Prism — Data Enrichment for AI Agents

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

The description discloses the cost per call ($0.005–$0.05 USDC on Base), but this is an operational detail rather than behavioral. There is no annotation provided, so the description carries the full burden of explaining side effects, data source, limitations, or response behavior. It doesn't mention whether the call is read-only, what happens on invalid domains, or if any state changes occur. The truncation ('from') also obscures the data source.

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 poorly structured, containing an awkward line break and a dangling 'from' that suggests truncation. The cost information is included inline, mixing pricing with functional description. It lacks clear organization and appears incomplete, which undermines its conciseness.

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 moderate complexity (one parameter, no output schema, no annotations), the description should clarify inputs, outputs, and usage scenarios. It lists some output fields but omits the source, the return format, and any edge cases. The truncated 'from' leaves a critical gap, and the cost info does not compensate. Overall, the description is incomplete and insufficient for reliable tool 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?

With schema coverage at 0% and only a single parameter named 'arg', the description must add meaning. The phrase 'by domain' indicates that the 'arg' parameter is the domain, which is a useful semantic hint. However, it doesn't specify the expected format (e.g., 'example.com' vs 'https://example.com') or any other constraints, so it does not fully compensate for the missing schema description.

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 'Company enrichment by domain' and lists specific data fields (legal entity, HQ, industry, funding, contractor status), which gives a clear picture of the tool's purpose. However, the truncated sentence ending in 'from' leaves the description incomplete and somewhat ambiguous about the source or exact action. It partially distinguishes from siblings like enrich_people by the phrase 'company profile', but not fully from enrich_domain.

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 is given about when to use this tool versus alternatives. There is no mention of prerequisites, use cases, or exclusions. The only hint is 'by domain', implying the input is a domain, but no comparison to sibling tools like enrich_domain or enrich_lite.

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 of disclosing behavior. It discloses the cost per call ($0.005–$0.05 USDC on Base) and the types of data returned, which are important operational traits. However, it does not mention request/response format, potential errors, or any side effects, leaving room for additional 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 and front-loads the core concept ('Domain intelligence'). It lists key data types and cost in a compact form. The phrase 'Lead enrichment + Cost' is structurally awkward, but overall the description is appropriately brief and free of fluff.

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?

There is no output schema or annotations, so the description alone must convey return structure and usage details. It lists high-level data categories but does not explain the format of DNS records, WHOIS details, or how the TLS footprint is structured. It also leaves the sole parameter ambiguous, making the tool under-specified for reliable 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?

The schema has one parameter ('arg') with no description, yielding 0% schema coverage. The description does not explicitly state that 'arg' should be a domain name, though it is implied by the tool name and context. No format, example, or constraints are given, so the description fails to compensate for the schema's lack of detail.

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 identifies the tool as providing domain intelligence, listing DNS records, WHOIS registration, and TLS certificate footprint. This resource (domain) and data categories distinguish it from sibling tools like enrich_email or enrich_people. However, it lacks an explicit verb like 'retrieves' or 'enriches', relying on the tool name to imply the action.

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 domain-related enrichment and lead enrichment, but it does not explicitly state when to choose this tool over alternatives such as enrich_company_profile or extract_url. No comparative guidance or exclusionary context is provided. The mention of 'Lead enrichment' hints at a use case but falls short of a clear directive.

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, and it does add some behavioral context: ranked guesses, real-world frequency, live MX validation, and cost. However, it omits crucial details like whether the tool is read-only, response format, rate limits, or error behaviors. The truncation ('Hu') also suggests missing disclosure.

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 the main purpose, but it suffers from truncation mid-sentence ('Hu') and includes pricing that, while useful, is secondary. The structure is acceptable but not polished, and the incomplete sentence detracts from clarity.

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 no annotations, no output schema, and a single poorly-described parameter, the description needs to compensate but does not. It omits the required format for 'arg', expected return structure, and any usage constraints. The inclusion of cost and validation details is helpful but insufficient for full autonomous 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?

The schema has a single generic 'arg' parameter with 0% description coverage. The description tells the conceptual inputs (person name + company domain) but fails to explain how these map to 'arg' — is it a comma-separated string, JSON, or separate fields? This is a significant gap that prevents correct invocation.

