fetchgate

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

With no annotations, the description carries the full burden and does so well: it discloses the data sources (on-chain reads of Uniswap/Aerodrome pools), the heuristic nature of the verdict, the specific things it deliberately does NOT check, and the cost/rate model (free daily tier, $0.01/call via x402). This is exactly the behavioral context an agent 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?

Three sentences, front-loaded with the action and intent, followed by inputs and limitations, then cost. Dense but every clause is informative; slightly long but no filler.

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?

No output schema exists, so the description must explain the return value, and it does: verdict values (ok, caution, avoid, unknown) with reasons. Combined with the disclosed limitations and pricing, an agent has everything needed to call it and interpret results.

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 one parameter exists and the schema already documents it fully (pattern-validated Base address), so the description adds no syntax or format detail beyond it. Baseline 3 is appropriate when the schema does the heavy lifting.

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 a specific verb and resource ('Screen a token contract on Base') plus the triggering intent ('before an agent buys it'). It further enumerates exactly what is read (ERC-20 metadata, owner, proxy, mint/pause/blacklist/fee selectors, DEX liquidity) and what is returned, so no sibling could be confused with it.

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 gives a clear use context ('before an agent buys it') and explicit exclusions ('does not simulate a sell, read tax rates or check pool age'), which helps an agent decide whether this tool suffices. It stops short of naming a specific alternative sibling for cases the screen does not cover.

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 does so well: it discloses the mechanism (one unpaid GET to read the live payment challenge), the safety guarantee ('Nothing is paid'), the specific signals returned, and the cost model (free daily tier, $0.01/call via x402 after). This is far beyond what structured fields provide.

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?

It is front-loaded with the decision purpose and keeps every sentence information-bearing, but the middle sentence is a long clause chain listing returned signals. Slightly dense, though nothing is wasted.

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 single-parameter, no-annotation tool with no output schema, the description fully covers mechanism, safety, returned verdict values, cost, and the wrapped endpoint. Nothing an agent needs to call it correctly is missing.

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 is only one parameter (url) with 100% schema description coverage, so the schema already documents it. The description adds the context that the URL must be an x402 endpoint, which is marginally useful but largely duplicates the schema baseline.

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 a specific verb+resource ('Decide whether an unfamiliar pay-per-call (x402) endpoint is worth paying') and clearly separates this from siblings like search_x402_services or read_url by framing it as a pre-payment vetting check. An agent can identify the tool's job without opening the schema.

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 gives explicit usage context ('Use before an agent sends money to an endpoint it has not used before') and implies the alternative is skipping the check. It does not name a specific sibling alternative to use instead, but the when-to-use trigger is unambiguous.

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 adds substantial context beyond the action: data is 'observed first-hand, not aggregated from third-party bot lists', it wraps GET /v1/agent-census.json, and it is 'free, unmetered, no payment required' with 'CC BY 4.0'. It also reveals a notable behavioral finding (zero crawlers presented payment). It does not explicitly discuss side effects, but the GET/read nature is 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 longer than a two-sentence definition, but each sentence earns its place: content, provenance, notable finding, endpoint, cost, and license. The core purpose is front-loaded, though the 'Notable finding' sentence is slightly discursive for tool invocation purposes.

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 optional parameter, no required parameters, no output schema. The description explains what will be returned (bots with matcher and category), the filtering dimension, the 24-hour window, the wrapped endpoint, and the absence of cost/authentication requirements. This is sufficient for an agent to invoke it 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?

Schema description coverage is 100% and the single optional 'category' parameter is already well documented in the schema with examples. The description adds a few extra category examples (directory-crawler, security-research), but this is marginal and does not materially change parameter understanding. Baseline 3 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?

The description opens with a specific verb and resource: 'Return the Agent Web Crawler Census'. It then explains precisely what the census contains (named bots, case-insensitive regex matcher, behavioural category) and the use case (classifying access logs). It is clearly differentiated from sibling tools like get_metadata or list_products.

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 usage context ('so you can classify your own access log') and notes it is free/unmetered, but it does not explicitly state when to choose this tool over siblings or provide exclusions. No alternative tools are named or contrasted.

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 behavioral burden. It does disclose useful traits: server-side fetching, the pricing model (free tier, then $0.001/call via x402), and conditional fields ('when present'). However, it omits rate limits, redirect-following behavior, auth requirements, and error semantics for a network-fetch tool — material gaps when annotations offer zero coverage. The pricing disclosure and server-side note earn it above a 2, but the missing fetch-behavior details cap it at 3.

