Omnicall — Agent Tools Gateway

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description must disclose behavioral traits. It mentions cost and model defaults but lacks details on token limits, rate limits, data logging, or side effects. This is insufficient for a general-purpose completion tool.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is two sentences plus a cost line, front-loaded with the core purpose. It is efficient and contains no filler, though additional details could be integrated without bloat.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the simplicity of the tool (single param, no output schema, no annotations), the description covers purpose, cost, and model options. However, it omits response format, token limits, and practical usage tips, leaving some gaps for an agent.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The single 'arg' parameter is described as a 'prompt' via the description, which adds meaning beyond the schema (which only gives type string). However, it does not specify format, length constraints, or whether it's the entire prompt, leaving some ambiguity.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the verb 'send', the resource 'prompt', and the output 'frontier-quality answer'. It also specifies model options, offering a clear distinction from sibling tools that handle different modalities or specific tasks.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

While the description implies general text completion use, it does not explicitly state when to use this tool versus other AI tools (e.g., ai_pro, ai_eco). No when-not or alternative guidance is provided beyond the model options.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations provided; description relies solely on text. It discloses cost and model selection but omits safety characteristics (e.g., read-only? destructive?), response format, rate limits, or authentication needs.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Short and somewhat front-loaded with name and cost, but lacks a clear verb (e.g., 'Generates...') and structure. Every sentence is earned, but missing critical details reduce efficiency.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple tool with one parameter and no output schema, the description should cover the parameter's purpose and return format. It fails to do so, leaving the agent guessing about behavior.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The single required parameter 'arg' is undefined in both schema (0% description coverage) and description. The description mentions model query parameters but does not clarify what 'arg' represents (e.g., prompt text).

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description states it's the 'cheapest pay-per-call LLM' and lists default and optional models. This clearly identifies it as a text completion tool at the eco tier, distinguishing it from siblings like ai_pro or ai_ultra via cost positioning.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

No explicit guidance on when to use this vs alternatives (e.g., ai_ask, ai_pro). Only cost and model options are mentioned, leaving the agent to infer use cases without sufficient direction.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description must disclose behavioral aspects. It mentions pay-per-image pricing and no API requirement, but omits important details like rate limits, image size constraints, content policies, or potential errors. This is insufficient 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.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is very concise (three sentences) and front-loaded with the core function. The cost information is included efficiently. However, it sacrifices necessary detail for brevity.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the lack of annotations, output schema, and parameter descriptions, the description must cover many aspects. It covers purpose and cost, but fails to explain prompt requirements, response format, error conditions, or behavioral constraints. The tool is simple but the description is incomplete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The single parameter 'arg' is not described in the schema (0% coverage). The description implies it is the prompt, but does not specify format, max length, or allowed content. This lack of detail requires the agent to guess or rely on external knowledge.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's action: text-to-image generation using Flux, and the resource: generating an image from a prompt and returning a hosted URL. This distinguishes it from sibling tools like ai_video or ai_music.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description mentions cost and payment method but provides no guidance on when to use this tool versus alternatives. It does not specify prerequisites or scenarios where this tool is preferable, leaving the agent uncertain about selection criteria.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Description discloses cost and royalty-free nature but omits behavioral traits like output format (audio file link?), duration limits, prompt constraints, or error handling. With no annotations, the description should provide more detail.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Extremely concise: two sentences covering core functionality and cost. No fluff, front-loaded with purpose and key details.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a content generation tool with one parameter and no output schema or annotations, the description is insufficient. It omits output format, generation time, prompt guidance, and error states, leaving agents underinformed.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The sole parameter 'arg' has no schema description, but the tool description implies it is a text prompt for music generation. This adds basic meaning but lacks specifics on prompt format, length limits, or style cues.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states it converts text to music (short instrumental tracks) using MusicGen, and the tool name 'ai_music' aligns with its purpose. It is distinguished from siblings like ai_speech or ai_video by its specific focus on music generation.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance on when to use this tool versus alternatives. It mentions pay-per-call and cost, but lacks any when-to-use or when-not-to-use advice, nor does it reference sibling tools.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