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 identifies the tool as a 'work-email finder' and specifies the input (person name + company domain) and output (ranked email guesses with MX validation). This distinguishes it from sibling tools like enrich_company_profile and enrich_people, making the purpose highly specific and action-oriented.

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 a use case but provides no explicit guidance on when to choose this tool over alternatives such as enrich_lite or lookup_email_validate. There is no mention of prerequisites, limitations, or when not to use it, leaving the agent without decision-making support.

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 must reveal behavioral traits on its own. It discloses the cost per call and StableEnrich compatibility, adding operational context. However, it does not state whether the operation is read-only or describe any side effects, leaving some burden unmet.

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 only two sentences and efficient, but the truncated 'gov/' phrase suggests an incomplete thought or typo, which harms clarity and professionalism.

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?

The description lists the types of output data but fails to specify the required input format. Since there is no output schema and no parameter explanation, the tool cannot be used correctly without additional information, a significant completeness gap.

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 string parameter 'arg' with no description and 0% schema coverage. The description does not explain what value 'arg' should contain, leaving the agent completely without guidance on how to invoke the tool.

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 function: 'All-in-one company enrichment' with a specific list of data types (domain, social, people, places, contact data). This distinguishes it from sibling tools like enrich_domain and enrich_email, which target single data types.

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 'all-in-one' wording implies a batch use case, but the description does not explicitly say when to use this tool versus calling individual enrichment tools. No exclusions or alternative recommendations are provided, so the guidance is only 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 are provided, so the description carries the full burden. It does disclose cost and claims 'Verified' data, but the truncated 'via a mu' leaves the source unclear, and there is no mention of failure modes, missing data, rate limits, privacy considerations, or return format.

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 brief and front-loaded with the core function, and the cost line is useful. However, the sentence fragment 'via a mu' is incomplete and disrupts the flow, making the text feel unfinished rather than intentionally concise.

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?

With no output schema and no annotations, the description provides the essential inputs, outputs, and cost, which is adequate for a straightforward enrichment tool. But it omits details such as how results are returned, what happens when no match is found, and any authentication or rate-limit requirements.

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 only exposes a generic 'arg' string with no description (0% coverage). The description compensates by specifying the input as 'a name + company domain' and providing the example 'Patrick Collison stripe.com', which clarifies the expected string format despite not formally describing the parameter.

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 produces 'Verified work-email + direct-phone + LinkedIn' from a name and company domain, with a concrete example. This distinguishes it from sibling enrichment tools like enrich_domain or enrich_email by specifying both the input format and the output fields.

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 you have a name and company domain and need verified contact details, reinforced by the example. However, it does not explicitly state when not to use this tool or mention alternatives among the many enrichment siblings (e.g., enrich_lite, enrich_company_profile).

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 discloses the cost range, which is transparent, and implies a read-only fetch of a URL. However, it does not mention potential limitations (e.g., file size, page limits, authentication) or exact output format.

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

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

The description is short and front-loaded with key information, including cost. However, the text appears truncated ('LLM-r'), which slightly mars clarity, though the overall structure is 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?

For a one-parameter tool with no output schema and no annotations, the description gives a reasonable overview of purpose and input, but lacks details on return structure, error handling, and any operational limits. The cost note adds value but does not fill all 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 schema has one parameter 'arg' with no description (0% coverage). The description partially compensates by indicating the argument is a PDF or document URL, but it does not specify formats (e.g., whether it accepts base64 or only direct links), leaving room for 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 a specific action ('extract') on a specific resource ('PDF or document URL') and lists expected outputs (text, tables, forms, invoice fields). This distinguishes it from sibling tools like extract_url or prism_scrape, which target web pages or general scraping.

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 structured extraction from documents but does not explicitly state when to use this tool over alternatives like extract_url or prism_scrape. No exclusions or alternative recommendations are provided.

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

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

With no annotations, the description carries the full burden. It discloses the output format (markdown with title/main content), the processing behavior (stripping boilerplate), and cost per call. However, it does not mention error handling for invalid URLs, auth requirements, or rate limits, leaving a gap in behavioral disclosure.

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 and front-loaded with the core transformation ('Any web URL -> clean LLM-ready markdown'). The cost line adds useful contextual information. However, the truncated phrase 'for ag' (likely 'agents') introduces an incomplete thought, slightly detracting from structure.