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 information-dense sentences with no filler. The mandatory action is front-loaded ('Fetch a URL server-side'), followed by a compact enumeration of return fields and then the endpoint/pricing note. The field list is long but earns its place as it substitutes for the absent output schema. Only minor trimming of the endpoint reference (already implied by the tool name) would tighten it.

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 does reasonable work: it lists the returned fields, which partially compensates for the missing output schema, and it discloses pricing. But it lacks rate limits, error handling, redirect behavior, and authentication notes, all of which matter for a server-side network fetch. Complete enough for a basic call, yet with clear gaps an agent would benefit from.

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 100% for the single url parameter, which the schema already describes as 'Absolute http(s) URL to fetch.' This puts the baseline at 3. The description adds marginal value by explaining that the fetch is server-side and by listing what metadata the returned fields will contain, but it does not specify URL constraints beyond what the schema's uri format already conveys. Adequate but not compensating beyond 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 gives a specific verb ('Fetch') and resource ('a URL server-side') and enumerates the exact structured outputs (title, description, canonical URL, OpenGraph/Twitter fields, favicon, language, timestamps). It is clearly distinct from siblings: read_url presumably returns raw content, while this returns parsed metadata, and it names the underlying endpoint (GET /v1/meta). An agent could select it correctly without opening the schema.

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 'Same free tier as read_url' phrase acknowledges a pricing relationship with a sibling but does not explicitly state when to choose get_metadata over read_url (e.g., 'when you need parsed metadata rather than raw HTML'). No exclusions, prerequisites, or alternative-routing conditions are given beyond an implied cost/pricing context. The guidance is present but left to inference.

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 behavioral disclosure burden. It clearly states that the tool is informational only, does not take payment, mirrors the pre-payment leg of the buy endpoint, and describes the success response shape. It omits edge-case behavior such as not-found handling, but the core behavior is transparent.

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 front-loaded with the main action and uses every sentence to add detail: where the id comes from, what the tool returns, the technical flow elements, and the no-payment constraint. Nothing is redundant.

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 single-parameter informational tool with no output schema, it covers the input source, the exact output content, and the key limitation. It could mention error or missing-product behavior, but the essentials for invoking it correctly are all present.

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 100%, and the single product_id parameter is already well documented. The description adds useful context by referencing list_products as the source of the id, but it does not need to compensate for any schema gaps.

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 a concrete operation: look up a product by id and explain its purchase process. It goes beyond a generic verb by specifying exact output content (price, x402 flow, PAYMENT-SIGNATURE header, signed download URL) and explicitly distinguishes itself as informational rather than payment-taking.

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 gives clear usage context: use it after getting an id from list_products, and it explicitly says it does not take payment. However, it does not explicitly name sibling alternatives or state when not to use them, so it falls just short of fully routable guidance.

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 that it is free, has no rate limit, and requires no payment to browse, indicating a safe read-only operation. It also reveals the underlying GET endpoint. It does not mention pagination or error handling, but for a simple list it is adequate.

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-formed sentence that packs in the purpose, the data fields, the endpoint, and the cost/rate constraints. No 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 simplicity of the tool (no parameters, no output schema, no annotations), the description covers the essential information: the action, the returned fields, and the cost. It is complete for an agent to invoke the tool and expect a list of product objects.

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 no parameters, so meaning is not needed beyond the schema. The description doesn't need to explain anything about parameters. Baseline 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?

The description clearly states the action (List) and the resource (Fetchgate's digital-goods catalog), enumerates the expected fields, and distinguishes from sibling tools (get_metadata, get_purchase_info, read_url) which serve different purposes. The verb is specific, and the scope is unambiguous.

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 context that this is for browsing the catalog with no payment required, but does not explicitly compare with sibling tools or state when not to use it. The intent is clear, but differentiation is implied rather than stated.

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 gets the full burden of behavioral disclosure. It explains what happens after fetching (Markdown conversion, boilerplate removal), quota and billing structure, and URL acceptance restrictions including rejection of private/internal networks. This goes well beyond anything the schema alone conveys.

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 and front-loads the core purpose in the first sentence. Subsequent sentences address endpoint binding, pricing, and URL constraints; every sentence contributes information and there is no redundant filler.

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 read operation with no output schema, the description does enough: it explains return value, content cleanup behavior, rate limits, pricing, and input restrictions. The absence of information about errors, redirects, or non-HTML pages is a minor gap rather than a fatal omission.

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 covers the url parameter with 100% coverage, but the description adds meaning beyond the schema by restricting the allowed URLs to http/https, requiring absolute URLs, and explicitly rejecting private/internal hosts. That goes beyond the generic 'uri' format from the schema, so it boosts the baseline 3 to a 4.