The description mentions cost ($0.005–$0.05 USDC) and default model options, but does not disclose key behavioral traits such as return format, streaming behavior, rate limits, or authorization requirements. Since no annotations are provided, the description should cover these, but it is significantly incomplete.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is short but appears truncated ('Pay USDC per') and lacks structured information like usage steps or response details. It is not effectively front-loaded and misses critical content that would justify its brevity.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the presence of many sibling AI tools, the absence of output schema, and lack of annotations, the description fails to provide sufficient context. It omits explanation of the input parameter, return values, and does not differentiate this tool from others.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The sole required parameter 'arg' has no description in the input schema (0% coverage), and the tool description also fails to explain what 'arg' represents or how to format it. The mention of '?model=' suggests additional parameters not in the schema, causing confusion.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description states 'AI completion, frontier tier', indicating it is for generating completions using the most advanced models. However, it does not explicitly specify the action (e.g., 'generate text' or 'respond to prompt') and lacks clear differentiation from sibling tools like ai_ask or ai_ultra, making the purpose somewhat vague.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use ai_pro versus its siblings. It lists alternative models but does not explain context for choosing this tool, leaving the agent without decision-making information.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the full burden. It mentions cost and no API key, but fails to disclose behavioral traits like output format, rate limits, or whether it is idempotent.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is short and front-loaded with purpose, but appears truncated ('For voice agents, narrati...'), reducing clarity. It contains useful cost info but lacks completeness.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the simple tool (1 param, no output schema), the description should cover parameter meaning and return value. It does not explain the parameter or output format, leaving significant gaps.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has a single 'arg' parameter with 0% coverage. The description indirectly implies the text input ('any text') but does not specify format, length limits, or other constraints, providing minimal additional meaning.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states 'Text-to-speech' and specifies the model 'Kokoro'. It distinguishes from sibling AI tools (ai_image, ai_music, etc.) by being speech-specific. The verb '(text-to-speech)' and resource 'voice audio' are explicit.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides context on when to use: for voice agents, with pay-per-call USDC and no API key. However, it lacks explicit guidance on when not to use or alternatives among siblings.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description must carry behavioral disclosure. It mentions payment via USDC and no API key, but does not describe the output format, whether the call is read-only, latency, or any side effects. The input parameter 'arg' is not explained in behavior.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is very short (two sentences, first truncated) but omits critical information such as what the parameter does and what the tool returns. It is not concise in a helpful way because the sentences do not fully specify the tool's operation.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool has a single parameter, no output schema, and no annotations, the description should provide thorough context. It fails to explain the input, output, or usage scenario, leaving significant gaps 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.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has a single required parameter 'arg' with zero description coverage. The tool description adds no meaning about what 'arg' represents (e.g., the prompt). This leaves the agent with no guidance on how to structure the argument.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description states it is an AI completion tool using 'Claude Opus 4.6, the top-end reasoning model,' which clearly identifies its purpose as generating high-tier completions. The 'ultra tier' label and mention of payment help distinguish it as a premium offering among sibling tools like ai_eco or ai_ask.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

No explicit guidance on when to use this tool versus alternatives. The description only implies it's for high-end reasoning due to the model name and cost, but lacks direct comparisons or context about trade-offs with sibling AI tools.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Discloses cost ($0.005–$0.05 USDC per call) and payment method (pay-per-call, no API key). No annotations provided, so description carries the burden. Does not mention safety (non-destructive) or idempotency, but cost transparency helps.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Relatively concise, front-loading purpose and cost. However, the description appears truncated mid-sentence, which detracts from structure and completeness.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Missing output details (duration, resolution, format), parameter specifics, and any prerequisites. Tool complexity is low (single param, no output schema), but description omits essential info for reliable agent invocation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 0% with a single undocumented 'arg' parameter. Description mentions 'generated from a prompt' implying arg is the prompt, but no format, length, or content details. Barely adds meaning beyond schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

Clearly states it is text-to-video generation, specifying model LTX-Video. Distinguishes from siblings like ai_image (image) and ai_music (music). However, the sentence is truncated at 'creative, media and', slightly reducing clarity.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

No explicit guidance on when to use this tool versus alternatives like ai_image. Incomplete sentence 'For creative, media and' leaves intended context unclear. Lacks when-not-to-use or alternative recommendations.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations provided; description mentions cost ($0.005–$0.05 USDC per call) but lacks details on read-only nature, rate limits, error responses, or what 'live' means. As a data retrieval tool, more behavioral context is needed.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Description is concise (one incomplete sentence plus cost) but the cutoff disrupts flow. No structural issues, but the truncation reduces effectiveness.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the simple tool (single param, no output schema, no annotations), the description omits important contextual details like output format, error handling, and meaning of 'live'. Cost info is a positive but not sufficient.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema has one undocumented string parameter 'arg'; description partially compensates by specifying it expects a protocol slug with examples. However, schema coverage is 0%, and more detail (e.g., case sensitivity, valid format) would improve clarity.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