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 no output schema, the description explains the main return value (markdown) and cost, which is good. But it lacks guidance on edge cases, such as handling of large pages, link rot, or placeholder content. The incomplete sentence ('for ag') also leaves the tool's positioning somewhat ambiguous, making the description not fully 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?

The schema has one param 'arg' with 0% description coverage, but the description adds 'Any web URL', which clearly indicates the expected value. It doesn't specify detailed format (e.g., must be absolute) but for a simple single-parameter tool, this is sufficient to meaningfully augment the 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?

The description clearly states a specific verb and resource: converting a web URL into clean, LLM-ready markdown with title and main content, stripping boilerplate. This distinguishes it from sibling tools like search_web (search results) and prism_scrape (generic scraping) by framing it as the retrieval/RAG primitive.

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?

It implies usage for retrieval/RAG by calling itself 'the retrieval/RAG primitive', giving a clear context for when to use it. However, it does not explicitly mention alternatives or when not to use it, such as comparing to extract_document or prism_scrape. The guidance is implied, not explicit.

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?

Full disclosure: returns ready-to-paste snippets, and states it's free. No annotations provided, but description covers behavior completely for a parameterless 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?

Single sentence, 20 words, front-loaded with key information. No unnecessary 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?

Describes output (snippets) and target IDEs. Could specify return format (e.g., object with keys per IDE), but sufficient for tool complexity.

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; schema coverage is trivially 100%. Description adds value by explaining the tool's output beyond the empty 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?

The description uses specific verb 'Return' and resource 'configuration snippets' for installing this MCP server in multiple IDEs. It clearly distinguishes from siblings like list_endpoints and 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?

Implicitly indicates usage for obtaining installation snippets, but lacks explicit when-to-use or comparisons to alternatives. For a simple tool, this is adequate.

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. It discloses that the tool is free and requires no wallet, which are key behavioral traits. It could add details about output format or pagination, but the transparency is sufficient for a simple list 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?

Two concise sentences with no wasted words. The first sentence front-loads the core function, and the second provides actionable guidance. Perfectly structured for quick comprehension.

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 adequately covers the tool's purpose and output. It explains what is listed (prices, live status) and that it's free, though it could specify the exact return structure. Overall, it's complete enough for its 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?

No parameters exist, so schema coverage is 100%. The description adds value by explaining what the output contains (prices, live status) and that it's free, going beyond the empty 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?

The description clearly states the tool lists paid endpoints with prices and live status. It distinguishes itself from siblings by focusing on endpoint discovery, not installation, pricing info, usage, or wallet functions.

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 explicitly advises to use this tool first to discover available tools, providing clear timing context. It does not discuss when not to use it, but the guidance is direct and helpful.

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 discloses that the tool performs syntax validation and live DNS/MX lookup, and includes cost information. However, it does not explain whether the operation is read-only, what the return value looks like, or any potential side effects or limitations. The description adds some behavioral context beyond the tool name but lacks thorough disclosure.

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 extremely short, which is concise in word count, but it is truncated ('returning th') and lacks structure. It provides a single sentence with cost information but omits essential details about parameters and return values. This is under-specification rather than effective conciseness, as every word is not earning 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?

For a tool with no annotations, no output schema, and a single undocumented parameter, the description is incomplete. It explains the core purpose and cost but does not define the argument, output format, or usage context. Given the tool's simplicity, it still misses critical information needed for correct 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?

The input schema has a single parameter 'arg' with 0% schema description coverage. The description does not explain what 'arg' should contain (e.g., an email address or domain). This is a critical gap because the tool's purpose implies the argument is an email to validate, but it is never explicitly stated. The description fails to compensate for the schema's lack of detail.

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 purpose as an 'Email deliverability check' with specific details about syntax validation and live DNS/MX lookup. This distinguishes it from sibling tools like 'enrich_email' which likely focus on data enrichment rather than validation. The verb is implicit but the resource (email deliverability) is clearly identified.

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 this tool versus alternatives like 'enrich_email' or any other validation tools. It does not state prerequisites, recommended contexts, or exclusions. The only hint is the tool's name, which implies email validation, but there is no explicit usage 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?

No annotations are provided, so the description carries the full burden. It discloses the cost range and that articles are tagged with computed sentiment, which are useful behavioral details. However, it does not describe the return format, pagination, rate limits, or whether results are real-time, leaving significant gaps.