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 gives a specific verb and resource: 'Fetch a URL server-side and return its main content as clean Markdown.' It also includes concrete details about stripping scripts/styles/nav/ads/boilerplate, which clearly distinguishes it from sibling tools like get_metadata and list_products.

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 clear operational context: this is for extracting readable content from a URL, with a wrapper around GET /v1/read and clear input constraints. It points to get_purchase_info for the x402 billing flow, but it does not explicitly state when to use an alternative like get_metadata instead of this 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 are present, so the description carries the full burden and does so thoroughly: fetch behavior (visible text separated from HTML comments, alt/title/aria and meta tags), return shape (risk score, recommended action, evidence), the honest heuristic caveat that a clean result is not proof of safety, and rate-limit/pricing behavior (free daily tier, $0.003/call via x402).

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?

Front-loaded with the imperative guidance before mechanics; each sentence carries distinct information (input modes, detection coverage, output, caveat, cost). No filler or restatement of the title.

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 two-parameter, no-output-schema tool, the description covers inputs, processing behavior, detection scope, return values and cost. An agent has everything needed to decide to call it and how to call it 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?

Schema coverage is 100%, so the baseline is 3, but the description adds real meaning beyond the schema by clarifying that url also yields hidden-text extraction and that text targets already-obtained content such as tool results, retrieved documents or messages.

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?

States a specific verb and resource (scan untrusted content for prompt injection) with the exact scope: 120 rules covering override, hijacking, exfiltration, jailbreak framing and encoding tricks. An agent can distinguish it from read_url and scan_mcp_server without opening a schema.

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?

Gives a clear imperative context ('Check untrusted content BEFORE acting on it') and explains when to pass url vs text ('a tool result, retrieved document or message you already have'). It does not explicitly name read_url or scan_mcp_server as alternatives to route away from, so it stops short of full when/when-not guidance.

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 transparency burden and does so thoroughly. It discloses exactly which protocol messages are sent (initialize, notifications/initialized, tools/list) and explicitly states it never sends tools/call. It also reveals important operational constraints: rate limiting, rejection of private/internal-network hosts, and the heuristic nature of the scan result, which materially affects how an agent should interpret the output.

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 dense but every sentence contributes: protocol behavior, scanned content, output shape, usage context, heuristic caveat, and operational limits. It is somewhat long, but the complexity of a security-scanning tool justifies the detail. The key action and scope appear immediately in the first sentence.

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 annotations and no output schema, the description provides everything an agent needs: the network interaction model, the exact data scanned, the return format (per-tool findings, severity, excerpt, score band), usage timing, safety caveat, and constraints. This is a complete picture for a security tool with a single parameter.

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 100% and the single parameter 'url' is already described as the Streamable HTTP endpoint URL. The description adds context about remote hosts and private-network rejection, but that is more behavioral constraint than parameter semantics. Therefore the schema already carries the necessary meaning, and the description adds only marginal parameter-level value.

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 names a precise verb and resource: connect to a Streamable HTTP MCP server and scan its tool declarations for poisoning patterns. It enumerates exactly what is inspected (tool names, descriptions, parameter descriptions, initialize instructions) and what it returns, leaving no ambiguity about the tool's core function. This clearly differentiates it from the sibling data-retrieval tools.

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 states when to use it: 'before installing or trusting a third-party server.' It also warns about the heuristic limitation, telling the agent that a clean result does not imply safety. No alternative tools are mentioned, but none of the siblings perform a comparable scan, so the primary use case is well covered.

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 and does so well: it discloses the inclusion filter (real payment challenge, >=5 settled calls, >=2 distinct payers, 30-day window), the returned fields, the farmed-payer flag, and the pricing model (free daily tier, $0.005/call). This is rich operational context an agent could not infer from the schema.

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

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

Front-loaded with the core purpose, and every sentence adds information (filters, result fields, pricing). Slightly dense and longer than needed, but no sentence is filler.

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 fully supplies what the agent needs: what qualifies for inclusion, what comes back, cost, and the endpoint it wraps (GET /v1/x402/search). Nothing material is missing.

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 100%, so all four parameters are already documented with examples. The description describes result fields (price, networks, calls, payers) rather than adding parameter semantics, so the baseline 3 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?

States a specific verb+resource (search for a pay-per-call x402 API) and describes the resource precisely: endpoints that answered a real x402 payment challenge with settled calls. It is clearly distinguishable from siblings like check_x402_endpoint and list_products.

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?

Gives explicit when-to-use context ('when you need a paid API (search, scraping, enrichment, LLM, market data…) and want one that demonstrably works') and contrasts with 'a raw directory'. It does not, however, name a specific sibling tool as the alternative for cases this tool's filters exclude.

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