Description states tool returns 'Live DeFi protocol TVL by chain & category + links' and requires a protocol slug. However, the description is cut off at 'Fo', causing ambiguity. Sibling distinctions like 'dex' or 'markets' are not addressed.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Provides example slugs for usage ('aave', 'uniswap', 'lido') but no guidance on when to use this tool over siblings like 'dex' or 'lookup_crypto', nor any exclusions or prerequisites.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden. It only discloses cost ($0.005–$0.05 USDC on Base per call) but omits other behavioral traits like rate limits, authentication, or error handling.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences: one for purpose and one for cost. No wasted words, front-loaded, efficient.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's simplicity (one parameter, no output schema, no annotations), the description covers the input and cost but does not explain the output format or behavior on invalid input, leaving minor gaps.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The single parameter 'arg' has 0% schema description coverage, but the description tells users to pass a token symbol or contract address, adding clear meaning and compensating for the schema gap.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states that the tool provides live DEX token prices, liquidity, and 24h volume across every chain using DexScreener, and instructs to pass a token symbol or contract address. This distinguishes it from siblings like 'lookup_crypto' or 'markets' which cover broader crypto data.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage by saying 'pass a token symbol or contract address' but does not explicitly state when to use this tool versus alternatives, nor does it provide exclusions or conditions.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description fully carries the behavioral burden. It declares the action (return snippets), the scope (multiple platforms), and an important trait (free). No contradictions or hidden behaviors are present.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single sentence that is concise and front-loaded. Every word adds value: verb, object, target platforms, and the 'free' attribute. No unnecessary information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a zero-parameter, no-output-schema tool, the description is complete. It fully specifies what the tool returns and for which environments. No additional context is needed.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has zero parameters, so schema description coverage is 100%. The description does not need to add parameter meaning, as there are none. Baseline 4 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description explicitly states the tool returns ready-to-paste configuration snippets for installing this MCP server in six named platforms, using a specific verb ('Return') and resource ('configuration snippets'). It clearly distinguishes from siblings like 'list_endpoints' or 'pricing_info'.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage when one needs installation snippets for the listed platforms. It does not explicitly mention when not to use or provide alternatives, but given the zero-parameter nature and clear purpose, the usage context is well implied.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations provided, so description carries full burden. It discloses that listing is free and requires no wallet, indicating no destructive behavior. However, lacks details on response format or 'live status' meaning.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Two concise sentences with no fluff, front-loading purpose and immediate usage guidance.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given zero parameters and no output schema, description covers purpose, usage, and behavioral expectations. Could briefly define 'live status' for completeness.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Zero parameters, schema coverage 100%, so no need for param details. Baseline of 4 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

Description clearly states it lists paid endpoints with prices and live status. It explicitly says to use first to discover tools, distinguishing it from sibling tools that are specific endpoints.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly says 'Free — no wallet required' and 'Use this first to discover what tools are available,' providing clear context and sequential guidance. No exclusions needed for a discovery tool.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries full burden but only reveals cost and output type. Missing details on side effects, read-only nature, rate limits, or authentication needs.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is very short, two sentences, but it omits essential information. Conciseness is not beneficial when it sacrifices clarity and completeness.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given no output schema and a cryptic parameter, the description fails to provide enough context for proper invocation. An agent cannot reliably determine what to pass in 'arg' or what output to expect.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The sole parameter 'arg' is required with no description in schema or in the tool description. At 0% schema coverage, the description does not clarify what value it expects (e.g., symbol like 'BTC'), making it unusable.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description states 'Crypto price + 24h change', clearly indicating the output, but it lacks specificity (which crypto?) and does not differentiate from sibling tools like 'markets' or 'pricing_info' that may offer similar data.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance on when to use this tool versus alternatives, no prerequisites or exclusions mentioned. The agent is left to infer usage from the vague purpose.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided. The description mentions cost and that odds are live, but does not disclose other behavioral traits such as rate limits, pagination, or whether the operation is read-only. The cost disclosure adds some transparency.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise (two sentences) and front-loaded with the core purpose. Every sentence adds value: first explains function, second specifies cost. No wasted words.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple search tool with one parameter and no output schema, the description covers the essential aspects: what it does, input, and cost. It could elaborate on return format but is otherwise complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With 0% schema coverage, the description compensates by stating the arg is a 'keyword' for searching markets. This gives meaning to the parameter, though it could specify format or accepted values.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description specifies the tool is for live prediction-market odds from Polymarket, with a clear action (search by keyword) and output (implied probability). It distinguishes from siblings like dex and defi which are for different domains.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies when to use: for searching active prediction markets by keyword. However, it lacks explicit when-not or alternative tools for specific contexts, though the sibling list provides implicit differentiation.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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 does not explicitly mention safety (read-only) or lack of side effects. However, the term 'Return' suggests a read operation, and it briefly notes it is free, which adds some 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.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, efficient sentence that conveys all necessary information without any fluff. It is well-structured and front-loaded.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given zero parameters, no annotations, and low complexity, the description is complete. It lists the key return items despite lacking an output schema.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