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

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

The description is brief but appears truncated, ending with '+ t' which breaks coherence. It is not well-structured; the cost sentence is separate but the main sentence is incomplete. This is under-specification rather than intentional conciseness.

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 tool with no output schema, no annotations, and a single undocumented parameter, the description should explain what the tool returns and how the parameter works. It only provides a vague notion of keyword search and sentiment tagging, which is insufficient for an agent to correctly invoke and interpret results, especially given similar sibling tools.

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 a single parameter 'arg' with zero description coverage. The description implies 'arg' is the keyword, but does not explicitly map 'arg' to the keyword, nor does it explain expected format or constraints. With 0% schema coverage, the description should compensate but does not fully.

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 Google News search results for a keyword, with each article tagged with sentiment. This distinguishes it from sibling tools like news_google_trending and news_headlines, though the truncation ('+ t') and lack of explicit verb slightly reduce 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 usage guidance is provided. The description does not mention when to use this tool versus news_google_sentiment or news_google_trending, nor does it state any exclusions or prerequisites. The cost information is the only practical note, but it doesn't help with tool selection.

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 responsibility for behavioral disclosure. It mentions cost per call, which is useful, but does not disclose rate limits, authentication needs, input format expectations, or what 'sentiment' means specifically. The truncated sentence about trends adds partial info but not enough 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 short but disorganized and incomplete. The phrase 'the wedge' is nonsensical, and the sentence cuts off mid-word ('and tre'), suggesting truncation. While cost information is included, the overall structure lacks a clean front-loaded definition and wastes space on unclear phrasing.

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?

There is no output schema, so the description must explain return values. It mentions sentiment percentages, top sources, and trends, but does not detail the structure or whether the output is JSON, text, etc. The undefined 'arg' parameter and lack of usage examples make the tool incomplete 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 input schema has one required parameter 'arg' with no description, and schema coverage is 0%. The description does not explain what 'arg' represents (e.g., search query, URL, topic). This is a critical gap for an agent to invoke the tool correctly.

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 aggregate sentiment breakdown (positive/negative/neutral %), top sources, and trends for Google News. This distinguishes it from sibling tools like news_google_search and news_google_trending. However, the phrase 'the wedge' is unclear and the description appears truncated ('and tre'), 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?

The description provides no guidance on when to use this tool vs alternatives. It does not mention whether to use it for sentiment analysis of a topic, query, or URL, nor excludes it for other use cases. No alternatives or contexts are referenced.

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 appropriately reveals cost ($0.005–$0.05 per call) and the absence of keyword filtering, but falls short on deeper behavioral details like response format, pagination, or what 'sentiment' entails. It doesn't contradict any annotations (none exist) but doesn't fully carry the burden of disclosure.

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, with the core function first and cost appended, containing no filler. It's efficiently structured and easy to parse.

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?

The tool is simple with one optional parameter, but without an output schema or a description of the response structure, the agent cannot predict what data will be returned beyond 'headlines'. Key questions about the query_string parameter and sentiment label format are left unanswered, so the context 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 sole parameter 'query_string' has zero schema description coverage and is never mentioned in the tool description. The phrase 'no keyword filter' actually creates confusion about whether the parameter has any effect, leaving the agent unable to decide what value to pass.

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 identifies the tool's function: returning current top Google News headlines with sentiment tags, explicitly stating 'no keyword filter' to differentiate from search-based alternatives. This provides a specific resource and scope, distinguishing it from siblings like news_google_search.

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 use for real-time trending headlines without filtering, but doesn't explicitly name alternatives or state when not to use it. The 'no keyword filter' phrase hints at a contrast with search tools, but leaves the decision open.

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 discloses the returned fields and per-call cost, which are useful behavioral traits. However, it does not mention rate limits, pagination, latency, or any potential side effects. The pricing info adds transparency, but gaps remain for a network-dependent monitoring 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 exceptionally concise: two sentences plus a cost line, with key information front-loaded. No fluff or repetition, every sentence contributes value.

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 no output schema, the description adequately covers purpose, parameter semantics, and return fields (title, source, published date, link). It also includes cost. Missing details like result count, sorting, and language/region controls are not critical for a monitoring primitive, but would round out 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?

The schema has 0% description coverage for the single required parameter 'arg'. The description compensates by clearly indicating that 'arg' represents the topic for which headlines are fetched. This is important semantic meaning, though it lacks format examples or constraints.

