expiration, error handling, and detailed conditional parameter requirements for each tool. No gaps given the complexity of 12 parameters and multiple dependent tools.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema already has 100% description coverage for all 12 parameters. The description adds significant value by explaining why parameters are required (pricing formulas) and giving specific pricing rules per tool (e.g., generate_text price per char, generate_video modes with costs).
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?
Clearly states the tool creates a Lightning invoice for one AI service call. Immediately distinguishes from sibling tools like get_cost_estimate and check_payment_status.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly describes the typical workflow (list_models → create_payment → check_payment_status → call tool) and advises to call once per operation. Also details when extra parameters are required based on the tool being paid for.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description fully discloses behavior: two modes, pricing (500 sats up to 10 pages, +50 per extra page), max limits (350 pages/50 MB), payment requirement via create_payment, and payment method (Bitcoin Lightning). It also clarifies no fax machine or phone line needed.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single well-structured paragraph. It front-loads purpose and use cases, then logically covers modes, cover page, pricing, limits, and payment. Every sentence adds value with no redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given 7 parameters, two modes, payment dependency, and no output schema, the description is complete. It covers when to use, how to use, pricing, limits, required parameters, and references the sibling create_payment tool for payment.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, but the description adds significant meaning beyond the schema: clarifies the two modes, optional cover page usage, that pdfUrl must be a public HTTPS URL, and that pages is used for pricing and must match create_payment's quantity.
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 sends a fax with two modes (pdf/text), optional cover page, and worldwide destination. It distinguishes from sibling tools like send_email and send_sms by emphasizing the need for a paper trail.
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 concrete examples of when to use (loan paperwork, signed contract, booking confirmation) and states it works worldwide. However, it does not explicitly state when not to use or mention alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Discloses error on non-completed jobs, URL expiry (~1 hour), free usage, and return shape per jobType. With no annotations, the description fully covers 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?
Concise (6 sentences), front-loaded with purpose, no redundancy. Every sentence adds unique value. Well-organized.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Fully covers return types, error conditions, URL lifespan, and free status despite no output schema or annotations. No gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema covers both parameters (100%). Description adds value by clarifying requestId's origin and mapping jobType to specific async tools, going beyond 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 'Retrieve the final output of a completed async job' and distinguishes from synchronous tools. It specifies the verb (retrieve), resource (final output), and precondition (job must be completed).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicit when to use: after check_job_status returns completed. States error if called early. Also excludes synchronous tools. Provides clear context for correct invocation.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description fully discloses key behaviors: it sends an email to the operator, detects duplicate tickets, and returns existing LNURL for metered services, providing comprehensive 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?
Description is thorough but slightly verbose; however, each sentence adds value and the structure is logical with clear sections for usage and behavior.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema or annotations, the description covers purpose, usage guidelines, parameter context, behavioral nuances, and edge cases (duplicate detection, metered service handling), making it fully complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Adds significant meaning beyond the 100% schema coverage by explaining context (paymentId from failed call, invoice as lightning address or bolt11, optional email for follow-up, feedback max 2000 chars) and constraints not in 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 opens a manual 48-hour refund review ticket for a failed service (error, timeout, wrong output) and distinguishes it from automatic refunds for metered services, making the purpose highly specific and differentiated from siblings.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicit instructions on when to use (failed service) and when not to (unused-minute refunds on metered services), with alternative explanation (automatic LNURL withdrawal) and duplicate detection behavior.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Discloses key behaviors: cost (1 sat per vote, multiple votes allowed), return format (JSON with success, slug, newVoteCount), and prioritization mechanism (highest-voted services prioritized). No annotations provided, but description covers essential aspects.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Four sentences, each adding essential information: purpose, return format, cost, prerequisite, and prioritization. No wasted words, front-loaded.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
All necessary context is present: prerequisite tool, payment requirement, cost details, return fields, and voting impact. No output schema needed as return is described.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% and description adds value by clarifying paymentId cost and slug source, plus return structure. Extra context beyond schema justifies above baseline.
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 action ('Vote for a planned service') and the resource ('planned service'), distinguishing it from sibling tools like list_planned_services.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly instructs to call list_planned_services first and to create a payment with toolName='vote_on_service', providing clear prerequisites and workflow context.
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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