There are zero parameters, so the baseline is 4. The description adds value by explaining what the tool returns, which is not evident from the schema alone.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states it returns pricing details for the GoCreative Agent API, listing specific items (base price, premium endpoints, cache TTLs, payment networks). It distinguishes itself from sibling tools, which are focused on other functionalities like AI, crypto, or endpoints.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage for obtaining pricing info and notes it is free, but does not explicitly state when not to use it or mention alternatives. However, the context of sibling tools makes it clear this is the go-to for pricing queries.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description partially fills the gap by disclosing the cost ($0.005–$0.05 USDC per call). However, it lacks details on error handling, rate limits, or result format. The multi-platform nature is stated but not further elaborated.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences convey the core purpose and cost without redundancy. Every word is functional, and the structure is straightforward.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Despite having a single parameter and no output schema, the description omits crucial context such as expected input format, return structure, or limitations. The cost mention is helpful but insufficient for full usability.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has one parameter 'arg' with 0% schema description coverage. The description does not explain what 'arg' represents (presumably the search query), leaving the agent without guidance on what to input. The low coverage demands compensation, which is absent.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states 'Multi-platform search in ONE call' and lists the specific platforms (open web, GitHub, Hacker News, etc.), providing a specific verb and resource. It distinguishes from sibling tools like 'web' (single-platform) and 'research_papers' (academic-only) by emphasizing the multi-source capability.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance on when to use this tool versus alternatives. The description does not mention when it is appropriate or inappropriate, nor does it reference sibling tools like 'web' for simple web searches or 'research_papers' for academic focus. The agent is left to infer use cases.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations are absent, so the description carries full burden. It discloses cost and data source but not other behavioral traits like rate limits, error handling, or that results are limited to 'recent' papers. The description is minimal.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is two sentences, front-loads purpose, and includes cost information. Every sentence is necessary; no waste.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the simplicity (one parameter, no output schema), the description is adequate but incomplete. It fails to specify input format or output structure, and does not mention pagination or result count.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema has one required parameter 'arg' with 0% description coverage. The description says 'for a query' but does not clarify what 'arg' should contain (e.g., search term, title, author). No format or examples are provided.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states 'Recent academic papers for a query' and lists fields (title, authors, journal, year, DOI, citations) and the data source (Crossref). This provides a specific verb+resource, but it could be more differentiated from the sibling 'research' tool.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description mentions cost, which is helpful for budgeting, but gives no guidance on when to use this tool vs alternatives, prerequisites, or 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.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided. The description mentions cost and that it uses public JSON-RPC, but does not disclose behavioral traits such as rate limits, authentication requirements, idempotency, or error handling. The description carries the full burden but provides minimal behavioral context.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is relatively short (two lines) but is cut off mid-sentence ('public JSO'), which harms structure. It earns its place by listing capabilities but is incomplete.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the complexity of a multi-chain RPC tool with one parameter and no output schema, the description is severely lacking. It does not specify how to use the arg, expected response format, supported chains list, or error handling. The tool is complex but the description provides minimal context.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%. The description does not explain what the single parameter 'arg' should contain (e.g., RPC method, parameters, or endpoint). It adds no meaning beyond the schema, which is essentially a raw string input.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool provides multi-chain EVM RPC across 48 chains and lists specific capabilities (native balance, tx count, contract check, latest block, gas price). It distinguishes from sibling tools which are mostly AI or other crypto tools. However, the description cuts off abruptly ('public JSO') and has a typo, slightly reducing clarity.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