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 recent news headlines for a topic with specific fields (title, source, published date, link) via Google News. It identifies itself as a 'monitoring primitive' for agents, but it does not explicitly differentiate from sibling tools like news_google_search or news_google_trending, so it loses a point.

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 phrase 'Monitoring primitive for agents' implies use for ongoing monitoring rather than one-off searches, which provides some usage context. However, there is no explicit guidance on when to use this tool versus alternatives like news_google_search or news_google_trending, nor any exclusion criteria. The cost note may influence decisions but does not direct tool selection.

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 convey behavioral traits. It states the tool is 'Free', which may imply no cost. However, it does not explicitly state side effects, rate limits, or that it is a read-only operation. For a simple info tool, this is adequate but not exceptional.

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, well-structured sentence that front-loads the purpose ('Return pricing details'), followed by a list of included items, ending with 'Free'. No unnecessary 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?

The tool has no output schema, so the description is the only source for return value structure. It lists categories of information but does not specify the format (e.g., JSON object fields, data types). For a simple pricing endpoint, this may be sufficient, but additional structure would improve 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?

The input schema has zero parameters with 100% schema description coverage. The description adds meaning by detailing the return content (base price per call, premium endpoints, cache TTLs, supported payment networks), which is the sole documentation for the tool's output.

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 pricing details for the API, listing specific items (base price per call, premium endpoints, cache TTLs, supported payment networks). It is distinct from sibling tools like install_snippets or list_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?

No explicit guidance on when to use or when not to use this tool versus alternatives. The description implies it is for retrieving pricing info, but does not mention any prerequisites or exclusions.

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 burden of behavioral disclosure. It mentions 'Keyless' and a cost range ($0.005–$0.05 USDC), which are useful behavioral details. However, it does not indicate whether the operation is read-only, potential side effects, rate limits, or error behavior. This is a moderate disclosure but leaves gaps.

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

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

The description is brief and front-loads the main purpose. However, cost information is repeated ('Keyless, $0.01' and 'Cost: $0.005–$0.05 USDC'), making it slightly redundant. Despite this, the structure is clear and avoids unnecessary verbosity.

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?

The tool is a single-parameter enrichment call with no output schema, so the description should explain what data is returned and what input is expected. It lists the output categories (legal entity, DNS/WHOIS, TLS, web footprint) but does not describe the output structure or clarify the exact input format. Without an output schema, this is a noticeable gap, but the listed categories provide a partial picture.

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 a single parameter 'arg' with no description (0% coverage). The description states 'firmographic enrichment for any domain,' which indirectly implies that 'arg' should be a domain string, but it never explicitly says 'arg' is the domain or provides format examples. This partially compensates for the missing schema description but leaves 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 tool performs firmographic enrichment for domains, listing specific outputs (legal entity, DNS/WHOIS, TLS, web footprint). This establishes a specific verb and resource, but it does not explicitly differentiate from sibling tools like enrich_domain or enrich_company_profile, so it lacks full sibling disambiguation.

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 this tool over alternatives. It does not mention sibling tools or exclusion/inclusion criteria (e.g., 'use for domains requiring firmographics' vs. 'use enrich_domain for a different purpose'). The only context is the tool name and generic enrichment description, leaving the agent without decision support.

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 burden of disclosing behavior. It mentions cost, ranking by pattern frequency, and live MX verification, which are useful. However, it does not disclose potential side effects, failure modes, data privacy, or rate limits, leaving significant gaps.

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

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

The description is relatively concise, with the purpose and cost separated clearly. It is front-loaded with the main action and includes relevant operational cost information. However, the text appears truncated, which prevents full clarity.

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 single parameter is completely undocumented, there is no output schema, and no annotations, the description is insufficient for an agent to understand all necessary context. The tool requires more explanation of input format and expected output to be used reliably.

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 no description, and the tool description does not explain how to format the name and company domain for that parameter. With 0% schema description coverage, the lack of any parameter guidance makes the tool nearly impossible to invoke correctly.

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 function: finding a person's likely work email from name and company domain, ranked by pattern frequency with live MX verification. This is specific and distinguishes it from sibling tools such as enrich_email or lookup_email_validate.

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 context (when you have a name and company domain) but provides no explicit guidance on when to use this tool over alternatives. There is no mention of alternative tools or exclusion 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, the burden falls on the description. It discloses that the tool is keyless and provides a cost range, which is useful operational context, but it does not mention return format, pagination, rate limits, or any side effects. This is minimal but not entirely absent.

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 short sentences with no filler. The purpose is front-loaded and the second sentence provides cost information that earns its place. It is appropriately concise.

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 important context such as expected return values, output format, and any usage restrictions. It also lacks sibling differentiation, so completeness is insufficient for confident 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?

The schema's only parameter 'arg' is an undocumented string, yet the description clarifies that it accepts a name or query with address, category, or coordinates. This adds essential semantics beyond the schema, though it does not specify exact formatting or JSON structure.

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 identifies the tool as returning business places and locations based on a name or query, including address, category, and coordinates. It is specific about the resource type, though it lacks an explicit action verb and does not contrast with the similarly named sibling tool search_places.

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 is given on when to use this tool versus alternatives such as search_places or prism_search. The description only states what the tool does and its cost, leaving the agent to infer appropriate 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?

With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions 'Keyless' and pricing, which is useful context, but does not cover limitations, error handling, authentication requirements, or what constitutes 'clean structured content' beyond a brief field list. The tool's behavior in edge cases (e.g., non-HTML URLs, dynamic pages) is opaque, leaving significant gaps for an agent to understand the tool's operational traits.

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 brief and front-loaded with the core purpose in the first clause. The inclusion of cost information is useful, though the '$0.01' in the first line is redundant with the '$0.005–$0.05' price range in the second sentence, adding minor bloat. Two sentences total, with no filler, earns a high score despite the slight redundancy.

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 an output schema and annotations, the description must provide more context. It lists output components (title, text, metadata, links) but omits details on pagination, request size limits, error responses, or how the 'arg' parameter maps to a URL. For a simple tool it is somewhat usable, but it leaves out critical operational details that an agent would need to invoke it reliably.

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 parameter named 'arg' with no description, and schema description coverage is 0% with no enums. The only clue is the description's mention of 'any URL', which implies that 'arg' should be the URL. However, this is not explicitly stated, and the generic parameter name provides no semantic value. The description partially compensates but fails to clearly document the expected input format, URL encoding requirements, or edge cases.

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 function: 'turn any URL into clean structured content (title, text, metadata, links)'. This is a specific verb+resource combination that conveys the core operation. However, it does not differentiate itself from sibling tools like 'extract_url' or 'extract_document', which likely overlap in functionality, so it falls short of a perfect 5.

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 you have a URL and need structured content—but it provides no explicit guidance on when to choose this tool over alternatives. No exclusions or alternative tools are mentioned, leaving the agent to infer the appropriate context from the generic 'any URL' phrasing. This is acceptable but lacks the explicit 'when vs. when-not' clarity of a 4.

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?

Since no annotations are provided, the description carries the full burden. It discloses keyless payment and cost range, which is useful, but lacks details on rate limits, authentication requirements, or error behavior. The output format is mentioned, adding 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 relatively short and gets to the point, but pricing information is duplicated (first says $0.01, then a range). Slight redundancy, but otherwise efficient and front-loaded with the core purpose.

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 conveys the basic purpose and result format. However, it fails to define the parameter and provides no context on how this tool differs from sibling search tools, making it incomplete for reliable selection and 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?

The input schema has a single required parameter 'arg' with no description, and schema coverage is 0%. The description does not explain what 'arg' represents, leaving the parameter completely ambiguous. This is a critical gap for a one-parameter tool.

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 with ranked results including titles, URLs, and snippets. It uses a specific verb ('search') and resource ('web') but does not differentiate from similar sibling tools like search_web or news_google_search.

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 is provided on when to use this tool versus alternatives. The mention of 'AI web search' and pay-per-call implies a general-purpose option, but there is no explicit 'use this when' or comparison to sibling search 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?

With no annotations, the description carries the full burden. It discloses that the operation is a lookup (read-only), is keyless, and specifies a cost range. However, it omits details like rate limits, error behavior, or authentication requirements beyond being keyless.

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 it redundantly mentions cost twice: once as '$0.01' and again as '$0.005–$0.05'. This creates mild inconsistency in framing, though it does not waste many 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 one-parameter tool, the description covers the core action, keyless access, cost, and expected output types. However, it fails to explicitly map the parameter 'arg' to a handle, and lacks guidance on when to choose this over sibling tools. It is minimally viable but with clear 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?