No explicit guidance on when to use this tool vs siblings. The cost is mentioned but not when it is appropriate. No alternatives or exclusions are provided.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations provided, so description bears full burden. It states it returns stats and is free, but does not disclose any behavioral traits like rate limits, data freshness, or side effects. Since it's a read-only stats call, it is acceptable 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.

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences, no wasted words. Front-loaded with action and content. Every sentence earns its place.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given zero parameters and no output schema, the description covers the purpose, content, and use case. Lacks return format or examples, but the low complexity makes it mostly complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

No parameters exist, so baseline is 4. Description adds no parameter info, but none is needed.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

Description clearly states the verb 'Return' and resource 'summary stats of how this MCP server has been used', listing specific content (top tools, success rate, recent activity). It distinguishes from sibling tools which are functional tools, while this is a meta-usage tool.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly says 'Use to verify your own integration is hitting the right tools', providing a clear use case. Lacks explicit 'when not to use' or alternatives, but the context is sufficient for a simple tool.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations provided, but the description fully explains the tool's behavior: it returns instructions. No side effects or complex behavior to disclose.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences, no fluff. Purpose and usage are immediately clear. Efficient and front-loaded.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given no input/output schema and no annotations, the description adequately covers all needed context. Sufficient for agent to select and invoke correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

No parameters exist, so schema coverage is 100% trivial. Description adds no param info, but none needed. Baseline of 4 for zero-parameter tools.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool returns step-by-step instructions for setting up x402 USDC autopay. It distinguishes from sibling tools (e.g., AI, defi) by focusing on payment setup.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly tells when to use: after a 402 error or when onboarding a new agent needing payment. Also mentions it is free, aiding decision.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries full burden for behavioral disclosure. It states the tool uses DuckDuckGo and has a cost range, but omits details on limits (e.g., result count, pagination), error handling, rate limiting, or whether it respects robots.txt. The safety and destructive nature are not addressed.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is relatively short but has an awkward trailing 'For' that suggests incomplete information. It front-loads the purpose, which is good, but the cost line is separated by a line break. Overall, it's acceptable but could be smoother.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's simplicity (one parameter, no output schema), the description is incomplete. It fails to explain return format (e.g., snippets, links), whether results are cached, or how to handle edge cases like no results. An agent would lack key information to use the tool effectively.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The single parameter 'arg' has no schema description (0% coverage). The tool description implies 'arg' is the search query by saying 'for any query', but adds no format constraints, length limits, or examples. This barely adds meaning beyond the schema's parameter name.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool performs web search using DuckDuckGo, providing 'instant answer, definition & related topics'. The verb 'search' and resource 'web' are explicit, and the mention of DuckDuckGo specifies the source. However, it does not differentiate from sibling tools like 'ai_ask' or 'research' which may also retrieve information.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description mentions cost and payment model ('pay-per-call USDC, no API key') but provides no guidance on when to use this tool versus alternatives. It does not specify scenarios where web search is preferred over other tools like 'ai_ask' for Q&A or 'research' for deep analysis.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations provided. Description discloses pay-per-call cost range and no key needed, which is helpful. However, it lacks details on rate limits, data freshness, or whether the tool modifies anything. Given no annotations, more would be expected.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Extremely concise: two sentences, front-loaded with purpose, then cost info. No wasted words. Efficient for an agent.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple tool with one parameter and no output schema, description is nearly complete. Lists data fields returned. Lacks mention of response format but acceptable given simplicity.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Only parameter 'arg' has 0% schema description coverage. Description implies the parameter is a handle ('for any handle') but does not explicitly state that 'arg' is the Twitter handle. Schema provides no type details beyond string.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

Description clearly states it fetches X/Twitter profile data, listing specific fields (followers, bio, verified status, post & following counts). Verb is implied ('get' or 'fetch'). Distinct from sibling tools which are mostly AI or crypto related.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance on when to use this tool vs alternatives. No comparison to siblings. Does not mention any prerequisites or restrictions.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.