Schema coverage is 0%, so the description must compensate. It does add meaning by stating the lookups are 'by handle', implying the arg is the handle, but it does not clarify format, examples, or whether it accepts URLs or raw handles. This is a partial but not complete compensation.

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 a specific verb ('lookup') and resource ('public social profile'), with the handle as the retrieval key. It lists the returned data fields (display name, bio, follower/post counts), which distinguishes it from sibling tools like prism_company or prism_places.

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: use this tool when you need a public social profile by handle. However, it does not explicitly state when to use it versus alternatives like prism_search or prism_contact, and offers no exclusions or prerequisite 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, the description carries the full burden of disclosing behavior. It provides cost information ($0.005–$0.05 USDC) and mentions it is a meta-search, but it does not explain how results are aggregated, rate limits, or any side effects. The truncated final sentence suggests missing behavioral details.

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 compact but structurally flawed – it ends mid-sentence with 'in ONE ca', indicating truncation or poor editing. Including cost is useful, but the incomplete sentence disrupts clarity. It is under-specified rather than concise.

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 complexity (multiple platforms), the single-parameter schema, and no output schema or annotations, the description is far from sufficient. It offers no guidance on how to craft the query, what to expect in the response, or any constraints. This is inadequate for reliable agent use.

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 no description and 0% schema description coverage. The tool description does not explain what should go into 'arg' (e.g., a natural language query, list of platforms, formatting rules). This is a severe gap for the only parameter.

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 identifies the tool as a 'Multi-platform meta-search' and lists specific sources (web, GitHub, Hacker News, Stack Overflow, arXiv, academic papers, Wikipedia, news), which gives a clear verb+resource scope. It distinguishes from sibling tools like search_web or search_github by emphasizing unified multi-platform access. However, the description is truncated ('ONE ca'), which slightly undermines completeness.

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 broad multi-platform search queries, but it does not explicitly state when to prefer this over individual sibling tools like search_web or research_papers. There are no 'use this when' or 'prefer alternatives when' instructions, leaving usage largely 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?

Annotations are absent, so the description carries the full burden. It discloses no behavioral traits beyond returning papers and their metadata. It does not mention rate limits, result count limits, pagination, query syntax, or the meaning of 'recent'. The cost line is informative but unrelated to behavior like read-only status or side effects.

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

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

The description is short but visibly truncated ('type, publis...'), making it structurally incomplete. It is not a full sentence and fails to deliver coherent guidance. While brevity is good, the fragment hurts readability and information 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?

For a simple one-parameter search tool, the description lists output fields, which is helpful, but it omits essential context such as expected input format, result ordering, limitations, and error behavior. With no output schema and no annotations, the description is insufficient for an agent to confidently 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 sole parameter 'arg' is completely undocumented in the schema (0% coverage). The description implies it is a query, but does not specify format, examples, or whether it supports advanced operators. Since schema coverage is low, the description needed to compensate, but it fails to clarify the parameter beyond 'for a query'.

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 the tool returns 'Recent academic papers for a query' and lists the fields included (title, authors, journal, year, DOI, citations, abstract when available, type, publishing info). This clearly identifies a specific resource (academic papers) and distinguishes it from general web search or news tools. However, it lacks an explicit verb like 'search' or 'retrieve', and the text is cut off, which slightly reduces 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 guidance is provided on when to use this tool versus alternatives such as 'research', 'search_web', or 'news_google_search'. The description does not mention that it is specialized for academic literature, nor does it exclude cases where other tools would be more appropriate. There is no 'when to use' or 'when not to use' 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?

The description discloses the cost ("$0.005–$0.05 USDC on Base per call"), which is a useful behavioral detail. With no annotations, it carries the full burden, but it does not mention return format, rate limits, authentication needs, or potential side effects. Cost is one strong disclosure, but gaps remain.

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 first sentence is concise and informative, but the text is truncated at "For dev a," which makes it incomplete and awkward. The cost sentence is clear but the overall structure suffers from the incomplete second thought.

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?

With no output schema and no annotations, the description is the only source. It covers the core function and cost, and lists output fields, but does not explain the return structure (e.g., list size, pagination) or explicitly map the parameter. Given the tool's low complexity, this is barely adequate.

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%, so the description must compensate. It says "for any keyword," implying that the single required parameter 'arg' is the search keyword. However, it does not explicitly state that arg is the keyword or explain its format, so the compensation is partial.

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 performs GitHub repository discovery, returning top repos by stars with specific fields (name, language, description, forks, topics) for any keyword. This distinguishes it from sibling search tools like search_web or search_places by focusing on GitHub-specific 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?

It provides clear context: use this when searching for GitHub repositories by keyword. However, it does not explicitly mention when not to use it or name alternative tools, so it lacks explicit exclusions that would warrant a 5.

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 burden of behavioral disclosure. It mentions the cost per call and the output fields, which adds value, but it omits details like rate limits, authentication, error behavior, or whether the arg input format is strict. This is adequate but not rich.

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 and front-loaded: the first sentence states the core function and outputs, the second provides cost information. Every word earns its place, with no padding or repetition.

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?

The tool is simple (one parameter, no output schema), and the description covers the main purpose, cost, and output type. However, it does not fully specify the input format or any constraints, which would make it more complete for a geocoding API.

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 a single 'arg' parameter with no description, and schema coverage is 0%. The description must compensate but only implies the arg is a location/address query; it does not specify the expected format (e.g., free text, structured address, coordinates). This leaves the parameter semantics under-explained.

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 geocoding for places/addresses using OpenStreetMap, returning full address, category, and coordinates. It goes beyond a tautology by specifying the resource and output, but it does not explicitly differentiate from sibling tools like prism_places, so it doesn't achieve a 5.

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 phrase 'For maps, logistics' provides a context of when to use the tool, implying relevant use cases. However, there is no explicit guidance on when not to use it or which alternative (e.g., prism_places) would be more appropriate, so it stops at implied usage.

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 burden of behavioral disclosure. It discloses the return format (title, URL, snippet) and cost range ($0.005–$0.05 USDC per call), which is useful. However, it does not mention rate limits, auth requirements, or whether the tool is read-only and safe.

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 brief and front-loads the main purpose, which is good. However, it appears truncated mid-sentence ('prosp') which makes it read as incomplete and unpolished. The cost information is a valuable extra but the broken structure lowers the score.

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 single-parameter search tool, the description covers the output and cost, which is a basic level of completeness. It lacks parameter examples, distinctions from related search tools, and any mention of pagination or result count limits. Given the sparse schema and annotations, these gaps make it only minimally adequate.

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 a single parameter 'arg' with no description and 0% schema coverage, so the description must compensate. The phrase 'for any query' implies that arg is the search query, providing basic meaning. Yet no more specific guidance on query format or constraints is given.

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 this is a web search tool returning ranked results with title, URL, and snippet for any query. It uses a specific verb and resource, providing clear output expectations. However, it does not explicitly distinguish itself from siblings like news_google_search or search_places.

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 suggests it is the core discovery primitive for lead-gen and prospecting, giving some context on when to use it. However, it does not mention alternatives or exclusions, leaving the agent to infer appropriate usage. The 'for lead-gen, prosp' phrasing is more implied than explicit.

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, but description adequately discloses it's a read-only query returning stats, with mention of 'Free'. No hidden 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.

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

Two succinct sentences with no redundant information, front-loaded with key purpose and use case.

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?

Lacks output schema but describes return content (top tools, success rate, recent activity) sufficiently for a simple stats 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?

No parameters exist; description correctly indicates zero input needed. Schema coverage is 100%.

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 summary stats (top tools, success rate, recent activity) and explicitly distinguishes from siblings like pricing_info or wallet_helper.

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 a clear use case (verify integration) and notes it's free, but doesn't specify when not to use compared to alternatives.

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?

Without annotations, the description transparently states it returns instructions and is free. No hidden effects or mutations are implied, making the behavior clear.

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 concise sentences with no redundant information. The purpose and usage are front-loaded, making it efficient for an agent to understand.

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 tool with no output schema, the description fully covers what the tool does, when to use it, and its cost implications. No gaps remain.

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; schema coverage is 100%. The description adds value by noting the tool is free, but parameter semantics are inherently not 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?

The description explicitly states the tool returns step-by-step instructions for x402 USDC autopay setup. It clearly distinguishes from siblings by focusing on payment configuration, not snippet installation or endpoint listing.

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 directly advises using this tool when a paid tool returns a 402 error or when onboarding a new agent. It provides clear context but does not excludedly 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.